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TMIP: Conference Proceedings August 14 to 17, 1994 Fort Worth, Texas

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                   Travel Model Improvement Program

The Department of Transportation, in Cooperation with the
Environmental Protection Agency and the Department of Energy, has
embarked on a research program to respond to the requirements of the
Clean Air Act Amendments of 1990 and the Intermodal Surface
Transportation Efficiency Act of 1991.  This program addresses the
linkage of transportation to air quality, energy, economic growth,
land use and the overall quality of life.  The program addresses both
analytic tools and the integration of these tools into the planning
process to better support decision makers.  The program has the
following objectives:

1. To increase the ability of existing travel forecasting procedures
   to respond to emerging issues including:  environmental concerns, 
   growth management, and lifestyle along with traditional
   transportation issues,

2. To redesign the travel forecasting process to reflect changes in
   behavior, to respond to greater information needs placed on the
   forecasting process and to take advantage of changes in data
   collection technology, and

3. To integrate the forecasting techniques into the decision making
   process, providing better understanding of the effects of
   transportation improvements and allowing decision makers in state 
   governments, local governments, transit operators, metropolitan   
   planning organizations and environmental agencies the capability of
   making improved transportation decisions.

This program was funded through the Travel Model Improvement Program.

Further information about the Travel Model Improvement Program may be
obtained by writing to:

                   Planning Support Branch (HEP-22)
                    Federal Highway Administration
                   U.S. Department of Transportation
                        400 Seventh Street, SW
                         Washington, DC 20590


Travel Model Improvement Program

Conference Proceedings
August 14-17, 1994

Prepared by:

Gordon A. Shunk
Texas Transportation Institute
1600 East Lamar Boulevard, Suite 112
Arlington, Texas 76011


Patricia L. Bass
Texas Transportation Institute
1600 East Lamar Boulevard, Suite 112
Arlington, Texas 76011

Funded by:

U.S. Department of Transportation
   Federal Highway Administration
   Federal Transit Administration
   Office of the Secretary

U.S. Environmental Protection Agency

Distributed in Cooperation with: 

Technology Sharing Program
Research and Special Program Administration
U.S. Department of Transportation
Washington, DC 20590



Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

   Keynote Address:  Evolution and Objectives of the Travel Model
   Improvement Program
   Martin Wachs, Ph.D. . . . . . . . . . . . . . . . . . . . . . . . 3

Workshop Recommendations . . . . . . . . . . . . . . . . . . . . . .11

   Travel Model Improvements . . . . . . . . . . . . . . . . . . . .12

   Air Quality . . . . . . . . . . . . . . . . . . . . . . . . . . .15

   Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . .15

   Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

   Land Use. . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

   Sample Populations. . . . . . . . . . . . . . . . . . . . . . . .20

   Training. . . . . . . . . . . . . . . . . . . . . . . . . . . . .20

   Program Guidance. . . . . . . . . . . . . . . . . . . . . . . . .22

Presentation Abstracts

   Activity Based Modeling and Policy Analysis
   Clarisse V. Lula. . . . . . . . . . . . . . . . . . . . . . . . .27

   Identification of Short-Term Travel Model Improvements
   Thomas Rossi. . . . . . . . . . . . . . . . . . . . . . . . . . .29

   Travel Survey Manual Update
   Thomas Rossi. . . . . . . . . . . . . . . . . . . . . . . . . . .31


   The Effects of Land Use and Travel Demand Management Strategies on
   Commuting Behavior
   John Suhrbier and Susan Moses, presentation by Thomas Rossi . . .33

   Improved Network Models:  Multicriteria Traffic Assignment, T2
   Robert B. Dial, Ph.D. . . . . . . . . . . . . . . . . . . . . . .35

   Equilibrium Conditions in Land Use and Travel Forecasting
   Stephen H. Putman, Ph.D., presentation by Frederick Ducca, Ph.D .37

Travel Model Improvement Program:  TRANSIMS Presentation

   Introductory Remarks
   Darrell Morgeson, Ph.D. . . . . . . . . . . . . . . . . . . . . .39

   TRANSIMS Model Requirements as Derived from Federal Legislation
   Vernon Loose, Ph.D. . . . . . . . . . . . . . . . . . . . . . . .43

   TRANSIMS Methodology
   Darrell Morgeson, Ph.D. . . . . . . . . . . . . . . . . . . . . .47

   Use of TRANSIMS for Air Quality Analysis
   Michael Williams, Ph.D. . . . . . . . . . . . . . . . . . . . . .51

   Interim Remarks
   Darrell Morgeson, Ph.D. . . . . . . . . . . . . . . . . . . . . .55

   TRANSIMS Microsimulation System Architecture
   Steen Rasmussen, Ph.D . . . . . . . . . . . . . . . . . . . . . .57

   Closing Remarks
   Edward Weiner . . . . . . . . . . . . . . . . . . . . . . . . . .61

   List of Attendees . . . . . . . . . . . . . . . . . . . . . . . .63



The travel forecasting models currently in widest use today were
developed more than 25 years ago, primarily to evaluate alternative
major highway capital improvements.  In the 1970s the models were
adapted for use in planning major transit capital facilities.  These
current models were not intended to evaluate congestion pricing,
transportation control measures, alternative development patterns, or
motor vehicle emissions.  It is not surprising that they are not well
suited to the tasks needed to meet the planning and air quality
requirements of the Intermodal Surface Transportation Efficiency Act
(ISTEA) or the Clean Air Act Amendments (CAA).

To address current model deficiencies, the Federal Highway
Administration, the Federal Transit Administration, and the Office of
the Secretary, U.S. Department of Transportation; the U.S.
Environmental Protection Agency; and the U.S. Department of Energy
have initiated a major program to enhance current models and develop
new procedures.  The Travel Model Improvement Program (TMIP) is a
cooperative effort among organizations involved in transportation,
land development, and environmental protection.  The program will seek
active technical involvement and financial participation from state
departments of transportation (DOTs), local governments and
metropolitan planning organizations (MPOs), environmental agencies,
and private sector entities.

The objectives of the Travel Model Improvement Program are:

-  To increase the policy sensitivity of existing travel forecasting
   procedures and their capacity to respond to emerging issues
   including environmental concerns, growth management, and changes in
   personal and household activity patterns, along with the
   traditional transportation issues.

-  To redesign the travel, forecasting process to reflect today's
   traveler behavior, to respond to greater information needs placed
   on the forecasting process, and to take advantage of changes in
   data collection technology; and,

-  To make travel forecasting model results more useful for decision

The Program is being conducted in four tracks, each with a specific
purpose and product.  Track A, Outreach, will help practitioners
improve their existing planning procedures to be consistent with
currently desirable practice.  This outreach will be a continuing
program of training, technical assistance, research coordination and a
clearinghouse for research findings.

Track B, Near Term Improvements, is a program of technical activities
to help MPOs and state DOTs elevate current practice to the state of
the art.  These efforts will implement model improvements already
developed but not widely included in current transportation, land use
and air quality planning activities.


Track C, Longer Term Improvements, involves major research and
development of new approaches to travel and land use forecasting. 
Issues and questions, and the roles of models in providing information
to address them, will be determined.  This research will advance the
state of the art of travel and land use modeling to meet those needs.

Efforts in Track D, Data Collection, will identify, design and develop
improved data collection procedures, that will meet decision makers'
current and future needs.  Data will be collected to assist
practitioners in meeting the requirements of the Intermodal Surface
Transportation Efficiency Act and the Clean Air Act, to improve
existing models and to develop new procedures.

The travel forecasting issues and needs of the transportation and
environmental planning communities must be identified to develop an
agenda for TMIP that will best serve these communities.  Additionally,
the approach and elements of research needed in travel forecasting
must be further defined.  The TMIP sponsored a workshop conference to
accomplish these tasks.  The purpose of this conference was to bring
together experts and practitioners to:

-  Review and receive comments on the work that his been    
   accomplished and the work currently being conducted in Tracks B and
   C of TMIP.
-  Receive input on additional short and long-term research that
   should be conducted as part of TMIP.
-  Gather information on the data and training needs of practitioners
   to assist in establishing the work to be conducted in Tracks A and

The first day of the conference focused on Track B, Near Term
Improvements.  Research that has been or is currently being conducted
under this track, as well as related research being conducted by
others, was presented during the morning session.  Following these
presentations, participants divided into workshops to discuss the
short-term research needs to improve existing models and analytical

On day two of the conference, efforts were directed toward research in
Track C, Longer Term Improvements.  During the morning, participants
heard presentations on TRANSIMS (Transportation Analysis and
Simulation Systems), the new model approach undertaken by the Los
Alamos National Laboratory.  The Tuesday workshops then focused their
discussions on TRANSIMS and other longer term model research needs
that must be addressed to perform current and anticipated future
planning and policy analyses.

Wednesday workshops concentrated on the issues of deployment,
dissemination and education, associated with Track A, Training and
Technical Assistance, and on the data needed to support continued
research in Tracks B and C as well as to support new model approaches
as outlined under Track D, Data Development.  Workshop participants
also prepared a summary of priority recommendations for the TMIP

This report presents a summary of the conference presentations and
highlights the recommendations made by the workshop participants.  It
is anticipated that future conferences will be held to provide
continuous outreach and direction to the TMIP.



Keynote Address:  Evolution and Objectives of the Travel Model
Improvement Program

by Martin Wachs, Ph.D., University of California

Twenty-one years ago, in 1973, Douglas Lee published an article in
what was then the Journal of the American Institute of Planners,
entitled "Requiem for Large Scale Models."  In that article, which has
been widely quoted and reprinted, discussed and debated, Lee argued
that the modeling movement had failed and that large-scale regional
activity location and transportation models were dead and should be
buried.  His criticisms were really leveled against models of land use
and urban form, models which distributed activities in space, but many
of us in the transportation planning community recognized his assault
as generic and inclusive of the urban transportation planning modeling
process.  Lee couched his argument, as many of you will recall, in
terms of what he called the seven deadly sins of modeling. The seven
sins were:
   1) Hypercomprehensiveness:  Meaning that the models tried to
      replicate too complex a system in a single shot, and were
      expected to serve too many different purposes at the same time.

   2) Grossness:  In a way, the converse of hypercomprehensiveness.  
      Even though they tried to do too much and serve too many    
      purposes, their results or outputs were too coarse and   
      aggregate, too simplistic to be useful for complicated and  
      sophisticated policy requirements.

   3) Data Hungriness:  Even to produce, gross outputs (a few
      variables), the models required us to input many variables for 
      many geographic units, and from at least several time periods in
      order to produce approximate projections, and very often we    
      could not afford the data collection efforts needed to run the
      models. In other instances, data simply didn't exist at the    
      levels of specificity which would be appropriate to run them. 

   4) Wrongheadedness:  Lee meant that the models suffered from
      substantial and largely unrecognized deviations between the    
      behavior claimed for them and the variables and equations which
      actually determined their behavior.  As an example, when
      regional averages were used to calibrate models, but forecasts 
      were made for local areas, the models deviated from reality
      because of specification errors which were often not even
      recognized by their users.

   5) Complicatedness:  Even though when you looked at them through
      one set of lenses the models seemed terribly simplistic, when
      looked at through another set of lenses they were outrageously
      complex.  Too simplistic in replicating urban economic and
      social processes, the models were too complex in their
      computational algorithms.  Errors were multiplied because



   there were so many equations, spatial units, and time periods.    
   Even the theoretical notion of the model or its representation of 
   an urban process was grossly simplistic compared with reality.    
   Often, the user didn't know how the errors were propagated
   through  series of sequential operations; and sometimes we needed
   to use systematic adjustments or "correction factors" to make the
   models more realistic even though we did not completely comprehend
   the sources of all the errors and could not interpret the
   correction  factors in real-world terms.

6) Mechanicalness: Lee meant that we routinely went through many steps
   in a modeling process without completely understanding why we did 
   so, and without fully comprehending the consequences in terms of  
   validity or error magnification.  He stated, for example, that even
   rounding errors could be compounded beyond reasonable bounds by
   mechanical steps taken to calibrate and apply many models without 
   the user's knowledge.

7) Expensiveness: The costs of the models, derived from their
   grossness, data hungriness, complicatedness, and so on, placed them
   beyond the financial means of many agencies, or depleted the
   resources of agencies so much that the very use of models precluded
   having the resources available to improve them or to fine tune them
   to make them appropriate to their applications.

Lee argued in 1973 that the models should be improved in four ways:

   1) Models should be made more transparent to users and   

   2) Models should combine strong theoretical foundations, objective
      information, and wisdom or good judgment.  Without these
      elements, they remain exercises in empty-headed empiricism,
      abstract theorizing, or false consciousness of what is   
      actually going on in our urban areas.

   3) We should start with problems and match our methods to the needs
      of particular situations, gathering no more information and    
      using no more modeling complexity than is really needed.

   4) We should build the simplest models possible, since complex
      models do not work well, and certainly are unlikely to be
      understood by those who are asked to act on the basis of the   
      model outputs.

These points remain good advice, but the context in which we try to
address them has changed constantly, in part because of advances in
computing, GIS, and so forth.  I hope you will address these
assertions in the workshops which we will have over the next several

In a symposium issue of the Journal of the American Planning
Association which was designed to reconsider Douglas Lee's arguments
20 years later, a very wise man named Britton Harris criticized Lee's
paper in retrospect, arguing that the force of Lee's arguments



gave modelers such a sense of futility and hopelessness, that many of
the best minds, and perhaps more importantly some of the best funding
agencies, turned away from urban and transportation modeling for
decades, convinced by Lee that there was no hope for dramatic
improvements and no point in marginal improvements.  Other brilliant
people, several of whom are present, continued to refine and adapt
models and research new approaches to travel demand forecasting and
network performance.  Even those people who know it better than the
rest of us realize how inadequate today's modeling capabilities are in
comparison with the need and with advances made in recent decades in
other fields.

Despite Douglas Lee's criticisms and those of many other thoughtful
people, travel demand modeling continued to be used on a very wide
scale.  Commercially available software packages made the models
widely available to consultants and agencies.  Legislation and
regulation made it almost a necessity to use travel forecasting models
despite their many limitations and flaws.  Air quality analysis
requirements led to a long chain of sequentially applied independent
models in which outputs of one become inputs to another:  from land
use or urban activity models to vehicle ownership models to trip
generation, trip distribution, mode split, and traffic assignment to
pollution generation to pollution dispersion models.

Where are we in 1994?  When I look at the state of the art and
practice of urban land use, urban transportation, and environmental
modeling and the connections between them, I see an extremely
disappointing picture made up of at least six disturbing dimensions.

First, the problems which Lee diagnosed are, in my opinion, still with
us to a great extent.  The problems he noted have not gone away, in
some cases because even after more than 20 years the models he
criticized are still the very same ones in use today.  Our models, by
and large, continue to commit the seven deadly sins about which he

Second, the models we use today reflect some progress, but limited
progress, in comparison with what we need when it comes to
incorporating new forms of data collection and management, such as
Geographic Information Systems, and rapidly increasing computing
power.  The models have not yet taken sufficient advantage of new
knowledge and new capabilities in these areas.

Third, I see modelers in some areas who seem content to use
inadequate, out-of-date models which fall short of modern
capabilities.  They are content with these models in part because
nobody demands more of them; in part because they don't have the
resources or the staff to expand and improve their models; and in part
because in some cases they do not even have the training and the skill
to recognize that they are falling short of any reasonable standard.

Fourth, I see software vendors and a consulting community which
continues to offer clients inferior models at high prices because the
clients are not sufficiently sophisticated to demand more, and because
the consultants live in a world of competition in which dollars spent
on development are not recouped from contract fees.  Thus, the
pressure is always there to apply what models we have rather than to
tailor more advanced models more directly to the needs of
policymakers.  Our existing capabilities, unfortunately, then become a



brake on efforts to forge new capabilities.  But our existing
capabilities are behind the needs of our time.

Fifth, I see a group of academic colleagues in the field of travel
demand analysis who have focused on subtle nuances of travel behavior,
elevating our understanding of travel choice behavior to a
sophisticated science which is discussed in arcane language that gets
ever farther removed from the consultants and practitioners and from
the immediate needs of policymakers.  We need to find ways of getting
this large and fascinating body of knowledge to be more accessible to
practitioners and policymakers in the form of usable applications,
packages and training programs.  The responsibility for doing this has
to be shared.  There are roles for academics, consultants, the federal
government, and the MPOs.

Sixth, I see the federal government, whose demanding planning
requirements and financial support in the sixties and seventies, made
research, development, and dissemination of model improvements a
lively area of concern for a community of scholars, consultants, and
clients, doing relatively little since the beginning of the eighties
to either promote research on model advances or to require modeling
applications which are a step beyond the existing poorly performing

For all these reasons, the models our practitioners use today for land
use, travel forecasting, and air pollution analysis have not been
seriously recast to address the policy issues of the nineties, such as
air quality, transportation demand management, parking management, and
road pricing, that are decidedly different from the policy issues of
the sixties, which largely dealt with facility location and sizing. 
Practitioners do not have the tools to do the required analyses;
agencies do not have the resources to push their capabilities into
these new directions; and, scholars are not worrying about how to
bridge the gap between theory and practice.  Additionally, federal
requirements and regulations are not pushing the state of the art, and
federal dollars have, until recently, been just a trickle in
comparison with what is needed to address this problem.

In 1972 I somewhat proudly told my transportation planning students
how complex issues in health care were being addressed mostly without
advanced and sophisticated models, while modern freeway and transit
systems were being planned and designed with the aid of advanced
analytical tools.  In 1994 we are still facing health care reforms in
a disorganized, unscientific way on the basis of polemics rather than
persuasive analysis.  Unfortunately, today we seem to be addressing
transportation problems as we are health care reform; methods of
analysis in our field having become less important to policymaking,
less influential in decision making than they were two decades ago. 
Worst of all, though many of us recognize this failure to advance in
transportation analysis, we are a divided community, blaming one
another for our problems instead of pulling together to solve them. 
We know how computing technology has improved and how GIS capabilities
increase the potential for travel demand analysis.  We know that
meeting air quality problems demands more of travel demand modeling
than we can adequately deliver, and we know that new understandings of
travel, choice behavior are not adequately incorporated into our
standard modeling practice.  We all look to each other to take the


lead in overcoming these problems.  Agencies blame their consultants,
consultants blame funding agencies and academics blame federal

I would like us to vow to make this conference and the Travel Model
Improvement Program landmarks in turning this situation around.  While
there are enough problems and there is enough blame to go around, we
all recognize the primitive state of our modeling capabilities in
practice in relation to planning and policymaking requirements, and we
also can easily see the other side of the coin.  There is also more
than enough opportunity to be shared in this business, to get excited
about.  Academics should and could be bringing their new
understandings before the community of practitioners; consultants
should and could be upgrading the standard capabilities of the studies
they perform; agencies should and could be upgrading their staff
capabilities, software and hardware; software vendors should and could
be putting forward new packages so that progress in transportation and
air quality analysis might be it least as dramatic as progress, in
video gaming; the federal government should and could be exerting more
leadership and providing more sponsorship in making all of this

In light of the demands of the Clean Air Act Amendments and the ISTEA,
the fledgling Travel Model Improvement Program (TMIP) is one mechanism
by which the federal government is attempting to play an active role
in this realm.  TMIP has participation from several different agencies
within the federal government:  the Federal Highway Administration,
the Federal Transit Administration, the Office of the Secretary of
Transportation, the Bureau of Transportation Statistics, the
Environmental Protection Agency and the Department of Energy.  Each
agency is represented in decision making about the program and each is
funding at least some of its components.  We envision a time period at
least on the order of five years to accomplish the goals of this
program, and perhaps longer.  The effort is still very modest, with
only a few people in each agency, and quite fragile in terms of
financial and political support.  It needs our vocal support if it is
to move ahead.

An important goal of this program is to increase the policy
sensitivity of travel forecasting procedures to allow us to do a
better job of testing policies related to growth management, air
quality, and energy conservation through applications of travel demand

Another goal is to advance the capability of travel demand models as
reflections of new knowledge about travel behavior, new data
collection and data management capabilities, and new computing

The TMIP includes a review panel composed of representatives of
various interest groups, transit operators, councils of governments,
environmental interests, the real estate, development community, and a
couple of academics, who look over the shoulders of the U.S.
government agencies, and offer advice and counsel.  I have the
pleasure of chairing the review panel, and it is in that capacity that
I was selected to welcome you to this conference.  As members of the
review committee, we agree not to have any financial attachments to
this program.  No funded research, for example, will go to review
panel members.  The review committee is only one of several mechanisms



by which the TMIP is seeking to reach out to the professional and
client communities.  Other ways are by hosting conferences like this
one at which your views will be the centerpiece, and by publishing a
series of newsletters, research reports and advisory reports over the
coming years.

The Monday morning session will present some of the results of
research already conducted with sponsorship of the TMIP and some
recommendations regarding near-term model improvements to the existing
travel forecasting methods kit bag and so is associated with Track B.
A series of research contracts has been let to consulting firms and
academics under this track and some of the principals of these studies
will be presenting their research results early Monday.

On Tuesday the focus will shift to progress made under Track C, longer
term efforts to achieve fundamentally new approaches to travel demand
forecasting.  As you will see, most of the work undertaken under Track
C to date has been conducted by Los Alamos National Laboratory, under
the rubric of TRANSIMS.

Wednesday we will be talking more about Track A and Track D and the
focus of our discussions will be on deployment, dissemination,
education, commercialization.

Many of you already know that the TMIP is a controversial program.  I
suppose it is inevitable that when funding starts to become available
in an area which has been inadequately supported over the past decade
and a half, there would be vigorous disagreements over priorities and
preferences.  Whatever strategy is adopted, each one of us could think
of an approach which we would personally prefer.  Believe me, there
have been vigorous disagreements within our review panel, between the
review panel and the federal staff, between the larger research
community, consulting community, software vending community and the
federal staff who are administering this program, between senior
federal officials and those closest to the program on a daily basis. 
I suppose such disagreement and debate is healthy, and perhaps no
ambitious program, whether the space exploration program, the
interstate highway program, or the model improvement program, can be
born in a peaceful, friendly, uncontentious atmosphere.  But let me
say just a few words about some major points of disagreement so far.

Each of the four tracks of the Program is important in its own right,
and very importantly, there will be coordination among tracks.  We
have not yet achieved the level of funding needed for all of the
tracks, but we hope to do better.  It is clear, for example, that so
far many more resources have been allocated to Track C and quite a bit
more to Track B than to Track D, the Data Development effort.  I am
particularly interested in Track D, and am assured and reassured that
the uneven progress to date is not a reflection of priority as much as
it is of funding opportunities which involve complex negotiations.  In
a five-or-more year program not every priority can be addressed at
exactly the same time.  I am going to use these workshops to lobby for
my priorities, and of course you are all invited to do the same.  It
would be a mistake, however, to interpret funding allocations made so
far as the sole indicator of program priorities.  There is a lot more
to come.



Secondly, I know that some of the academic researchers present would
have liked a larger share of the action.  They wanted this program to
promote research which advances our understanding of travel behavior
and have complained that the program as conceived is too applied.  The
TMIP is admittedly aimed at advancing the state-of-the-practice in the
world because the state of the practice is, as I said earlier,
appalling in comparison with the societal need for better travel
demand modeling.  I agree with this priority, and yet I believe there
will be many, many opportunities funded under this program to conduct
basic research on travel behavior in order to close gaps in our
knowledge and to build better bridges between theory and practice. 
The doors of this program are wide open to travel behavior researchers
who want to join in our effort to improve travel modeling in practice,
and who want to apply ongoing research to the building of usable
products.  But the emphasis on usable products is to my mind quite

Thirdly, I am aware of the fact that some of you are skeptical about
the award of a large contract to Los Alamos National Laboratories for
the purpose of taking a new and fresh look at simulating travel
patterns.  At Los Alamos, experienced, sophisticated, and extremely
competent modelers and computer scientists are paying attention to our
interests, but they are new to our community and it is natural to feel
a bit uncomfortable about their involvement.  The obvious question in
some of your minds is:  Why not award the funds to people who have a
track record in our field, whose expertise and familiarity with travel
and transportation are clearly established?  There are several reasons
for involving Los Alamos, all of which should be considered.          

One is their world-renowned expertise in simulation which gives rise
to the possibility that a new look, a fresh approach might just offer
a way of approaching problems that is exciting because it is a bit
different from what one might expect from the people having a great
deal of experience in the field.  Another is their astounding
computing capacity which allows them to try a wider range of
simulation approaches than most of the rest of us.  A third reason is
that the funding which became available to involve Los Alamos in this
program WAS NOT available for other purposes.  It could not have been
given to others.

While many of us reacted to the funding of Los Alamos as if a very
large piece of a small pie was going to the laboratory instead of to
us, the truth may be just the opposite.  That is, by virtue of the
considerable progress being made at Los Alamos so far, and the
demonstration of exciting new capabilities for travel modeling which
is going on there, I think that more funding will become available for
travel behavior analysis and modeling.  In other words, the pie will
be larger because of their work, and the rest of our community of
interest will benefit from the collective attention given to travel
models in part as a result of the work at Los Alamos.  I think you
will be impressed with the progress the Los Alamos team has made in an
amazingly short time.  I hope we can focus on synergies:  the ways in
which the Los Alamos work can be integrated with work done by many of
us in the room.  And it is in the synthesis of their innovations with
the deep understandings of travel behavior which others here can bring
to the table, that I think the most progress can be made.



This is a workshop conference, and each and every one of us is a
participant.  We have kept the presentations to a really small
proportion of the total time, and the majority of the time is devoted
to interactive discussions.  We encourage your vigorous, active
participation.  We are delighted by the turnout which far exceeded our
expectations, and we hope that you will in the year 2014 - by which
time transportation modeling will be flawless - refer to the Fort
Worth conference as the source of some of the best ideas which helped
us modernize and improve travel forecasting and analysis during the
coming 20 years.



and improved models.  While not specific research actions, these
criteria indicate the kinds of considerations and procedures that
should be followed to produce models that satisfactorily meet today's
travel forecasting needs.

Travel Model Improvements

The recommendations begin with general recommendations for research
and criteria applicable to models in general or broader aspects of
travel forecasting.  These are followed by specific recommendations
for individual model types.

A key concern about improvements to travel forecasting models is that
they should focus strongly on the role of models in policy development
and other decision making.  The new models must be sensitive to
emerging transportation policies such as pricing, travel demand
management, other transportation control measures, and roadway lane
use restrictions.  The new models must also be both efficient and
accurate, capable of providing credible answers quickly, under
political pressure, in response to the needs and questions of today's
decision makers.

The forecasts resulting from the new and improved models must be
reasonably tractable and logical to withstand the scrutiny necessary
to sustain credibility with their audiences.  The new models must be
capable of producing credible and consistent answers at different
scales, regional, corridor and subarea.  The models should be
rigorously validated according to generally recognized criteria
established jointly by planning, funding and operating agencies and
professional organizations.  The validations should be conducted for
more than one year to assure that the models are sensitive to changes
in conditions that affect travel behavior.  The validations should
provide measures of accuracy and confidence for the forecasts they
produce, in terms of probabilities that reflect the randomness and
inherent variability of individuals' travel choice behavior. 
Comparison of previous forecasts to actual outcomes should be
undertaken systematically to understand if and why those forecasts
were inaccurate and to modify future procedures to accommodate any
problems.  The micro simulation models being developed must be capable
of forecasting accurately and not just replicating existing

The models resulting from this research must be capable of testing
alternative scenarios with consistency.  The models should be
transferable between applications and locations.  A test of
transferability and consistency at different scales of developed area
and locations would be appropriate, comparing results for Los Angeles
and Albuquerque for example.  It remains desirable that the models
contain default parameters but are also amenable to localized
customization.  It is important that the model development research be
mindful of the use of the new models for air quality conformity
analysis in order to produce the kind, detail, and accuracy necessary
for that work.  The models must also be capable of incorporating the
effects of intelligent transportation system improvements.

The models should provide understandable transportation system
performance measures for use in comparative evaluation of
alternatives.  Those measures must include transportation cost



information for use in the financial planning process.

More specific recommendations for research and criteria for specific
models and other aspects of the travel forecasting process are
provided in the following paragraphs.

Travel Behavior
Research is needed on household activity behavior that results in
travel choices.  This should include investigation of how households
select activities and allocate resources for travel, especially time,
money, and vehicle use.  This research should relate the choice
behavior to the life-style and position in the life-cycle of household
residents.  The behavior of similar households should be followed over
time to see how it changes as conditions in the households change.  In
particular the research should identify established, changing and
emerging habit patterns of activity and choices and the inertia or
reluctance to change those patterns under various conditions and
influences.  This work will help to understand the stability or flux
of travel model parameters over time.  The research should lead to
improved understanding of households and members utilities or values
that affect their preferences and choices among travel options.  Those
preferences are what will drive the travel models.

The research should examine the choices, sequences and durations of
activities and relate them to the resultant chaining of trips and the
timing and routing of the travel of households and their members.  The
choice of departure time is important for its influence on peak
spreading.  Factors influencing the choice or allocation of vehicle
use and their relation to characteristics of the assigned driver and
the resulting trip should be determined because that will influence
motor vehicle emissions.  The research should also consider the
interaction among the several choices that influence travel
characteristics for example, activity type affects trip destination,
which affects timing and possibly mode.  Factors inducing new travel
or changes in travel characteristics should be studied for their
possible influences on transportation controls and improvements. 
Through all of this behavioral research, it is important to identify
and understand the day-to-day variability with factors of influence
held constant in order to understand the level of confidence that can
be placed in travel forecasts.  Research is needed on whether current
trip purpose definitions are appropriate for the new travel models or
for studying the characteristics of today's travel behavior.

Trip Generation
Research is needed into the decision processes that produce travel
behavior of households, persons in those households, and use of their
vehicles or other transportation modes.  Additional interest is in
travel behavior on weekends and seasonal variations.  Trip generation
models need to be sensitive to the type and level of transportation
services available and to the accessibility those services provide to
activities in the urban area.  The research should relate trip making
to the activities desired by the household members and in particular
should address the phenomenon of trip chaining to identify the
characteristics of trips and travelers and other conditions that lead
to trip chaining.

Trip Distribution
This research should examine the factors influencing destination
choice.  The research should examine the influence of available



transportation services on destination selection.  The criteria
considered for destination selection should include combinations of
travel costs and other factors that influence that decision.  Research
is especially needed on the destination selection process for non-home
based trips.

Mode Choice
Research on the decision process in the choice of travel mode needs to
address the influence of travel mode on trip chaining.  The mode
choice process for non-work trips is an area of particular research
need.  Mode choice in small and medium size areas should also be
examined.  There is particular interest in the factors and modeling of
car pool formation and in forecasting HOV travel for non-work
purposes.  The influence on choice of mode from parking supply,
availability, proximity to destinations, and cost should also be
examined.  Research is needed to develop procedures for forecasting
park-and-ride lot demand.  The choice research should develop
procedures for pre-model estimation of choice sets.  Research into the
influence of safety and security on mode selection is another area
needing study.  Finally, better software is needed for developing
nested logit mode choice models.

Traffic Assignment
Route selection criteria and decision processes should be
investigated.  Reliable procedures are needed for identifying queuing
and bottlenecks, i.e., where, when, and why they occur and how to
alleviate them.  More work is needed to develop equilibrium,
stochastic and dynamic assignment techniques, identifying the
advantages, roles, and disadvantages of each for different classes of
trips and different applications.  The time dimensions of dynamic
assignment applications need to be addressed.  Trip chaining is an
important consideration in this category as well.

Research is needed to develop better network analysis procedures and
to improve on volume/density relationships.  Procedures should be
identified for standardizing network coding for highway and transit
services.  There is particular interest in improving the coding of
alternative means of access to transit routes.  New travel forecasting
and air quality analyses will require computerized transportation
networks that represent terrain and roadway geometrics and conditions. 
Stochastic network analysis is another area needing further

Other Travel Modeling
Research is needed to develop models for forecasting goods and freight
movement and distribution of services and deliveries.  These analyses
should include effects of inter-city freight movements that terminate
or traverse the urban area.  The freight studies should also examine
the effects of traffic access to intermodal (transshipment) terminals.

Research is needed to develop procedures for forecasting person travel
originating outside the urban area that terminates, traverses, or is
temporarily visiting within the urban area.  Similar needs exist for
better understanding and forecasting of travel using non-motorized
modes and interaction using non-transport modes, e.g., telecommuni-
cations and televised shopping.  Procedures should be developed for
subarea analysis, including ability to analyze site or corridor
conditions, consistent, integrated, and interfaced with regional
models but with added detail and flexibility.  Travel forecasting 



procedures should identify and work with the most appropriate level of
detail, based on the information needed for decisions.  These
procedures are needed for evaluating alternatives and other major
investment studies.

Considerable interest and activity is currently focused on increased
and enhanced use of simulation in the travel forecasting process.  To
further such approaches, research is needed to improve application of
simulation procedures and develop computationally efficient algorithms
for use in those procedures.  For these new applications it is
important to be aware of which simulation strategies work best in
various settings and applications.

Air Quality

The travel models need to be improved to more accurately forecast
motor vehicle emissions.  This will require research to identify how
vehicle characteristics and vehicle operation influence emissions. 
Research is also needed to develop techniques for forecasting the
operating and emissions characteristics of vehicles available in each
household and which vehicle will be used for each trip.  The vehicle
operating characteristics of each driver will also have to be
identified.  In addition, changes in fleet mix, vehicle
characteristics, and fuel type will have to be forecast.

Research is also necessary to develop procedures for forecasting the
conditions under which each vehicle will be operating.  These include
where cold starts occur where the vehicles travel during engine warm-
up, and the location and degree of vehicle acceleration and
deceleration and grade handling.  Improved information on the
operating characteristics of trucks will also have to be identified. 
For the period until improved information on vehicles and operating
characteristics is available, an acceptable procedure for post-
processing traffic characteristics to obtain accurate emissions
estimates should be developed.

Related to these research needs is the question of whether efforts to
improve air quality models are necessary if there is a possibility of
improving fuels and vehicles sufficiently to provide a technological
"fix" for air quality problems.  This demonstrates a need for special
research to determine the likelihood that such technological
innovation will occur and when.  There is a need to look beyond the
Clean Air Act requirements to what will be needed for air quality
forecasting in future decades.  In the meantime research is needed to
improve emissions models now so current needs can be met with
confidence until those needs are satisfied.


The software for the new models should be developed with open
architecture and standard interfaces to permit interchange of modules
with alternative capabilities and for different applications.  The new
software should be developed according to guidelines and functional
specifications that assure accomplishing the requirements of the
models.  The model structures themselves should be standardized.  The
software should be object-oriented and developed for application in a
distributed computing environment.



The software package for the new models should include exterior
modules and post processors to permit flexibility of use for different
areas and different size problems.  The programs should provide query
and browse capabilities to aid and expedite analysis of applications,
operations and problems.  The software packages should include an
array of utility programs for ease and efficient preparation and
manipulation of input and output information.  The package should
include graphical presentation capabilities and quick reaction
processors for use in responding to and providing support for

An information exchange should be established to organize and
facilitate technical assistance and feedback on program usage. 
Software source code should be available to users for potential
customization and modifications for unique situations and conditions. 
The descriptive material for algorithms should also be available to


The workshop discussions identified particularly important or new data
needs and recommendations for revisions of and research on data
collection procedures.

Land Use
Information is needed on the effects of different urban designs on
travel patterns.

Data on the travel behavior of minority populations is needed to
better determine the effects on travel in metropolitan areas where
they are a major population segment.

Trip Generation
Data is needed on changes in travel behavior over time in order to
modify forecasts to accommodate those changes.  Information is needed
on trip chaining as reflected in intermediate stops and short distance
movement of vehicles in shopping districts.  Daily and seasonal
variation in travel is needed to better understand the variability of
travel forecasts.  Information on weekend travel patterns is also
needed for better understanding the differences in traffic from
typical weekday forecasts; this is particularly important for
recreational travel but also for shopping trips.  Information is also
needed on travel patterns of non-residents, whether visitors or
through trips.

Information on the effects of congestion and travel time reliability
on choice of departure time is needed for better understanding
congestion phenomena and estimating the spreading of peak travel
periods.  Data on trip attraction characteristics of special
generators is needed to determine their effects on congestion. 
Finally the effects of telecommunications on travel attenuation should
be determined.

Mode Choice
Data needed for these models includes the nature, amount, and
variation of vehicle use within households as correlated to the
characteristics of those households.  Allocation of available vehicles
among trips and drivers within the household should be determined.   



Conditions and characteristics that affect ridesharing should be
identified.  Vehicle occupancy for non-work travel is especially
needed.  Travel patterns by bicycles and as pedestrians should be
identified along with the characteristics that influence decisions to
use those modes.  Data on telecommunications, telecommuting and
facsimile communications in lieu of travel should be obtained with the
conditions and characteristics of those choices.

Information is needed on the distribution of non-resident and other
inter-urban traffic on transportation networks.  Improved traffic
count data is needed for identifying current problems and for
validating travel model development.  The relationships between
transportation system conditions and changes and land use patterns and
changes should be identified.  Measures of speed variability and
acceleration and deceleration need to be improved for application in
network coding and for determining emission characteristics from
traffic assignments on networks.  This information should include
better identification of relationships between speed and traffic

Other Data Needs
Better information on emission model inputs is needed, such as
emission rates for cold starts, acceleration at varying rates, hill
climbing and hot soaks.  The percent of vehicle time spent in each
operating mode is also needed.  Information on commodity flows and
transport characteristics is another need and should include driver
work rules that influence those characteristics and driver logs for
actual itineraries.

Data Preparation
Guidelines are needed for collection of data to serve the travel
models.  These guidelines should establish standards, including the
expectations or requirements for data to be used by the models. 
Research is needed to determine the critical data elements and the
needed level of detail of data for the models.  It is also important
to collect data in a continuing process in order to identify trends
and other changes in key factors that affect travel behavior.  The
data collected must be in sufficient detail and accuracy for
validating the models.  The characteristics needed for the data to be
adequate for validation should be established by the research.

It is important to begin now to establish the characteristics and
criteria for data needed for models so that planning and acquisition
of the data can proceed in a timely manner so that it will be ready
when needed for validation.  It is especially important that the
requirements for data for the new models be established and that those
requirements are available to planning agencies so that data
collection efforts currently being planned are accurately designed to
provide data adequate for the new models.

Another criterion for data acquisition is to determine what different
data is required for levels of analysis.  Policy studies for example
may be able to use much more aggregated data as long as it is
regionally representative, whereas corridor or other subarea analysis
must have localized and more precise information.  Standards should be
established for data to be used by the models so that data collectors
and users know what to collect and why and how that information will
be used in the models.  The standardization should extend to data
format and data collection designed to assure that the necessary and
proper data is being obtained.

In designing the models and efforts to collect data for them,
consideration should be given to maximizing the use of existing
databases.  Where possible the models should be adjusted if feasible
in order to accommodate using existing databases, such as the Census
CTPP for example.  This does not mean that the models should be
"hammered" to use available data but that due consideration for
efficiency and economy should be exercised in specifying the models
and their data needs.  Probably the most important of the databases to
consider in establishing the data needs of models are those produced
by various geographic information systems.  These powerful databases
are a key to providing the detail required by the new models.

Data collection efforts should be based on statistical experiment
designs for the actual purpose and use intended.  What those designs
might be for different uses and conditions should be established
according to the model needs as part of the model design process. 
This may require research into the possible approaches and strategies
for experiment analysis and survey designs that will best fit the
requirements of the models.  The survey design and model development
process should also examine techniques to merge synthetic and survey
data since the synthetic data is potentially a strong attribute of
some of the newly developed models.  The survey designs will identify
the appropriate sample sizes for surveys, and special attention should
be paid to distribution characteristics and behavior of non-work
trips.  Another important consideration in data collection for
improved and new models is trends and other changes over time.  This
has been addressed in recent longitudinal panel surveys that revisit
samples periodically, and this approach should be considered for
application in the data collection for the new models.

Concern was expressed at the conference about the resource
requirements, especially the amount and quality of data needed for the
TRANSIMS model or any other simulation model.  It is important to
consider the staffing and funding implications of any new model under
development.  Those have obvious and potentially onerous implications
for agencies preparing to use the new models.  These concerns add
support to the recommendation that the model development activities
endeavor to maximize use of existing data sources rather than
requiring wholly new databases.  Data sources that should be more
strongly considered for exploitation are the Census CTPP and the NPTS. 
Another resource is SHRP information on surveys and syntheses of
practice.  The problems with detail, accuracy and timely availability
of these sources should be addressed in order to increase their
utility for the new models.  These databases offer especially
significant potential as the empirical basis for generation of
synthetic populations and activities.  Those synthetic databases will
rely on periodic updates from the CTPP and the NPTS that serve as a
real and consistent foundation and to identify changes to be
incorporated in the travel models.

Other issues related to data included concern about the privacy
restrictions on credit card data that may obviate its utility as a
source for the new travel models.  A specific recommendation is for a
data collection process to replace or redevelop the HPMS.  There were
recommendations for improvement in and expanded use of geographic
information systems.  The GIS database could serve as the framework
for the new travel models.  Another recommendation was for improved 


data structures such as using dynamic segmentation in the manner
employed for some GIS.

There is a need for research on survey methods, including alternative
types of surveys, sample sizes, survey designs, etc., to improve the
efficiency of data acquisition and data processing.  This research
should include examination of sampling procedures and design of survey
instruments.  The research should investigate and identify appropriate
experiment designs for transportation analysis and forecasting.  The
utility of stated preference surveys is a study of prime interest. 
The survey designs should be especially oriented to obtain information
for short trips, which are often lost in existing survey techniques. 
Time series data on activities and travel should be obtained to aid in
forecasting more accurately.

Land Use

Considerable research is needed on land use and development
forecasting procedures to improve the information available for urban
planning and to provide better information required to improve travel
forecasts.  This should include research on the effects of development
patterns on activity patterns generated in the travel models. 
Information for both near-term and longer period developments needs to
be improved.  A key concern of this conference was to assure that
"feedback" occurs between transportation level of service and
development allocation so that a reasonable equilibrium is established
between transportation service and land use.  The land use and travel
models must be carefully integrated so that the variability of spatial
activity distribution can influence the activity decisions and
behavior in the travel models.

An important aspect of both activity and development forecasting is
accurately forecasting demographic and economic conditions that affect
both development and travel behavior.  Research is needed to better
understand the social and economic factors that influence development
and travel.  Among the factors generally recognized as influential but
whose forecasting requires further research are size of households,
age and gender composition, life-style and place in life cycle, family
situation (i.e., single parentage) the roles of household members, and
how activities are allocated in the households.  All of these
influence choice of location of residence, workplace, and other
activities as well as travel behavior.  There is a need for research
into the methods of demographic and economic forecasting including
econometric procedures.

There is a need for research on land use forecasting models and
related procedures.  Paramount in this research would be improving
prediction of locations of residential, industrial, other workplaces,
and other activities.  Interest is in better understanding and
replicating the decision process and participants in that process and
their respective roles.  The role of real estate prices is a key
factor identified for consideration in this research.  Temporal
dynamics, the change in factors, conditions and their influences on
development decision processes should also be addressed.  The new land
use and development, location models forthcoming from this research
must be behaviorally oriented and related to activities in order to be
properly integrated with and produce information needed for the travel



models.  For the newly emerging travel models, it is important that
research of location decisions be oriented to understanding activity
locations rather than merely land use or development, recognizing that
the activity type and characteristics dictate locations and are the
principal influences on travel behavior.

The research leading to development of the activity and development
location models should consider relationships between the influence of
the marketplace and developer plans.  The interactive relationship of
urban form and design with travel behavior should also be studied. 
Models to forecast detailed or micro-scale land use in relation to
small area transportation services should be considered as well.  The
longer term evolution of urban development and land use patterns is
another appropriate aspect for study.  Finally a simple land use model
for application in smaller or less complex urban settings would be

Sample Populations

There are several issues related to demographic forecasting that are
recommended for research.  One of these should address whether
creating synthetic populations from samples yields an accurately
representative mix of the true diversity of population
characteristics.  Another concern is the examination of the continual
changes in the behavior of individual persons, households and
neighborhoods.  These characteristics need to be included in the
forecasting process if it is to be accurate.

Model development research should pay careful attention to activity
generation and forecasting, in addition to the conventional emphasis
on the travel aspects of the process.  Both the activity and travel
models must be based on behavior observed and developed in a well
designed empirical process.  Concepts such as development and change
of habits and adaptation to changes of conditions that influence
activity and travel decisions should be carefully researched.


The recommendations on training reveal that inadequate attention has
been given to these areas during the past decade.  The needs and
resulting recommendations fell generally into two categories:  target
groups for training and training needs.

Target Groups for Training
Training is needed for all levels of professional and technical staff
involved in transportation and environmental planning who must use
models to prepare travel forecasts, for policy/decision makers who use
travel forecasts for decisions, and for lay persons who are
stakeholders affected by the forecasts.  Special training is needed
for persons who are not computer literate.  A program of training
should be designed for entry, mid- and advanced practitioners,
including model users from MPOs state DOTs, cities, environmental
agencies and other organizations.  Of particular importance is the
need for this program to differentiate between the training needs of
the large and small MPOs.

The need for intensive professional training programs spanning several
months was identified, but consideration of the staff time constraints
was also cited.  Training programs should provide continuing 



professional education with certification and credit.  A particular
need is for short duration continuing education to help keep
practitioners up-to-date.

Curricula for university transportation planning courses should be
developed.  Tuition assistance, fellowships, and internships should be
provided to encourage undergraduates to study specialties related to
transportation planning.

Training Needs and Programs
A wide range of training topics was identified.  These include topics
that should be covered during the next five years and others necessary
for using model improvements being developed in the longer term.  The
training should begin with an assessment of current training
activities in order to improve and complement them rather than
duplicating them.  That information would in itself be valuable to
practitioners.  Training courses should cover both theory and
practice, and should include case studies to illustrate implementation
in different situations according to need.

Short-term training needs include appropriate practices for use of the
existing models, methods and practices from outside the United States,
how to use and implement quick-fixes, how to perform major investment
analyses, how to calibrate distribution models, and how to implement
the requirements of ISTEA with existing tools.  Training should be
provided for integrating travel models with GIS, translating simple
concepts into computer code, guidance on software selection and how to
install new software for existing model program packages.  The
training should address the use and application of model forecasts,
not just how to use the computer programs.  The latter is a necessary
part of deployment.

Training that should be considered for longer-term needs includes how
to transition from existing models to those being developed in the
TRANSIMS project, how the new models can be used in the transportation
decision process, how to forecast the new variables required by the
models, and training in new data requirements, collection and storage

Training programs should be designed to accommodate the differences
between areas and agencies and between appropriate practice and best
practice.  Such programs might include in-house training during
implementation of new procedures of software vendors.  The Florida DOT
and New York Metropolitan Transportation Commission programs are
considered good examples of effective training.

The program should include extensive use of computer based training
and video training materials.  These programs should employ newly
developed multimedia techniques and should include hands-on guidance
using case studies and demonstration projects.  It is important for
training to be carefully linked to the research and development for
improved models.

It was recommended that there be strong federal leadership in the
development and deployment of training programs.  This should include
use of triennial reviews to determine MPO practices and needs and the
use of project grants for project specific training.  The various
training programs should be concurrently available to include all
interested participants but avoid a long lag time in reaching all



The University Transportation Research Centers could be used to
develop and deploy the training programs.  Universities would provide
hands-on training for software.  The training should be provided at
outside universities and should include periodic in-service and mid-
career activities as well as for entry levels.  Training "circuit
riders" should be available to visit MPOs and provide on-site area and
agency specific assistance.  All trainers should be certified for the
specific course material/training they are providing.  Consultants
should play an active role in providing training although there was
concern over the loss of feedback and control over the program when
contractors are used.  It was recommended that training mandates be
supported by dedicating part of planning program budgets.  Possible
approaches are to use university transportation research centers, for
MPOs to fund fellowships for work on their staffs, or for their staffs
to be training at universities or through other organized efforts.

Program Guidance

A strategic plan for travel model improvement, development, and
related efforts should be prepared to guide the process.  New
procedures from any of the tracks of this program should be
recommended but should not be required for use in the planning

There was concern about the need for more communication with the Los
Alamos National Laboratory regarding the TRANSIMS project.  Both the
user and the research communities need to be working more closely with
the Los Alamos team to communicate their needs and ideas and to
provide guidance as requested by the team and the sponsors.  It was
suggested that an avenue communication could be established by Los
Alamos hiring a person experienced in travel forecasting as currently

Communication on the content and status of the Program is needed for
support, participation and acceptance of the ultimate products.  To
accomplish this, special communication channels should be established
to facilitate sharing information on and the status of the TRANSIMS
project.  The media used could be video conferencing, video taping, or
newsletters.  Interactive remote transmissions would be helpful for
all parties, explaining things to the audience and providing guidance
to the researchers.  The communication must be carefully designed to
clearly translate complex new concepts for understanding by
practitioners as well as constituents of the planning and forecasting

Increased communication among MPOS should be encouraged to facilitate
sharing of problems and solutions.  This can be accomplished using
Internet or other bulletin board services.  Communication about model
development work in other countries is an important consideration for
the Program as well.

A need for additional funding was expressed to broaden the research in
universities and other research centers to complement the TRANSIMS
efforts.  One possibility would be stronger financial involvement of
FTA, NSF and state DOTs.  The increased funding is needed for
activities in areas other than the TRANSIMS work in Track C,



particularly for training and other dissemination of new and improved
models.  Funding of case studies and other demonstrations should be
provided to stimulate developing innovative practice.  Some of that
funding should be provided for research by and for MPOs.

Concern was expressed about the amount of resources needed to develop
and use the new travel models.  In particular the staffing and
computing requirements and related funding demands seem to be beyond
current funding availability.  Special attention is needed to
alleviate those problems.  This underscores the need for strong
training efforts to develop new and improved staff resources.

Peer Review
It was strongly recommended that a peer review process be established
for all aspects of the Travel Model Improvement Program.  Peer groups
should be formed to review proposals and project progress, results,
and products.  These groups would provide guidance, support, and a
framework for judging the reasonability of model structures and
factors incorporated in the models.  The review groups should be
composed of professionals experienced in the several disciplines that
influence or are affected by travel forecasts, including academicians,
consultants, other practitioners, and vendors.

Professional Participation
Comments in the workshops included the need for Program activities to
be open to professional review and comment in addition to the peer
review process.  Establishing an association of transportation
professionals that would recommend or endorse education and training
programs was another recommendation.  Such an umbrella association
could encourage forming regional transportation planner practitioner
groups.  Working more closely with ITE, ASCE, and AASHTO, the existing
professional organizations.

Research Options
These options are more general recommendations for research than
specific items mentioned elsewhere in this document.  Research should
be conducted in Track C on other new modeling approaches as options to
TRANSIMS.  Research conducted in Europe and elsewhere outside this
country should be considered for possible contributions to this
program.  A dynamic systems framework should be explored for domestic
travel model research.  A university research program funded by small
three-year grants should be established for possible contributions to
this program.

Transferability of research findings among metropolitan areas should
be a major or concern of the Program.  To accomplish this, the
research should identify how procedures can be tailored to suit the
particular needs of different locations and decisions.

Early Products
Early access should be provided to interim products of the TRANSIMS
project, particularly for advances usable in air quality assessments. 
The U.S. DOT should establish an information clearinghouse that would
facilitate dissemination of information on the Program and
distribution of Program products and other procedures potentially
useful to the transportation planning community.



Concerns were expressed about how to deploy the new and improved
models.  An issue in this regard is the need to assure that
descriptions of the Program do not establish unsatisfiable
expectations.  Implementing results of the Program, whether improved
or new models, must consider the problems and needs of practitioners
as they transition from previous procedures.  The transition process
should be carefully conceived, led and monitored by the U.S. DOT to
establish guidelines and standards for improved procedures without
disruptive mandates.

The model documentation, particularly for TRANSIMS, must be
understandable to the Program audiences, policy/decision makers, the
general public and other affected parties as well as to practitioners
using the models.  To accomplish this, one page summaries of various
existing as well as new techniques should be prepared.  Documentation
of recommended modeling practices should include alternative
strategies for different types and levels of usage, particularly
reflecting the varying needs of different MPOS.

A variety of methods should be used to disseminate current and future
information.  As with the training program, a strong federal role was
encouraged.  A federal clearinghouse for information should be
established and made accessible through different media such as
Internet, electronic bulletin boards, and regular mailings.  A
resource library of models and data sets needs to be established.

Results of the Program should be made available continually for
practitioners and affected audiences as soon as the Program and other
products are tested for validity and available.  These results would
include periodic enhancements and refinements as they occur and
results of case studies and demonstrations.  It may be appropriate to
have different dissemination procedures or media for various groups,
depending on the level and nature of their involvement in the Program
and the planning process and on the level of modeling they employ. 
The Planning Methods Applications Conference is a medium for timely
dissemination that should be continued.

Information considered important for timely dissemination includes
recommended modeling practices and analytical techniques, data
collection procedures, new products as they are available, the results
of case studies and demonstrations, and progress of development of
TRANSIMS.  Particular attention should be dedicated to describing
which techniques are most appropriate for different locations and
situations and how various techniques can be adapted to suit
particular conditions.

The dissemination of existing model improvements and analysis
techniques should begin as quickly as possible.  A catalog of
available tools should be developed complete with summaries of methods
and products, contact names and phone numbers.  Guidance should be
provided on standards of good and best practices in the form of
manuals that codify existing practices and available model
improvements and that include flow charts for each technique or
application.  "How To" manuals on modeling, surveys, data collection/



storage, and transit, external/nonresident, and special generator
travel were also noted as needs.  Information which compares existing
and now software products, and better, more user friendly software
documentation are also immediate needs.
Sample RFPs for various study types should be made available, as
should timely notification of conferences and distribution of
conference results.

Regular updates on TRANSIMS and other application test cases should be
provided.  Notice of new data requirements, collection and storage
procedures should be given at the earliest possible date.:

A partnership between the federal agencies, state DOTs and MPOs would
assure that the most current information is available to all
interested persons, particularly individuals involved in related
fields.  MPOs should establish an information exchange to assist in
the distribution of materials and practices.





Presentation Abstracts
Activity-Based Modeling and Policy Analysis

by Clarisse V. Lula, Research Decision Consultants, Inc.

This project is designed to conduct activity-based research for the
Metropolitan Washington Council of Governments (MWCOG).  The goal of
the project is to develop a regional policy model which will be used
to assess the ways in which an individual's travel behavior changes in
response to the introduction of regional transportation control
measures (TCMs).  This activity Mobility system (AMOS) is a
microsimulation approach in which changes in individual's travel
behavior are based on treating travel as a demand derived from the
distribution of his or her daily schedule of activities in time and
space.  As such, the approach provides the structure for examining the
impact of policies on a broad range of behavioral factors including
changes in time-of-day of travel, the sequence of activities and
trips, trip generation, trip chaining, destination choice and mode

The prototype implementation of the activity-based approach (AMOS) for
the MWCOG region is formulated as a "regional policy model" that
estimates impacts on travel indices and emissions in the short- and
mid-term.  The system is a dynamic system that will allow behaviors to
change gradually over time and allow for behavioral inertias. 
Individuals' potential response to changes in their travel environment
will be examined using a survey conducted in the region that has been
designed to obtain both baseline activity-trip patterns and people's
stated preferences in response to potential changes in their travel
environment.  The sample survey will be weighted to reflect the socio-
economic and demographic profile of the region.  Quantitative changes
in behavior will be derived using neural network models, and
encapsulated in a TCM policy response generator that indicates an
individual's initial change in his or her travel pattern in response
to the introduction Of TCMs.  As well, people's preferences for
ancillary modifications to their trip patterns will be further refined
based on the survey results and checked against a rule-base for
consistency with existing constraints on their schedules.  The model
system is designed to search for an "acceptable", or "satisfactory"
new activity-travel pattern for the individual through a trial and
error procedure that simulates people's learning behavior.  The
resulting changes in travel behavior are either accepted or rejected
based on various time-use utility functions.

The activity-based modeling components have been designed and tested
on a preliminary basis.  The model has been initiated by MWCOG and is
being integrated into its existing system.  The AMOS survey has been
designed and will be fielded in the fall of 1994.  It is expected that
the neural network modeling will begin in late 1994.  Implementation
and testing of the AMOS model system will begin in early 1995 using
AMOS' 1994 survey as well as MWCOG's 1994 household travel survey.





Identification of Short-Term Travel Mode Improvements

by Thomas Rossi, Cambridge Systematics, Inc.

The purpose of this project was to identify existing methods and
applications to improve current urban travel models in the short term. 
In general, the identified improvements are methods and procedures
that have been implemented in some urban areas, although many of these
improvements may not be well known.

This project was performed by Cambridge Systematics, Inc. and Barton-
Aschman Associates.  In addition to the experience of the project
consultants, other travel demand modeling experts and practitioners in
the United States were canvassed to identify existing model
improvements.  Those interviewed included MPO and state DOT staff
members, consultants, academic researchers, and U.S. DOT officials. 
The findings are documented in the report "Short Term Travel Model
Improvements," dated August 1994.  This report may be used as a
reference for identifying potential model improvements and as a guide
to identifying model improvements for further documentation.

The project identified 12 general categories of model improvements. 
These are documented in the report as follows:

Travel Surveys
Methods for conducting various types of travel model related surveys
and survey processing issues such as expansion and geocoding are

Modeling Non-Motorized Travel
Most modeling processes in use do not incorporate pedestrian or
bicycle trips.  Because mode choice can be affected by the types of
variables included in the model, most new models are now incorporating
these options both as primary travel modes and for transit access. 
The review of modeling procedures for non-motorized trips also
included a review of methods to incorporate measures of the pedestrian
environment into the travel models system.

Land Use Allocation Models
The most widely used land use allocation models were identified.  The
data needs, necessary resources, advantages and disadvantages, and
alternatives to using a land use model were prepared.

Dynamic Assignment
Dynamic traffic assignments are not widely used in models employed by
most MPOs although there is available software.  The uses, advantages,
and disadvantages of dynamic traffic assignment, as well as
appropriate situations for use and available software, are identified.

Air Quality Analysis Methods
Methods currently used to predict trips by vehicle operating mode
(i.e., hot/cold start) and to adjust speeds both during and after
traffic assignment are reviewed.  The necessary resources to implement
these procedures and the drawbacks are discussed.



Modeling Trip Chaining Behavior
No current procedures to account for trip chaining were identified. 
Research into methods to model trip chains and how to incorporate
these into the travel modeling process, however, are reviewed.

Mode Choice Modeling Improvements
Numerous mode choice issues including incremental logit modeling, HOV
modeling, transit captivity, transit transfers, integration of mode
choice with other steps in the model process, transferring models
between different areas, use of Monte Carlo simulation, and modeling
toll facilities are identified and reviewed.

Parking Analysis Procedure
Parking is an issue that is not handled effectively, if at all, in
current travel models.  Methods to reallocate trip ends to parking
locations rather than destinations, to analyze the effect of time-of-
day on parking and to model parking costs are discussed.

Time-of-Day Models
Most areas currently factor daily trip tables to reflect specific
times or time periods.  Methods to factor daily trip tables to peak
periods, to reduce peak hour trip tables to reflect to network
capacity constraints, to model peak spreading, and to model time-of-
day prior to trip distribution are identified and discussed.

Trip Table Estimation
Available procedures to estimate trip tables from data such as traffic
counts, the available software and discussion of necessary resources
are presented.

Modeling of Trip Generation Input Variables
Modeling trip generation inputs such as auto ownership, employment and
household characteristics using existing data are reviewed.  Most of
the available models are based on either logit formulas or regression
equations and represent a kind of choice model.  Household simulation,
in which household decisions such as location, car ownership and
household size are estimated, is an area that is relatively new, but
is being pursued in several areas.

Trip Assignment Issues
Methods to code transit access using GIS, analyze toll highways, and a
discussion of instability issues in saturated networks are included.



Travel Survey Manual Update

by Thomas Rossi, Cambridge Systematics, Inc.

The need for this work was identified subsequent to completion of the
report "Short Term Travel Model Improvements," dated August 1994.  The
last travel survey manual was prepared 20 years ago.  Since then, a
number of new methods, types of surveys, and analysis procedures have
been developed.  Travel modeling procedures have advanced, and
technological improvements have made conducting surveys and analyzing
data more efficient.  The objective of the project is to develop a new
manual of travel survey techniques.  This manual will be based on the
advances made during the past two decades and will draw upon the
experience of a number of recent surveys conducted at various
locations in the United States.  Particular attention will be paid to
the needs and uses of travel survey data, especially emerging demands
on surveys due to new transportation planning requirements, air
quality analysis needs, and ongoing travel model improvements.

The manual will cover a variety of survey types, including:

   -  Household travel,
   -  Transit on-board,
   -  Vehicle intercept,
   -  Commercial vehicle/freight,
   -  Workplace/establishment/visitor,
   -  Panel,
   -  Stated preference,
   -  Special generator, and
   -  Parking.

Detailed descriptions on how to conduct the various types of surveys
will be provided.  The manual will include information on survey
administration, survey design, sampling, data collection procedures,
pre-testing, data entry, verification, and data analysis.  The issue
of geocoding will be discussed, and the purpose, methods, and data
sources involved will be described.  Additionally, the manual will
include a listing of recent survey information including contact
names, survey forms, and requests for proposals.

The revised manual is expected to be completed by January 1995.





The Effects of Land Use and Travel Demand Management Strategies on
Commuting Behavior

by John Suhrbier and Susan Moses, presentation by Thomas Rossi,
Cambridge Systematics, Inc.

There is considerable interest in the effects of urban design and land
use characteristics on individual transportation choices.  The
underlying assumption is that these employment site characteristics
have an important influence on a person's willingness to commute by
transit, ridesharing, bicycling, walking, or modes other than drive
alone.  Furthermore, the selection of transportation demand management
(TDM) strategies that an employer may choose to implement should be a
function of surrounding site characteristics, and the combination of
site characteristics and TDM strategies can have a positive
interactive effect in influencing an employee's choice of commute
travel mode.

For this project, an integrated database of land use characteristics
and TDM strategies was developed for specific locations in Los Angeles
County.  The integrated database was constructed by adding land use
and site information to the "Regulation XV" data set of the South
Coast Air Quality Management District (SCAQMD).  The SCAQMD data set
includes information about aggregate employee travel characteristics,
and the incentive programs offered by employers.  This integrated data
set was then analyzed to explore the interactions that may exist
between TDM programs, land use, urban design characteristics, and
employee mode choice.  The primary objective was to develop
conclusions about the combined impacts of land use and travel demand
management strategies on employee travel behavior.

The technique of Principal Components analysis was used to group land
use variables into composite variables representing site
characteristics.  Five specific land use/urban design characteristics
were defined:  sites perceived as safe, aesthetically pleasing urban
sites, sites with a mix of land uses sites with a diversity of
convenience-oriented services, and sites with good accessibility to
services.  Standard analysis of variance techniques were then used to
understand the effects of these composite land use variables and TDM
programs on travel behavior.

It was found in the study that financial incentives are the most
effective TDM strategy for reducing the drive alone mode share.  At
sites where financial incentives were offered, the drive alone share
decreased by 6.4 percent from the time that the Regulation XV programs
were implemented, compared with a 1.7 percent decrease at sites
without financial incentives.  For each land use/urban design
category, financial incentives accounted for the majority of the
reduction in the drive alone mode share.

The analysis revealed that the effectiveness of TDM programs did
increase in areas with supportive land use and urban design
characteristics.  The data revealed that when financial incentives are
present, the greatest reduction in the drive alone share is realized 



in areas with aesthetically pleasing urban character.  The drive alone
mode share at these sites is at least three percent less than at sites
exhibiting any other land use characteristics analyzed.  This appears
to be the result of the availability of alternative modes and the
quality of the environment.  Sites with a preponderance of
convenience-oriented services realized the next greatest reduction in
the drive alone share, followed by sites with good access to services,
sites with the perception of safety, and sites with a mix of land

TDM strategies have a larger influence on reducing the drive alone
mode share than do land use characteristics when each is considered
individually.  The findings, however, further revealed that there is a
positive cumulative impact on increasing average vehicle ridership
(AVR) and reducing drive alone mode share when both financial
incentives and one of the five land use characteristics analyzed are
present.  The impacts are not linear in that the cumulative effect is
less than the sum of the parts.

The TDM programs examined are most beneficial in increasing the level
of ridesharing.  This increase, however, results not only in a
decrease in the drive alone mode, but also in a decrease in transit,
walking, and bicycling trips.  Transit and walk/bike mode shares are
highest at sites with supportive land use and urban design
characteristics.  This further indicates that mode choice is
influenced by both land use characteristics and the availability of

Employer-provided transportation assistance programs have a small but
statistically significant impact on reducing the drive alone modal
share (-5.3 percent) and increasing the AVR (from 1.223 to 1.285) at
sites having a mix of convenience-oriented services.  Assistance
programs alone were not found to have a significant impact on either
the drive alone share or AVR at sites with other land use

While the average level of walking and biking over all the sites
surveyed was 5.4 percent, selected sites had post-implementation mode
shares that were two and one-half times this level.  These sites were
characterized by land use and urban design characteristics that
encourage alternative modes of travel for the work trip.  Furthermore,
these sites offered financial incentives in the form of walk and
bicycle subsidies that were well above the average for all sites



Improved Network Models:  Multicriteria Traffic Assignment, T2

by Robert B. Dial, Ph.D., U.S. DOT/Volpe Center

T2 is an equilibrium traffic assignment model that solves the
following problem:  given a network whose arcs have two disutilities,
call them cost and time:

   ce=cost on arc e
Te(xe)=total arc e, a function of total flow on the arc
   xe=total flow on the arc e,

assume each trip chooses a path p that minimizes its particular
perceived generalized cost where gp(a), where

   gp=cp + atp 
   cp=ce = the out-of-the pocket cost of the path
   tp=te(xe) = the time on the path
   a=the "value of time,"

and the value-of-time parameter a is a random variable, with arbitrary
given probability density that may vary by o-d pair.  Now, given an o-
d matrix of total trips, the problem is to find an equilibrium traffic
flow, which has every trip using a path that minimizes its particular
perceived gp(a).

T2 generalizes conventional traffic assignment by relaxing the value-
of-time parameter in the generalized-cost function from a constant to
a random variable, with an arbitrary probability density function. 
Its application potential spans a wide domain of currently difficult
problems in traffic, highway and transit planning - including
simultaneous mode/route choice, congestion pricing and parking

A model was defined, its mathematical formulation cast, and solution
algorithms designed.  The algorithm is very space efficient: it can
find the total arc flows at equilibrium without having to save
individual arc flow for each value-of-time a.  No real information is
lost, since these latter flows are not unique.  Hence, T2 can run on
networks as large as those for conventional traffic assignment.

A prototype code running under TransCad demonstrates the model's
sensitivities on toy networks.  By the end of the year, a "production
code," will be available along with statistics describing its
performance on a PC solving networks having up to 100,000 nodes.

Planned future work will increase the number of criteria from two to
three, and implement the model as a dynamic assignment.

These results appear in the technical paper:

Multicriteria Equilibrium Traffic Assignment:  Basic Theory and
Elementary Algorithms, Part I, T2:  The Bicriteria Model





Equilibrium Conditions in Land Use and Travel Forecasting

by Stephen H. Putman, Ph.D., S.H. Putman Associates, presentation by
Frederick Ducca, Ph.D.

The consistency requirements of both ISTEA and CAAA explicitly
recognize the inter-relatedness of transportation and land use and
assume the need for a proper representation of those linkages between
land use and transportation phenomena which can significantly alter
the outcomes of long-range forecasts.  Much of the discussion in
transportation and land use planning practice, when it does
acknowledge the potential importance of these interactions, addresses
the issue in terms of requirements for equilibrium solutions.  What is
not known is:  a) Whether such solutions are computationally
practical; b) Whether they will differ significantly from solutions
achieved in the absence of formal linkages between the two forecasting
activities; and c) Whether they will actually be better forecasts of
the future land use and transportation reality.

In order to address the above issues and improve its current
forecasting process, the Metropolitan Service District (METRO), in
cooperation with the Oregon Department of Transportation and the
Federal Highway Administration has undertaken to perform a
comprehensive series of tests to determine the criticality of the
consistency issue with particular reference to the land use feedback
to transportation models, identify conditions under which it must be
addressed, and make technical recommendations for methods to modify
existing procedures.  It is expected that the results of this study
will have implications not only for METRO but nationally for other
users of the travel demand process.

Following extensive reexamination of model structures and subsequent
model recalibrations, four land use/transportation model sensitivity
experiments have been completed for a single forecast period 1990-
1995.  For all the experiments, the EMPAL/DRAM employment and
household forecasts were made at a 100 zone level of geographic
detail.  Two levels of geographic detail were tested for the METRO
travel demand models and trip assignment procedures.  The sketch level
uses an aggregated network specification, and both travel demand and
trip assignment are estimated for a network with 100 load nodes
exactly corresponding to the EMPAL/DRAM zone centroids.  The detailed
network specification has 1189 load nodes and 18,960 one-way links. 
When running experiments for the detailed network, the EMPAL/DRAM
forecasts are disaggregated from 100 zones to 1189 traffic analysis
zones.  The output of METRO's travel demand and trip assignment models
(i.e., a 1189x1189 travel time matrix) is collapsed to a l00xl00
travel time matrix for use in EMPAL and DRAM.

For both levels of network detail, two configurations of travel
demand/trip assignment (inner) iterations and linked land
use/transportation (outer) iterations were used.  The first
configuration uses a single travel demand/trip assignment iteration
within each linked land use/transportation model iteration.  The 



second configuration uses three travel demand/trip assignment
iterations within each linked land use/transportation model iteration. 
The three inner iterations begin with an estimation of travel demand
based on travel times from the previous outer iteration and the
current EMPAL/DRAM forecast of employment/household location.  The
estimated travel demand is used as an input to the trip assignment
procedure, and the resulting travel times are used to re-estimate
travel demand.  The final set of travel times is based on an
assignment of the trips produced by the third travel demand

For the sketch level of network detail, both configurations of the
land use/ transportation model converged to the same solution in three
outer iterations.  Each inner iteration required approximately 14 trip
assignment iterations.  At the system wide equilibrium solution, the
user equilibrium (UE) objective function for trip assignment is
minimized and household consumer surplus is maximized.  The solution
trajectory for the model with three inner iterations is smoother than
the solution trajectory for the model with one inner iteration, but
the rate of convergence is essentially identical.

For the detailed network, both configurations of the land
use/transportation model system converged to the same solution in
three outer iterations.  Each inner iteration required approximately 8
trip assignment iterations.   At the equilibrium solution, the UE
objective function is minimized and household consumer surplus is

For the sketch network specification, approximately 20 percent of
trips are intrazonal and are not assigned to the transportation
network.  For the detailed network specification, only 3 percent of
trips are intrazonal.  At the equilibrium solution for the sketch
network, total VHT equals 130,123 and total VMT equals 3,897,550 with
average link speed equal to 29.95 mph.  At the equilibrium solution
for the detailed network, total VHT equals 172,394 and total VMT
equals 4,947,882 with an average link speed of 28.7 mph.  As a result,
household consumer surplus is higher when the sketch network
specification is used.

These tests show that there is a significant difference in the
outputs, of both land use and transportation variables, from the
equilibrium solution procedure as compared to the traditional linear
four-step procedure.  The differences are present for both levels of
geographic detail and for various levels of network congestion.  An
auxiliary set of tests done with data for the Los Angeles region
yielded similar results.

In the next phase of the project, these tests will be extended to
longer time horizons and will be examined more closely for the exact
sources of the differences which have been detected between the
different model system configuration results.



Travel Model Improvement Program:  TRANSIMS Presentation
[This is an edited transcript of the August 16, 1994 conference
presentation on TRANSIMS by Los Alamos National Laboratory staff.]

Introductory Remarks

by Darrell Morgeson, Ph.D., Los Alamos National Laboratory

The presentation this morning will explain the technical activity and
direction of TRANSIMS.  We will describe the algorithms and
methodology.  As we go through that, we will point out places Tracks B
and C might merge or complement one another, both in the near and long
term.  We will also describe how the systems architecture and
formulations may accommodate and integrate with the research that
others have done and are doing.

Fred Ducca mentioned that we began working on TRANSIMS about two years
ago.  A New Mexico organization called the Alliance for Transportation
Research seeks to draw out the particular strengths of the
laboratories and universities in the state, and in this case they
apply to transportation issues.  Los Alamos National Laboratory has
been working with simulations for about ten years.  Most of those have
been for the Department of Defense, and a lot of our methodologies
draw from that.  Early on we developed an interest in environmental
issues, driven largely by the Clean Air Act.  The work reported here
is requirement and policy driven, starting with the questions of what
is the computational framework which will satisfy those issues.  This
work was not restricted to use the computing power on your desktop
today, personal computers and the current generation of computational
technology.  The work is aimed at machines that will be affordable
and effective for you by the end of the century.  If you just trace or
graph how computing power and performance is going relative to the
price, you will realize that some staggering things are going to be on
your desktop.

The TRANSIMS team personnel have done a lot of reading to learn the
four-step method and other practices used today.  One piece that I
read said "under the assumption that you cannot compute every
household."  That is not the assumption of out work.  Our assumption
is the opposite of that.  TRANSIMS computes every household and every
individual.  We, and others internationally, have shown that can be
done.  But computing at that level in large metropolitan areas
introduces a new issue of systems science into the process. 
Simulating at this level of detail requires and generates a tremendous
amount of data.  In Albuquerque, for example, a ten minute simulation
of traffic along the interstate highway, including about thirty or
forty thousand vehicles, required about 10 kilo bites of data.  That
is about equal to two or three versions of the Encyclopedia
Britannica.  For a twenty-four hour period, not only does
understanding and interpreting the data become a problem, but storing
it as well.  Our project will have to develop ways to look at these
large volumes of data and pick out patterns that are of interest and
importance.  The systems science issues are very important here if you
expect to compute at that level.  We have done a lot of work in  



Mexico City identifying the sources of emissions, how complex air
chemistry and air mixing models go together to represent the Mexico
City air pollution.  We have measured that air over the last eight
years and perfected those models so that they are probably among the
best predictors available of what happens to the environment due to
heavy sources of pollution.

To produce better information related to environmental issues, these
models require a certain level of detail and microsimulation. 
Averages of required data are not sufficient.  With these models you
cannot use averages.  You cannot mix everything together and get an
average speed on roadway segments to input to the environmental models
that we are examining.  Acceleration, engine temperature and cold
starts make a difference.  If they make a difference at a casual
level, they must be modeled in order to accurately predict their
effects.  TRANSIMS tracks every car, every driver, every stoplight,
acceleration, deceleration, braking and turning.  We also use
information on roadway grade.  All of that is done for one second
intervals, and that feeds the environmental model.

Given that you are going to simulate in that detail, the question is
how do you know what is on the roadway.  How do you treat demand?  You
do not just throw all the vehicles out there and have them interact. 
So we produce trip plans.  This is a very simple and straightforward
concept.  In the model every individual has a set of activities that
he or she wants to engage in.  This is done on a household level. 
Those activities and their destinations together with data on the
behavior of individual people in the household, their income, and
various demographic variables, feed into the trip planner.  The
planner determines what particular roadway segments, bus segments and
rail segments that each individual will use.  Implicit in the trip
planner are intermodal decisions.  We account for intermodal trips
inside the trip Planner.  For the entire population for the entire
twenty-four hours, we account for each trip decision implicitly. 
Drivers and households resolve conflicts among themselves as they do
in the real world.  They decide to leave earlier in order to avoid
congestion.  They decide which route to take and/or which mode and so

The information that feeds the trip planner is obtained from
demographic and land use planning models.  The demand is estimated for
people and individuals, and for commodities and freight.  Once this
computation machinery is integrated, it doesn't care whether it is
moving a box or a person.  It simply has identified the demand to get
from Point A to Points B, C and D throughout the day.  This process
requires a lot of data, and we have identified some good sources of

Over the last 20 years, the IRS and financial institutions have
adapted to the use of credit cards in lieu of green stuff in our
billfolds.  The information they have on each of us is beginning to be
scary.  Some of that information is available and usable.  The story I
use to illustrate this is that when we first bought a horse three
years ago, I bought my first piece of tack.  Within three weeks I was
getting three or four different horse magazines, asking me to order
items.  How did they know?  They knew because I used a credit card for
that first purchase.  All of a sudden they can have my complete
demographic spending pattern.  The magazines I did not order from, I 



don't get any more, and the ones that I do order from, I now get all
the sister versions of those.  We are in a society in which
information available about individuals is extraordinary.  You can
exploit that information to address some simple things like vehicle
ownership.  Some of the data we have struggled to obtain is now
readily available.

TRANSIMS is a large and extensive project.  It is one of the largest
undertakings we have ever done.   We are in the formative stages of
the project now.  We are collecting information.  We are looking at
ideas.  We want you to give us your ideas, your best advice about what
we are not doing, what we are doing that is wrong and if you think our
pursuit is worthwhile.

We want to interact with the users.  We can learn a lot that way.  We
may change our opinions, but clearly, as contrasted with merely a
research endeavor, we are going to build something.  Our intent is to
make this available, not five years from now, but portions of it as
quickly as possible so that we satisfy the greatest needs quickly and
early with our products.  But to know what is needed, we have to learn
from the users.  Clearly there are many things that we are not doing
that others have done and can do better.  We call that related
research activity demand.  A part of that is understanding what drives
people to go from Point A to Point B during the day.  We have proposed
an outreach program to assist us in interacting with other relative
research important to this project.  There are methodologies being
developed, Bob Dial mentioned one yesterday and there are others as
well, that are quite good that may be relevant to this project.  All
of these efforts are part of the consideration of requirements and
methodology which leads to a design.  So this formative phase of the
project is the time for information to come in and be shared so that
we can better understand what the architecture of TRANSIMS ought to

Today we are going to show you what we did in Albuquerque.  We wanted
to know if we could compute and plan two million trips over twenty-
four hours for a city with less than 500,000 population.  We did it. 
We know we can do that.  We wanted to know if we could simulate a
large number of vehicles fast enough even on medium size machines
today.  We did that, and I will show you some of the results.  And, we
wanted to know if we could learn anything by doing this.  Once you
compute at that level, is it decipherable?  Does it show things like
latent demand?  Indeed it did.  Did we validate it; did we calibrate
it?  No.  That is yet to come.  But applications are important.  Our
intent over the next five years is to pick two major applications in
two metropolitan planning organization areas to test key policy
issues.  We want to use this framework to examine them and ask if this
makes sense.  Out of all of this there is an interesting connection to
Track B.  It is the connection between the data, models and
simulations; how do you build them up?  One of the things we can do is
use simulations as a basis for understanding the problem.  In fact, I
learned way back in graduate school that if you can understand the
problem any other way, do not simulate it because that is expensive,
time-consuming and difficult.  Many of you already know that.  But
when the problem is otherwise intractable, when it defies your
imagination, your insight and your intuition, then simulate.  It does
not mean you have to simulate forever.  What you are looking for are



insights at the cause and effect level.  And on the basis of that, you
are able to develop, in many instances, a model that is simple and
easier to use, uses less data and is quite satisfactory for the
intended purposes.  One example of this is that our simulation is
tracking things here in detail.  Perhaps all of that detail does not
have to be tracked.  Perhaps not all of those variables are important,
but until we simulate at that level, we do not know which items are
important.  So, this symbiotic relationship between the data that we
get, how it feeds the simulation, the data that we produce for the
models, all of that fits together in very important fundamental ways.

This morning we have three presentations on key aspects of TRANSIMS. 
First, Vernon Loose is going to talk about applications and
requirements driven by the Clean Air Act and ISTEA.  Then, Mike
Williams will talk about what policies and requirements initiated this
project.  And, finally Steen Rasmussen will conclude by talking about
the system architecture.



TRANSIMS Model Requirements As Derived From Federal Legislation

by Vernon Loose, Ph.D., Los Alamos National Laboratory

Our program plan calls for us to investigate and document the
specifications that TRANSIMS should have in order to address the
issues and requirements.  We are circulating the requirements paper in
draft in order to seek your input, your feedback, your reactions.  We
need to get this right in order for TRANSIMS to be a useful model. 
So, we are very open and in need of your feedback to understand your
requirements.  That is why we are starting out at the beginning of the
development of the model to address these issues.

Our program plan also calls for us to develop two applications within
the five-year development period of the TRANSIMS project.  We have
begun to investigate the issues and requirements through discussions
with MPOs and others of the user community to identify sites,
interests and useful and necessary policy issues for which TRANSIMS
can provide useful information.

Our perspective is that TRANSIMS is a response to needs.  The needs
are to satisfy requirements in legislation but also requirements from
your policy environment, the questions your policy and administrative
people are asking as well as the community at large.  You have
indicated that the procedures developed over the last 30 or 40 years
are no longer adequate to accomplish what you need to do.  So that is
where we start from.  We start from the need you express for something
to address the questions in this new policy environment.

And, if I can use an economist's term, TRANSIMS is a response to a
demand from the user community.

The policy environment that the user community is facing is based on a
foundation of state and metropolitan goals and objectives for the
transportation system.  The Clean Air Act Amendments of 1990 and the
Intermodal Surface Transportation Efficiency Act of 1991 are major
contributors to the issues and requirements that TRANSIMS has to
address.  And in particular, the ISTEA points to the conformity
determinations as requirements to develop the transportation system.

The 1990 Clean Air Act Amendments established attainment standards for
six transportation related pollutants.  The Act requires
transportation planners to determine that major capital improvement
projects will not increase emissions.  That is, the traffic control
measures and projects proposed must be pollution neutral.  Title 1 of
the Act also mandates that most significant transportation projects
must have emissions analyses supporting them.  More importantly, the
focus of the Amendments is that the new conformity standards require
planners to prepare long-range travel demand forecasts and to conduct
air quality analyses with sufficient detail to predict the levels of
pollution due to increased travel and to changes in design and
operation of the transportation system.  I realize that I am telling
you about conditions that you are very familiar with, and yet we need
to emphasize that TRANSIMS will address these conditions, but we need



you to tell us where we have it wrong or where we need to modify it or
amplify it to meet your needs.  The new requirements are why the
TRANSIMS project has been initiated, to assist you in meeting these

Title 2 of the Act goes further by establishing more stringent
emissions standards for cars and trucks produced between 1996 and
2003.  What this means for TRANSIMS is that in order to analyze the
situation subsequent to 1995, the emissions models must be sensitive
to the engineering and design features of the future vehicles.  Not
only that, but the clean fuel requirements of Title 2 require the use
of alternative fuels so the models must also be sensitive to that in
the newly designed vehicles.  Detail and model sensitivity to these
new variables is critical.

The conformity determination conducted by transportation planners will
have to verify that the transportation control measures are being
implemented and that all the transportation projects have been
evaluated and are pollution neutral.  The conformity determinations
must be based on the latest planning assumptions and forecasts for the
particular region and on the latest EPA emissions model.  It must
include consultation with the air quality community and provide for
timely implementation of TCMs.

To go one layer further into the development of plans in an urban
area, the SIP development process must take place in states and
metropolitan areas.  The major components of the process are the
national ambient air quality standards for six pollutants.  The SIP
process requires an emissions analysis with projections of specific
pollutant emissions by source and development of emissions budgets
over the planning horizon so that for each different mode, cars,
public transportation, other modes, you have to develop budgets of
emissions outputs.  These emission budgets have to be scheduled with
transportation control measures so that at some future time the
implementation of transportation control measures will achieve

Thus, the CAA results in a long list of air quality requirements for
transportation planners.  There are other requirements far too
numerous to list.

We say that the development of TRANSIMS is going to be applications
driven.  The reason we are emphasizing applications is that we feel
that applications are important to drive the model.  They help us to
assure that the specifications of the model are suited to real world
needs.  If TRANSIMS is going to be used, we have to insure that it
addresses the issues of interest to the practitioner community.

Applications also provide for incremental designing and testing.  The
process of developing an application, structuring a model to address a
particular policy issue, will help us design and test a model.  This
offers an opportunity to deliver intermediate products; you are not
going to have to wait five years to get some benefit from TRANSIMS. 
We very much want to develop intermediate products which will aid
policy analysis for which TRANSIMS will be used to address particular
issues in two Metropolitan Planning Organizations that we will select
in the future.  They also offer an opportunity to integrate research
results.  The research, the model development and applications are



proceeding virtually simultaneously, and we can integrate research
results into the application process as those research results come

Finally, and perhaps most importantly, it guarantees interaction with
the user community.  It forces us to interface with the user community
on a day-to-day basis and to so structure the model.  To develop the
applications, we have to select and design the application.  When we
select a site and an application, we will develop a detailed study
design with the associated MPO to structure the model to address that
application.  We have to go through a data acquisition phase, and then
of course, implement the application.  The process will give us an
opportunity to spin off interim products and will provide an
environment for the validation of the model and for doing sensitivity

As I mentioned, we have begun that process and we have the draft
issues and requirements paper.  We have completed two MPO visits,
Dallas/Fort Worth and Boston.  We have scheduled visits to San
Francisco and Portland, Oregon, for September, and our final visits to
Denver and Chicago will be later in the fall.  But in addition to the
MPOs, we are aware that the requirements for TRANSIMS may stimulate
interest from other organizations.  So we are planning visits to the
Florida Department of Transportation and one other, as yet
unspecified, state department of transportation.  We are going to
visit with the EPA and the Environmental Defense Fund.  We need to
establish contact with those organizations to include their views and
needs.  And then too, private consulting firms that provide services
to the user community will be contacted.

Finally, I would like to give you a thumbnail sketch of the results of
our visits so far.  We are entering a new environment.  We need to
learn a lot, and that is the reason for establishing contact early on,
up front with the user community.  Our eyes were really opened by our
visits to Dallas/Fort Worth and Boston.  We gained a great deal of
knowledge about the actual planning environments that TRANSIMS is
going to be used in.  We need to learn a lot more about those
environments.  Not that we are the judge, but it is always nice to be
able to work with good people, and we would be honored to work with
either of those staffs to develop applications.  They are doing well
to address the required issues even though they know that some of
their tools are limited.  We learned that we are probably going to be
facing very different and unique planning environments.  We expect
that we will continue to see that when we visit other MPOs.  The
planning environments in the different cities, the things that are
important will vary from city to city.  We expect this.  TRANSIMS has
to be adaptable enough to address these different environments.

In our visits to Dallas/Fort Worth and Boston, we also found out that
it is extremely important to get early and rapid detection of
incidents so that they can be mitigated, so that negative impacts  on
air quality can be reduced.  We learned some interesting things about
off-street parking regulations which Neil Pedersen was talking about
yesterday.  In Boston the parking regulations may cause an unduly
large number of cold starts when people are moving their cars around
to comply with the off street parking regulations.



In summary, the process of defining requirements to determine an
application selection has begun.  We see requirements and applications
as closely linked and extremely important and we sincerely seek your
guidance and input to this overall process.  We need to have
that input in order to make sure that TRANSIMS is a useful model in
the end.



TRANSIMS Methodology

by Darrell Morgeson, Ph.D., Los Alamos National Laboratory

I want to go briefly through the methodology of TRANSIMS, but before I
begin I would like to make one comment.  What is the surest way that
TRANSIMS can fail?  In my opinion, it is trying to do too much for too
many people.  It is trying to be all things to all people.  Even
though it is broad and treats a lot of issues, we have to narrow that
through a well defined set of goals or else we will tack on thing
after thing and never get anything accomplished.  I know that with
this budget and this time frame there is a feeling that it ought to
solve all things for all people.  I hate to dash your expectations,
but I do not think that we will do that in this time frame.

Having said that, I am going to show you the work in progress.  Some
of this we are in the process of doing and I hope not to get into the
philosophical discussion of how we will do that.

To start we had to treat demand generation.  We put together synthetic
populations and travel itineraries.  The fundamental inputs are
representations of the intermodal transportation network and some
estimation of the load based travel demand.  Load is a general term
that includes passengers and commodities.  This is conceptually a
nested computational loop executed for everything to be moved.  The
program computes trip plans for two million people across the
transportation network.  Once the two million plans have been created,
we estimate how they interact with one another in time and space,
spreading those out along the network until the trip plans reach an
equilibrium.  This spreads the trips in space and time along the
alternative routes of the network.

The trip plan generation is really very simple and is related to
travel behavior modeling and decisions.  The first step is destination
choices followed by mode choice decisions.  We start with network
properties including distances and link distances through a
generalized cost approach.  We transform the objective description of
the transportation network into the subjective view of the traveler
from the population on the travel demand list, including their
individual preferences and choices and their view of the
transportation network.  When they reach a node, they make a decision
to continue driving their car or take another mode.  As we do this for
each individual, determining how to get from here to there, the
program recomputes the costs based on a generalized cost equation
where the cost of traversing any link is a function of the operating
cost of driving a car or taking transit, but including time cost. 
Every time you approach a node, you recompute all the alternative
links from the node, based on that cost equation.

Computationally then, the algorithm is simple but not intuitive.  As I
approach any particular node, I compute the cost of exiting that node
on all the alternative links.  So the intermodal decision is made at
the node.  The probability of choosing a link is inversely
proportional to its length so the shortest link is more attractive. 
Using a Monte Carlo simulation, one link is selected.  At any
particular node, I can go in any different random direction.  The
selection is biased on the basis of the least cost and in a direction



generally in favor of satisfying your goals.  While it is non-
intuitive, the algorithm works very well.  It finds a good path in
about 95 percent of the cases.  When it does not find the optimum
path, it finds candidates that have some slight variation.  It
produces a family of paths that have slightly higher costs.  For a
whole population of drivers from here to there, random variations
might be attributable to stopping at a gas station or some other
random deviations.

The first computational step produces trip plans for everybody.  The
next thing to do is to compare them.  Every individual, every
household has goals it tries to achieve, such as getting to work by a
certain time or not exceeding certain costs.  So the next step is to
project the plan.  How long would it take under predicted traffic
conditions to execute the plan and how much would it cost?  Then
compare these measures to the goals of each traveler or commodity. 
The goals do not guide the route or mode of selection.  Those are
functions of the cost equation, but the goals do determine whether or
not the trip plan is acceptable.  Some examples of unsatisfied goals
are maximum, minimum costs, not later than arrival time; not earlier
than departure time; and others.  You can combine goals, such as
having an adjusted time of arrival.  You compare the goals with the
trip plan.  If it meets all the goals, then the trip plan can be
loaded to the network.  The trip plan may look like it is going to do
everything the traveler wanted it to do, but if all goals are not
satisfied, the process is to look for alternative trip plans.  The
program searches for new time of departures.  This process is called
preference adjustment.

Preference adjustment deals with trips independently, one at a time. 
Briefly, here is one example of what the preference adjustment phase
does.  The traveler might have a desire or a preference to avoid
downtown or high crime areas, but the trip to one of his activities
puts him on an interstate highway, a safe route, a leisurely route,
but it doesn't get him there on time.  What happens is that his
preferences change, such as the attractiveness or unattractiveness of
certain areas.  That is called preference adjustment.  What the
methodology actually allows is to dynamically change preferences and
then look for different trip plan solutions.  If the first solution
meets part of the travelers' goals, in this case going on the
interstate highway, but it does not get them there on time, they can
seek alternative solutions through areas that they normally would not
go through if their time goal had been met.  The goal in the first
step is to get as many goals satisfied on the trip plans as possible
and try to reduce this number to zero.  But that is not possible in
all cases.  There may not be a solution that will satisfy all goals. 
An example of that is inner-city people who want jobs in the suburbs
but cannot get to those jobs because they have no car or transit
available.  Therefore, their trip cannot be planned and the demand is

The planner represents a best guess.  All kinds of complexities occur
that are not accounted for in the planner unless they happen
consistently from day-to-day.  Persistently.  What we seek to do in
the microsimulation is to execute the plan.  The program actually puts
together representations of cars and drivers and their driving
behavior; each driver with his own profile.  That is what a car driver
in the microsimulation does.  It tries to execute the trip plan as 



best it can.  If it gets very far from the goals, then we replan the
trip.  At one second intervals, we go through and update all of this
and make decisions on where to pass, break, stop at traffic signals. 
The turning kinds of behaviors are not imposed on the cars and
drivers; they are a function of executing the route plan.  So when I
come to a node, I turn left or right based on what my plan has
indicated.  It is at a regional scale.  We track acceleration engine
temperatures because we have to drive to provide input for the
environmental models.  We use object oriented programming to represent
this information.

The idea of synthetic populations is something we developed to drive
the intermodal planner.  We started with a survey done for the MPO in
Albuquerque that included 2,100 households.  We synthetically produced
a population of roughly 400,000 people.  The process produces the
households and the activities that each household engages in.  We
picked out a set of demographic variables and asked the question that,
"if I am producing a new synthetic house to expand my population, what
would be the probability that this particular characteristic exists in
the new house?"  Some proportion of the actual houses would have that
characteristic, and that proportion was used to produce a probability
density function for that characteristic.  We then drew from that
density function to determine the probability of households with that
characteristic.  So we created new houses and new people that looked
very like the old houses of the old people but had this nice sort of
random variation.

Travel itineraries of all households were collected in the travel
survey.  Activities that the household engaged in during the day were
also identified.  We expanded and produced new synthetic activities
which again looked very realistic but were not exactly the same as the
original population.  In determining the destination choice for each
one of these, we did something simpler yet.  It was similar to an
inverse gravity method that you use in the four-step planning process
to say where these would occur, and we associated goals for every one
of them.

The data sources that we are developing fit in two phases:  generation
of the household itself with all of its activities and then the
destinations and the goal choices of each one of those.  This is a
somewhat sensitive area for us right now but we are working with some
commercial or private sources to look at what new kinds of information
might be available.  I will tell you only that these are promising and
sensitive.  And putting all of these together, we think they yield
interesting composite pictures.

You can make some rough comparisons between the four-step planning
process and what we do in activity demand or generation of loads, and
households, and destinations.  If I look at the steps of generation
and distribution in the synthetic household population, associated
with each household is certain information about where it is, income
levels, and we produced the destinations and trip goals.  The
generation and the distribution steps are embedded in the synthetic
populations in a consistently disaggregated manner.  So it is roughly
comparable to the four-step planning process.

For example, consider several simple trips:  first, home, work, home. 
Another one is slightly more complicated:  home, drop the kid off at



school, go to work, pick the kid up at school and go back home.  A
third is even more complicated, come home at noon, eat lunch, go
shopping, take the wife shopping, come back home, go back to work,
come back home.  These are examples of itineraries.  We constructed a
vocabulary of letters to form words that describe the trips.  These
words form a vocabulary which if complete includes all trips in the
city.  The Monte Carlo simulation is then used to randomly produce new
synthetic itineraries based on letter combinations to create words. 
All of those look very much like the original itineraries, but have
random properties so that not all households are the same.  Then we
just associated the time you came back home with a trip chain, and
these particular itineraries would represent trip chains.  By tracking
the time of day and engine temperatures, you get the effect of cold
starts and other emission generation.

One of the things that we are trying to accomplish today is to view
this as integrating framework with other relevant research and
methodology and to try to identify those things in this meeting and in
our interaction with the research community.

Within the planning area, you are making estimates based on your best
guess of what traffic conditions will be.  The simulations are
producing better estimates of real traffic conditions, so there is
that feedback.

All of this is an exercise in uncertainty.  As we could not see ten
years ago what we have today, we cannot clearly see ten years from
now.  But we are computing at the fundamental cause and effect level. 
It does not make sense to go much beyond that because you get down
into molecules and such.  It is theoretically possible but not very
practical.  Flexibility comes not from what TRANSIMS might be at its
first level of production but what we might learn from it to produce
more simplified versions and better focus on what is really needed and
important.  The other thing that drives us to flexibility is to
produce something that responds to the requirements and needed



Use of TRANSIMS for Air Quality Analysis

by Michael Williams, Ph.D., Los Alamos National Laboratory

The goals for the TRANSIMS modeling system look something like this. 
We want to be able to translate traveler behavior into special and
temporal air pollution concentration.  The reason we are really
interested in this project is that we do not think we do that very
well right now.  We skip the travel behavior, and we only deal with
aggregate systems.  What we will put into this system are itineraries,
vehicle mix, driver personalities that will go into the traffic
simulation model that you have heard about.  From that we will get
distributions of speed and acceleration, catalytic converter
operations and engine temperatures.  That goes into the emissions
model, and from that we get spatial and temporal distributions of
various pollutants.

From that you can go to a dispersion model.  This is a fairly
sophisticated Monte Carlo type model.  It will produce concentration
fields of things that do not react very much in the atmosphere such as
carbon monoxide.  You can also go to the air chemistry model and get
out things that do react in the atmosphere:  ozone, oxides and
nitrogen and hydrocarbons, and also aerosols.  Now from the other
side, we are bringing in large-scale weather data and that goes into
an atmospheric model.  This is the kind of model you use to predict
the weather.  It is driven more by local conditions.  It knows the
fact that you have an area, an urban area as opposed to farmland.  It
is very sensitive to terrain.  It produces turbulence fields and wind
temperature fields.  We anticipate that traffic engineers, urban
planners, environmental scientists, air quality regulators and health
scientists might use this to estimate such things as the impact on air
quality when instituting mass transit systems or building new
highways.  These are the things we do not get in the current systems
of modeling, including different vehicle mixes, traffic jams,
stoplights, tollbooths, that sort of thing.

I am attempting to approach the air quality analysis needs as a user. 
I would like to be able to get good emissions data, and I would like
to be able to really understand what is going on in our cities in
terms of air quality.  We have basically three kinds of tools to deal
with that.  We have a set of measurements, we have a set of emissions
estimates, and we have the models.  And yet, the current status of our
understanding is not very good.  We need to work all these three
pieces together in order to get a better understanding.  The modeling
can tell us something about the representativeness of the
measurements.  For instance, in Mexico City we found that the balloons
one uses to measure weather conditions are providing the data that
drives the meteorological models throughout the world.  However, they
traverse the turbulent layers so quickly that they do not give us a
very representative value of wind direction.  So that is an idea of
how modeling can help us understand what our measurements are really
telling us.  On the other hand, of course, measurements help us to
understand whether the models are working properly.

What are we going to do differently in TRANSIMS?  We are putting in
traffic flow parameters and emissions that are calculated as a
function of space and time.  We account for fluctuation in traffic,



abnormal congestion and intersections.  Typically you only get these
things in a crude fashion, or you have to specify them specifically
for each case if you use the traditional modeling techniques.  We are
driving the system with a prognostic meteorological models which
allows us to look at variable speeds and wind directions.  Its
continuous stability is particularly good for complex terrain.  And
one of the things that we are finding is that there is a link between
the slopes and the emissions that result as cars go up a grade; and,
of course, those same slopes will drive the meteorology, so it is
important to tie these things together.  Typically, traditional
techniques do not treat these things well.  The kind of emission model
we are talking about is still in the developmental stage.  It is very
important that we get these speeds and accelerations, engine loads and
things like that, and from that we can put out carbon monoxide,
hydrocarbons, NOX and aerosol emissions.  Why is acceleration so
important?  It turns out that one second under high acceleration puts
out as much ozone as 2,500 seconds under normal operations based on
actual measurements.

For the emissions module development, we are looking at the current
status based on the federal test procedure driving cycle.  EPA MOBILE
is based on that sort of thing.  What we are going to do is extend
work of the California Department of Transportation into the regimes
of heavier accelerations.  The heavy emitters are another thing that
we picked up from remote sensing, and we will be able to incorporate
that.  We also will deal better with cold starts because of data
coming more directly with our simulations.  We are also working with
Georgia Tech, and there is a recent University of Michigan study using
data from auto manufacturers.

The kind of components that we are talking about for this system
include the mesoscale meteorological model which we have and we are
continuing to develop; the emissions model we do not have; a set of
algorithms that represent the California work and some of the high
accelerations (we need to extend that to more vehicle types although I
believe we have the right general kinds of behavior; the random
particle dispersion model which allows us to treat that in a
sophisticated fashion; and, the air chemistry model which is, of
course, the right way to treat ozone and things of that nature.

What do we think of the strengths of this system are?  Integrated
traffic, emissions meteorology, and air pollution models.  The
prognostic meteorological models that are predictive three dimension
time dependent meteorological models are a flexible tool for analyzing
"what if".  You choose to build a freeway somewhere and you know that
the will area will become more urban, and that will actually change
the local meteorology.  We can reflect that in this kind of system. 
We can address problems ranging from tens of meters to hundreds of
kilometers in scale.  We can account for special and temporal flux in
traffic flow and emissions.  Right now, it looks like in certain cases
such as with CO, they are dominated by abnormal situations.  They are
dominated by emissions from cold starts, from high accelerations and
heavy emitters.  They are dominated by the typical kinds of emissions.



In summary, that is what we are contemplating and I have great hopes. 
I believe that if we do not use something like this, you have to be a
little pessimistic about our ability to understand air quality in
urban areas.





Interim Remarks

by Darrell Morgeson, Ph.D., Los Alamos National Laboratory

I know the morning is drawing long here so we will try to be brief
about the remainder of the presentations.  I would like to review the
study we did in Albuquerque and show you some of the results, the data
outputs, some of the phenomena that we observed.  We did a very simple
thing in Albuquerque.  We planned the entire city for 24 hours.  We
took those trips that just utilized the I-25 and I-40 exchange. 
Albuquerque is very regular, north, south, east, west like that.  All
of the trips that appear in a certain block of time on those highway
segments were simulated.  Then we introduced a mass transit system
such as a train system or bus system.  These paralleled the interstate
so that we made it available to cars and drivers that were the
baseline using the interstates.  We made use of the transit system
part of their preferences and goals.  And some of the result
calibrated to what we thought would happen, with our intuition, and
some did not.  We supposed that the total number of freeway trips
during rush hour would go down.  They did.  What we did not expect
going into the exercise was that the total number of vehicle trips
overall during this time period would increase.  They did.  They
increased because of the availability of cars from the workers who
were using the buses were then left at home.  And also, because there
were latent demands to go shopping and do other things, that were not
being accessed before, the total number of trips went up.  The overall
duration of the trips went down, because the trips were local trips,
shopping and school trips.  And because the total number of trips
increased, the total trip length went down.  And from that, you might
conclude that you were dealing with colder engines, which we were. 
And so, if you work through the results, the overall pollution went up
because you had a facility like mass transit put into place.  The
environmental transportation planners did not like to see that and so
we did not want to announce it.  But, that is what the model is
showing.  So in some sense if you think about it, you can calibrate
your intuition that way.





TRANSIMS Microsimulation System Architecture 

by Steen Rasmussen, Ph.D., Los Alamos National Laboratory

I will briefly review the issues that are associated with the
architecture of this type of high-speed simulation.  Given the time we
have available, we cannot get into too many details, of course.  I
will only talk on two issues.  The first is highspeed simulation in
large complex systems and some of the experimental design of the
simulation of these dynamical systems.  Once you allow a system to
become dynamical, a lot of things happen.  And most of these things
are emergent.  That is, they are not encoded in the system.  For
instance, when you think about the concept of a vehicle, you do not
have any definition of congestion.  You also do not have any
definition of congestion when you look at a roadway.  But once you put
lots of cars on a roadway and you allow them to interact, you get
congestion.  That is an example of an emergent phenomena.  Travel
times are emergent phenomena.  Pollution and air quality are emergent
phenomena.  Incidents are emergent phenomena.  We want to generate
these dynamical phenomena and also to detect and figure out which ones
are important.

In large systems such as those we are describing, that is not a
trivial task.  So, one of the issues to consider first is to make a
very fast regional microsimulation of traffic.  If we want to be able
to simulate say ten to twelve million travelers interacting in Los
Angeles, we need to be able to produce in those simulations in the
order of one hundred million vehicle seconds per second, which is
quite a lot.  And to do that, we have to use a lot of tricks.  We have
to use large computers, but that will not do the trick alone.  We also
have to use simplified driving models for some of our simulations.  We
have to simplify the way we produce the vehicle dynamics.  It is
essential for this simulation to run really fast in order to control
the fidelity of the individual objects in the simulation.  I will
explain more about that shortly.

We have been working quite a lot with large systems, but we are not
quite there yet.  I believe, however, that it is indeed possible to
get there soon, within the next year or so.  We have produced some
phase transitions and spontaneous structures in this vehicle traffic. 
We have been looking at congestion pricing and some of the self-
organizing phenomena that occur on the simple networks when that is
introduced.  We are also working different algorithms to address the
whole new box of problems that you get into when you work with high
performance computers.  We have to determine how we put these systems
on multicomputational machines which means we also have to have self-
organizing algorithms to take care of how the control of computations
actually occurs.

Let me just give an example of what we have done here (shows slide). 
This example has to do with the so-called cell audiometer simulation
of traffic.  Unfortunately, I can't get into the details of the
algorithm, but basically the output of the system as shown here is a
space timed diagram.  This is a simple example.  We have a single lane
and a single lane goes from here to here or from here to here.  What
we see are a lot of dots and each dot is a vehicle.  And this
represents an evolution in time.  The time goes downward.  When you
look at these here, these are indications for vehicles at different



times and different places on this little link.  All of these pictures
here on the left-hand side are taken from a little below maximum
capacity.  And these pictures here are taken a little above maximum
capacity.  What you see here is that even below capacity, we have the
occurrence of congestion which is indicated by these backward
traversing waves.  One of the things that we did not know is that you
have a merging of these traffic jams.  But, you actually have critical
dynamics at the transition.  That, of course, is interesting if you
are a theorist, but what does that mean?  Does it mean anything at
all?  It does.  Look at the fundamental diagram; you are all familiar
with it, this is the density of the roadway traffic.  Actually, this
is from simulation.  We do not have quite as much variation as we see
in real traffic measurements.  Once we put in the truck
characteristics, we get some more variance.  We get also these two
typical different slopes below capacity.  We will be able to improve
this as we work with it.  But, maximum capacity is about .08, and this
is where most cost can go through, the flow is on the y axis here.

Now consider criticality.  What does that mean?  It means that the
variation of the travel time is a function of density.  Once the
traffic volume hits capacity or is in the vicinity of capacity, then
the uncertainty of how long it takes to traverse that link explodes. 
The travel time uncertainty well below capacity is very low because
you are more or less driving like you own the road.  You do not need
to interact with other vehicles.  But once you reach capacity, you
have the occurrence of an infinite number of congestion points.  What
we can show mathematically, is that below capacity we can have traffic
jams.  We can have congestion points, but the probability of very
large traffic jams is very, very low.  But at capacity, an infinitely
large number of traffic jams emerge.  That is the point at which we go
from about four percent uncertainty of the travel time given desired
speed on a single link to almost 70 percent uncertainty.  It is
actually quite amazing to think about this because what we would want
to do when we are building our infrastructure is to utilize it as best
we can.  So we would like to have everything operating at capacity. 
In particular, all these informational systems (e.g., IVHS) that we
are thinking about putting into use are intended to take traffic from
crowded roads and put it onto less crowded roads.  That means that we
are producing a self-organizing critical system.  And that means that,
first of all, since any incidents at this point in principal, greatly
propagate traffic congestion, our systems in a mathematical sense, are
not controllable.  So to estimate or predict the function of these
systems at capacity is, at least in mathematical sense, not possible.

Those are some issues that we have to think about when we are talking
about these informational systems.  In this area there are two
contradictory directions.  One has to do with controllability and the
other one has to do with the flow.  There are some ways out of it. 
The obvious one is that we have to make sure that these informational
systems push the density down below the capacity so that we do not run
into the critical regime.  Another thing which we should note is, that
when we are in a situation where we have lots and lots of acceleration
and braking and where we go from high speed to low speed, that is
exactly where we produce the most pollution.  So we get very foul air
if we operate at capacity.  And thirdly, a philosophical point is that


we probably have the most severe accidents at maximum capacity since
it is just common sense that when you go from high speed to low speed
to high speed to low speed you have very varied kinds of driving.

These are some issues I am emphasizing because they cannot be captured
by using the equilibrium methods.  We are talking about calibrations
between the microsimulation and the planner.  One of the things that
is useful when we calibrate is that we know we are right when we have
maximum divergence in the planner (i.e., congestion and traffic are
widely spread).  I want to emphasize that because there has been a lot
of discussion about equilibrium and it has some real problems.

One other thing I want to say before I close is that with these simple
models, we cannot say anything about accidents because the models are
designed in a way that we cannot have accidents.  They are consistent
so that you do not get collisions.  If you want to look more into
these situations, we have to use intelligent objects that were
mentioned earlier.  We are working on an integrated simulator that can
both have these very simple and more complicated representations of
the traveler.  You can switch between them depending on which
questions you are asking and also the conditions in your system.





Closing Remarks

by Edward Weiner, U.S. Department of Transportation

We feds have compared notes during this conference, and I would like
to give you some reaction to what we've heard.  First of all, the
conference has been far better than we had any right to expect.  We
have just been stunned at the amount of information we've received.  I
have said to a couple of people, what else should we expect when we
hold a conference only every 15 years?  The information kind of builds

The feds are clearly struggling with their role here, and if you have
some views on this subject, feel free to make them known to us.  We
are working from the inside to try and get more of a cooperative role,
a proactive role for the feds, who have not been very active in this
role for the last 15 years or so.  It doesn't hurt us to hear from the
people on the firing line that this is a problem.  We can report that
this is a problem, but if we get letters and hear and talk to people
who say that it's a problem, that helps as well.

We will go back and try and deal with as many recommendations as we
can, but the list is overwhelming.  In our group, the lists were very
long.  But even though you may have heard the same recommendations
from other workshops, all were worth hearing.  Even though the top ten
may need to be addressed first, that doesn't mean that the rest of
them don't need attention.  We'll do our best, but the level of
expectation is so high that we hope not to disappoint you by our

There will be proceedings from this conference.  We've  talked about
having another conference next year and for them to be ongoing.  We
will do our best to get as much information out as fast as we can.  I
think part of the concern about this program being a closed system is
that there aren't enough people to get information out fast enough. 
This conference was one attempt to try to do it en masse, but we know
we have to do more.

Thank you for your participation.  Every person here has contributed
an amazing amount.  It's been a very high quality professional
operations, and we are thrilled about the results.





List of Attendees

Name                          Affiliation
Bernard Alpern                URS Consultants, Inc.
Cathy Arthur                  Maricopa Association of Governments
Gene Bandy                    Baltimore Metropolitan Council
Patricia Bass                 Texas Transportation Institute
Moshe Ben-Akiva               Massachusetts Institute of Technology
Julian Benjamin               North Carolina Agricultural & Technical
                              State University
Jim Benson                    Texas Transportation Institute
Kathryn Berkbigler            Los Alamos National Laboratory
Chandra Bhat                  University of Massachusetts, Amherst
Lawrence Blain                Puget Sound Regional Council
Jon Bloom                     Minnesota Department of Transportation
Jerry Bobo                    Houston-Galveston Area Council
John Bowman                   Massachusetts Institute of Technology
Mark Bradley                  Hague Consulting Groups
Dan Brand                     Charles River Associates, Inc.
Jeffrey Bruggeman             Peat Marwick Main & Company,
Jim Bunch                     COMSIS Corporation
George Cardwell               Maryland National Planning Commission
Ken Cervenka                  North Central Texas Council of
David Clawson                 AASHTO
Patrick Costinett             KJS, Associates
Charles Crevo                 Vanasse Hangen Brustlin, Inc.
Gary Davies                   Garmen Associates
John Davis                    Los Alamos National Laboratory
Stephen Decker                Cambridge Systematics, Inc.
Robert Dial                   U.S. Department of Transportation/Volpe
Rick Donnelly                 Parsons, Brinckerhoff, Quade & Douglas
Bruce Douglas                 Parsons, Brinckerhoff, Quade & Douglas
Fred Ducca                    Federal Highway Administration
Jerry Faris                   Transportation Support Group
LiYang Feng                   Denver Regional Council of Governments
Erik Ferguson                 Georgia Institute of Technology
Kim Fisher                    Texas Transportation Institute
Chris Fleet                   Federal Highway Administration
Michael Florian               INRO Consultants, Inc.
Tom Golob                     University of California, Irvine
Konstadinos Goulias           Penn State University
Zachary Graham                Texas Department of Transportation
Ed Granzow                    The Urban Analysis Group, Inc.
John Hamburg                  JRH Associates
Susan Handy                   University of Texas at Austin



List of Attendees (continued)

Name                          Affiliation
David Hartgen                 University of North Carolina, Charlotte
James Harvey                  Regional Planning Commission (New
Greig Harvey                  Deakin, Harvey, Skabardonis, Inc.
David Hensher                 University of Sydney
James Hogan                   Metropolitan Washington Council of
George Hoyt                   George Hoyt & Associates, Inc.
David Hyder                   North Carolina Department of
Michael Jacobs                U.S. Department of Transportation/Volpe
Martyn James                  East West Gateway Coordination Council
Bruce Janson                  University of Colorado - Denver
Ron Jensen-Fisher             Federal Transit Administration
Jon Kessler                   Environmental Protection Agency
Ryuichi Kitamura              University of California - Davis
Deborah Kubicek               Los Alamos National Laboratory
David Kurth                   Barton-Aschman Associates, Inc.
Terry Lathrop                 City of Charlotte, North Carolina
Keith Lawton                  METRO Planning Department (Portland)
Vernon Loose                  Los Alamos National Laboratory
Clarisse Lula                 Resource Decision Consultants, Inc.
Hani Mahmassani               University of Texas at Austin
Thomas Marchwinski            New Jersey Transit
Richard Marshment             University of Oklahoma
Marilee Martin                Houston-Galveston Area Council
Bill Martin                   Barton-Aschman Associates, Inc.
Robert McCullough             Florida Department of Transportation
Eric Miller                   University of Toronto
Richard Miller                Kansas Department of Transportation
Darrell Morgeson              Los Alamos National Laboratory
Michael Morris                North Central Texas Council of
Elaine Murakami               Federal Highway Administration
Richard Nellett               Michigan Department of Transportation
Tom Newnam                    North Carolina Department of
Felix Nwoko                   City of Durham Department of
Bill Olsen                    URS Consultants, Inc.
Norbert Oppenheim             City College of New York
Eric Pas                      Duke University
David Pearson                 Texas Transportation Institute
Jay Pease                     Southeast Michigan Council of
Neil Pedersen                 Maryland Department of Transportation.
Eugenia Pogany                North East Ohio Areawide Coordinating
John Poorman                  Capital District Transportation
                              Commission, Albany
Dick Pratt                    Richard H. Pratt, Consultant, Inc.



List of Attendees (continued)

Name                          Affiliation
Chuck Purvis                  Metropolitan Transportation Commission,
                              San Francisco
Karl Quackenbush              Central Transportation Planning Staff
Steen Rasmussen               Los Alamos National Laboratory
Abdul Razak                   Memphis Metropolitan Planning
Michael Replogle              Environmental Defense Fund
Martin Richards               MVA Group
Doug Roberts                  Los Alamos National Laboratory
Thomas Rossi                  Cambridge Systematics, Inc.
Matt de Rouville              Baltimore Metropolitan Council
Raymond Ruggieri              New York Metropolitan Transportation
Earl Ruiter                   Cambridge Systematics, Inc.
Larry Saben                   COMSIS Corporation
Charles Schaub                Kentucky Transportation Cabinet
Patti Schropp                 Atlanta Regional Commission
Gordon Schultz                Parsons, Brinckerhoff, Quade & Douglas
Larry Seiders                 COMSIS Corporation
Arnold Sherwood               Southern California Association of
Jun Shi                       Korve Engineering
Gordon Shunk                  Texas Transportation Institute
Bob Sicko                     Puget Sound Regional Council
LaRon Smith                   Los Alamos National Laboratory
Austin Smyth                  The Urban Analysis Group, Inc.
Bing Song                     Mid-Ohio Regional Planning Commission
Frank Southworth              Oak Ridge National Laboratory
Frank Spielberg               SG Associates, Inc.
Peter Stopher                 Louisiana State University
Charlie Sullivan              Texas Department of Transportation
Kevin Tierney                 Cambridge Systematics, Inc.
Paul Tilley                   Texas Department of Transportation
Mary Lynn Tischer             Virginia Department of Transportation
Linda Trocki                  Los Alamos National Laboratory
Bill Upton                    Oregon Department of Transportation
Martin Wachs                  University of California
Edward Weiner                 U.S. Department of Transportation
Michael Williams              Los Alamos National Laboratory
Tom Williams                  Texas Transportation Institute
Bill Woodford                 Peat Marwick Main & Company
Ansen Wu                      Ohio Department of Transportation
Sweson Yang                   City of Indianapolis
Robert Zarnetske              Bureau of Transportation Statistics



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