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The Effects of Land Use

Preface

This report presents the analytical results of a larger project undertaken for 
the Federal Highway Administration by Cambridge Systematics, Inc. 
investigating the "Effects of Land Use and Demand Management on Traffic 
Congestion and Transportation Efficiency."  Prior tasks examined the 
literature that was available on this subject and explored the utility of 
either using or augmenting existing databases.  In support of this previous 
work, JHK and Associates, Inc., assessed the potential of using the employment 
site dataset developed as part of NCHRP Project 3-38(2), "Travel 
Characteristics of Large-Scale Suburban Activity Centers."  It was determined, 
though, that the site characteristics contained in this NCHRP dataset were not 
sufficient to support investigation of the interactive effects of land use and 
travel demand management policies on an employee's commuting behavior.

The work reported herein represents an ambitious program of data collection 
and analysis with respect to employment sites located in the Los Angeles 
metropolitan area.  Activities were carefully designed so that the most 
interesting land use and urban design variables could be tested to determine 
their influence on travel behavior.  Data analysis was carried out by 
Cambridge Systematics, including preparation of this report.  Primary 
contributors included Arlee Reno, Susan Moses, John Suhrbier, Eric Paquette, 
Anne Martin, Krista Rhoades, and Yoram Shiftan.  An initial phase of the 
project was performed by Sam Seskin.

The data collection design portion of the work was performed by Elizabeth 
Deakin of the University of California at Berkeley and the firm of Deakin, 
Harvey, Skabardonis, with advice from other team members and FHWA.  The actual 
data collection was conducted by graduate students from UCLA, under the 
supervision of Ms. Deakin.  Site information, a comprehensive set of 
Regulation XV data, advice on site selection, and review of the technical 
results was provided by Professor Genevieve Giuliano of the University of 
Southern California's School of Urban and Regional Planning, serving as a 
consultant to Cambridge Systematics, Inc.  Data was gathered for the 
employment site itself, for its immediate surroundings, and for the general 
environs.  All project work was carried out under the overall direction of 
Fred Ducca of the Federal Highway Administration.

Copies of the compiled "Los Angeles Land Use/TDM" dataset, Accession No. PB95-
500427 can be obtained either from the U.S. Department of Commerce, National 
Technical Information Service, Springfield, VA  22161; or by contacting:

Cambridge Systematics, Inc.
150 CambridgePark Drive
Suite 4000
Cambridge, MA  02140
Telephone:	(617) 354-0167
Fax:	(617) 354-1542
Table of Contents

Preface		vii

1.0	Introduction		1-1
	1.1	Overview		1-1
	1.2	Background		1-2

2.0	Approach		2-1
	2.1	Overview		2-1
	2.2	Selection of Variables to be Analyzed		2-1
	2.3	The Southern California Regulation XV Database		2-4
	2.4	Survey Instrument Design		2-6
	2.5	Sample of Employment Sites Surveyed		2-9
	2.6	On-Site Data Collection		2-11
	2.7	Data Quality Assessment		2-11
	2.8	Data Analysis 		2-12

3.0	Findings		3-1
	3.1	Change in Mode Share After TDM Implementation		3-1
	3.2	Impacts of Transportation Demand Management Strategies		3-3
	3.3	Impacts of Land Use and Urban Design Characteristics
		on Work Trip Mode Choice		3-5
	3.4	Summary of Impacts of Land Use and Urban Design
		Characteristics		3-21
	3.5	Land Use and Urban Design Characteristics of Sites with a
		High Walk or Bicycle Mode Share		3-23
	3.6	Sites with Low Single-Occupant Vehicle Use		3-25

4.0	Conclusions		4-1

Appendix A
	Site Survey Data Collection Form

Appendix B
	Procedures and Instructions Provided to Data Collection Staff
List of Tables2.1	Data Elements # General Environs of the Site		2-7

2.2	Data Elements # Site Area (One-quarter mile radius)		2-8

2.3	Data Elements # Work Place Characteristics		2-10

2.4	Distribution of AVR, Location, and Industry Characteristics		2-13

2.5	Distribution of Employer Size and Trip Reduction Incentives		2-14

2.6	Distribution of Transportation Mode Shares at Implementation
	and One Year Later		2-15

2.7	Composite Land Use/Urban Design Variables		2-22

3.1	Change in Work Trip Mode Share		3-2

3.2	Categories of TDM Strategies		3-2

3.3	Impact of Financial Incentives on Mode Share		3-4

3.4	Impact of TDM Strategies on Drive Alone Mode Share 1989-1991		3-4

3.5	Impact of TDM Strategies on Ridesharing 1989-1991		3-6

3.6	Land Use and Urban Design Feature:  Land Use Mix		3-8

3.7	Land Use and Urban Design Feature:  Availability of Convenience
	Services		3-12

3.8	Land Use and Urban Design Feature:  Availability of Convenience
	Services		3-14

3.9	Land Use and Urban Design Feature:  Accessibility		3-16

3.10	Land Use and Urban Design Feature:  Perception of Safety		3-18

3.11	Land Use and Urban Design Feature:  Aesthetics of Area		3-20

3.12	Comparison of Drive Alone Shares Among Sites with Financial
	Incentives and Alternative Land Use Characteristics		3-22

3.13	Characteristics of Sites with a High Walk or
	Bicycle Mode Share		3-24

3.14	Sites Having a Low Percentage of Single-Occupant Vehicle
	Commuters		3-25
List of Figures
2.1	Overall Approach to Data Collection and Analysis		2-2

2.2	Factors Influencing Work Trip Mode Choice		2-3

2.3	Analysis of Data		2-17

2.4	Principal Components Analysis		2-20

1.0  Introduction

1.1  Overview
There is considerable current interest in the effects of urban design and land 
use characteristics on the transportation choices made by commuters.  The 
underlying assumption is that these employment site characteristics have an 
important influence on a person's willingness to commute by transit, 
ridesharing, bicycling, or walking - modes other than driving alone.  Further, 
the selection of transportation demand management (TDM) strategies that an 
employer may choose to implement should be a function of surrounding site 
characteristics, and that the combination of site characteristics and TDM 
strategies can have a positive interactive effect in influencing an employee's 
choice of commute travel mode.  While the effectiveness of travel demand 
management strategies, implemented both individually and in combination, has 
been investigated, relatively little empirical work has been done to evaluate 
the interactive effects of land use and TDM strategies on commuting behavior.

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

Information was collected regarding the land use and urban design 
characteristics of a work site, the set of transportation incentives provided 
to the employees by the employer at that site, and the mode of travel by 
employees both before and after implementation of the transportation 
incentives for the trip between home and work.  Data were collected and 
analyzed for individual employment sites.  Data were not available regarding 
the characteristics or travel behavior of individual employees at a given work 
site, only the aggregate distribution of modal shares.  Information was not 
available in the dataset about the residential end of the work trip.  
Similarly, data on midday travel, trip chaining, or other related topics were 
not available.

The second section of this report describes the methodological approach 
utilized, including the specific data collection and analysis procedures.  
Findings of the statistical analyses are presented in the third section.  The 
effects of various travel demand management strategies were examined both 
individually and in combination with land use characteristics.  General 
conclusions are presented in the final section.  The overall finding is that 
an interaction effect does indeed exist.  The effectiveness of programs of 
travel demand management measures is increased at those locations where 
supportive land use and urban design characteristics also exist.

The results presented here represent an initial or preliminary analysis of an 
extensive dataset; considerable additional analysis is possible and is 
encouraged.  The integrated employment site, land use/transportation database 
represents a valuable product by itself.  Previously existing datasets do not 
include descriptions of both land use and travel demand management programs 
for individual employment sites.

1.2  Background
An examination of trends in travel behavior shows increases in the number of 
workers per household, licensed drivers per household, vehicles per household, 
vehicle trips, and average trip length.  Both employment and housing are 
growing at faster rates in suburban than at central city locations, with the 
majority of employment growth now occurring at suburban employment centers.  
Auto occupancies are declining and the percentage of single-occupant commuting 
is increasing.  Overall, vehicle miles of travel, in most areas, are growing 
at a higher rate than either employment or population.  The result of these 
trends is an increasing level of traffic congestion in urban and suburban 
areas across the country, particularly during peak commute periods.  
Congestion often exists in newly developing suburbs and semi-rural areas, as 
well as in central business districts and older residential neighborhoods.

A response to these increasing levels of congestion has been a desire to 
increase the effectiveness with which existing transportation resources are 
utilized.  While this response has included both the expansion of highway 
capacity and increasingly sophisticated systems of traffic engineering and 
control, a variety of transportation demand management measures also have been 
implemented throughout the country.  TDM programs typically include a variety 
of employer provided incentives aimed at inducing commuters to rideshare, use 
public transportation, walk or bicycle to work.  TDM incentives include 
ridesharing and transit subsidies, preferential parking for rideshare 
vehicles, rideshare matching services, facilities for bicyclists (e.g., bike 
storage, showers and lockers), award programs, and a variety of other 
miscellaneous strategies.

Over the past two years, experience with transportation demand management 
(TDM) measures has grown rapidly.  For example, it has been found that transit 
works best in areas with moderate to high densities at both the work and the 
home end of the trip; ridesharing is most effective for long trips destined 
for large centers where "matches" can be easily found.

The importance of land use policies and patterns as determinants of travel 
conditions and choices has increasingly been hypothesized in the last few 
years, and efforts to avoid and manage traffic problems through more forceful 
land use planning have received attention.  It is believed that the 
effectiveness of a particular transportation demand measure depends, in large 
part, on its application in a supportive environment.  For example, commute 
alternative programs are most successful in settings having a mix of uses to 
which midday trips easily can be made on foot.  Where supportive conditions 
are absent, or when conditions tend to cancel out (as when ridesharing is 
"encouraged" but convenient free parking is guaranteed), efforts at demand 
management generally are far less successful.

Project-level planning and zoning controls are one way of affecting 
transportation through the land development process; measures include 
exactions and impact fees to help pay for transportation improvements, and 
conditional approvals requiring on-site traffic mitigation programs.

With respect to urban design, a better matching of transportation to the 
specific land uses it serves often is advocated.  Design objectives aim for 
environments in which buildings are clustered and uses are mixed; a balance of 
housing, jobs, and services is available; and the streets, sidewalks, transit 
stops, and bike facilities are designed to be transit-oriented and pedestrian-
friendly.  At the regional or large-area level, policies such as urban limit 
lines, infill incentives, level of service requirements, and control of 
activities at the rural fringe are considered as means of channeling growth to 
areas best able to accommodate it, pacing growth to reflect infrastructure 
availability, and providing incentives to manage travel demand, simultaneously 
protecting valuable farmland and open space.

An important issue that has arisen is how best to design transportation 
strategies so that these transportation actions are fully reflective of an 
area's land use characteristics, and therefore have maximum impact in 
controlling congestion.  Analogously, the urban design characteristics of an 
employment site can be adjusted so that they are more fully supportive of the 
particular transportation demand management strategies to be utilized at that 
particular location than otherwise might be the case.  It is argued that by 
matching carefully conceived packages of transportation demand management 
programs with land use plans and actions, greater transportation benefits 
should accrue, such as reduced congestion and improved air quality.  Since 
relatively little work has been done that explicitly sorts out the respective 
contributions of transportation measures and land use controls, or that 
identifies and quantifies the synergistic effects of joint land use and 
transportation actions, this project was undertaken to develop this specific 
kind of quantitative information.

The underlying hypothesis of this project, therefore, is that land use and 
urban design characteristics of work sites affect employee work trip mode 
choices.  Furthermore, these land use and urban design characteristics may 
interact with various employer-based transportation demand management (TDM) 
strategies to alter commuter work trip mode choice.  That is, similar TDM's 
may cause different changes in mode choice as a result of the mix of land use 
and urban design characteristics present at different work sites.  In order to 
test these hypotheses, it is necessary to define and quantify the specific 
aspects of land use and urban design which may actually influence mode choice.

It is further hypothesized that the general quality, ambiance, or environment 
of a work site, rather than any individual characteristic, helps determine 
mode choice.  It is not necessarily an individual land use or urban design 
characteristic that influences mode choice, rather it is their combination 
which create a work site environment.  For instance, the presence of a vacant 
lot may not by itself cause a person to choose a different work trip mode of 
travel.  However, that vacant lot may influence a commuter's perception of the 
overall safety of the work site, with mode choice being affected more by 
overall safety considerations than by any one particular land use 
characteristic.  In this example, the commuter's perceived safety is an 
influential site characteristic in determining mode choice, while the presence 
of a vacant lot (in combination with other individual land use and urban 
design characteristics) influences the perception of safety.  Acknowledgement 
of this relationship is essential to defining the factors which contribute to 
mode choice.
2.0  Approach

2.1  Overview
The underlying objective of the analytical portion of this project was to 
statistically analyze employee commuter behavior at individual employment 
sites to determine any differences that may exist that are a function of both 
the particular transportation demand management incentives and the particular 
land use and urban design characteristics that exist at that site.  As 
indicated in Figure 2.1, the first step involved the definition of the 
particular variables to be tested.

Assembly of a dataset containing information on the desired variables then 
constituted the second, and difficult, step of the analysis process.  It was 
determined that no existing dataset contained the desired information on both 
TDM and land use.  Consequently, it eventually was decided to build upon the 
results of earlier analyses of the data developed for the Southern California 
Regulation XV Employee Trip Reduction Ordinance that had been conducted by 
Giuliano, Hwang, and Wachs, (1992), (Begin Footnote:  Giuliano G., Hwang K., 
and Wachs M., "Employee Trip Reduction in Southern California:  First Year 
Results," June 1992  End Footnote) which are primarily oriented to travel 
demand management (TDM) variables.  For a subset of the sites contained in the 
dataset developed by Giuliano et al., detailed land use and urban design data 
were collected.  The data on urban design and land use were then analyzed in 
conjunction with the transportation demand management measures that existed at 
that employment site to determine their respective influence on the choice of 
travel mode for commuting.  The site specific land use/TDM dataset is far more 
detailed than any data on employment sites and commuter travel patterns 
previously developed or evaluated.  The following subsections describe the 
analytical methodologies utilized, addressing each of the steps identified in 
Figure 2.1.

2.2  Selection of Variables to be Analyzed
Work trip mode choice is affected by many factors.  Giuliano, Hwang, and Wachs 
(1992), in their analyses of the Southern California Regulation XV data, 
classified the factors known to influence commuters' choice of modes into 
three broad categories:  employee characteristics, intraorganizational 
characteristics of the work place, and environmental factors (Figure 2.2).  
Employee characteristics were defined as including factors such as workers' 
commute distances, incomes, and levels of automobile ownership.  Similarly, 
organizational characteristics defined include factors such as the need in 
certain industries for all workers to be at work simultaneously, and the 
degree to which certain jobs require traveling (i.e., sales).

Environmental factors, as defined by Giuliano et al., include transportation 
supply characteristics, such as the availability of alternative modes and mode 
attributes (e.g., travel time and cost, parking availability, and congestion); 
factors that have been shown through prior research to have a strong influence 
upon commuters' choice of modes.  The approach in this project was to build 
upon the Giuliano et al classification by expanding upon the definition of 
'environmental factors' to also incorporate a range of land use and urban 
design variables.

Land use encompasses factors relating to the spatial pattern of urban 
development, as well as the distribution of different activities within an 
urban area.  Land use factors hypothesized to influence commuters' choice of 
modes included accessibility to services, particularly the mix and intensity 
of services within walking distance of the work place, and employment density.  
It was hypothesized that increasing the mix and intensity of services within a 
convenient walking distance encourages the use of alternative modes (e.g., 
transit, ridesharing, biking, walking) by increasing the feasibility and 
desirability of making midday trips without using a personal vehicle.  
Although employment density was not hypothesized to affect commuters' choice 
of modes directly, it was included because of its strong relationship with the 
level of transportation services provided, the range and intensity of other 
services provided, and urban form characteristics.

Urban design relates to the physical characteristics of specific sites.  These 
characteristics include architecture, streetscape, and site layout, and can 
influence the way people perceive urban environments and, in turn, the way 
urban environments make people feel.  Urban design features hypothesized to 
affect commuters' choice of travel modes included characteristics that would 
enhance the aesthetic appearance of the work place, and those features 
contributing to feelings of comfort and safety.  It was hypothesized that 
these factors encourage the use of alternative modes for the work trip in two 
ways:  1) by increasing the desirability of using an alternative mode for the 
work trip itself; and 2) by increasing the desirability of fulfilling midday 
trip needs without the use of a personal vehicle.

2.3  The Southern California Regulation XV Database
Extensive data on travel demand management measures and employee travel to 
work sites had been collected in association with the South Coast Air Quality 
Management District's (SCAQMD) Regulation XV Trip Reduction program.  After 
examining the use of other potential datasets and data collection 
opportunities, a decision was made to focus data collection efforts upon 
augmenting the existing Southern California dataset with the desired 
information on land use and urban design characteristics.  (Begin Footnote:  
The SCAQMD Regulation XV dataset included travel demand management variables 
and employee travel behavior, but no land use variables at the employment 
site.  Thus, additional data collection was necessary to add site-specific 
land use variables.

An alternative dataset considered was one that included land use variables and 
employee travel behavior information at a national sample of employment sites 
having over five million square feet of existing floor space.  Developed by 
JHK and Associates, Inc. as part of NCHRP Project 3-38(2), "Travel 
Characteristics of Large-Scale Suburban Activity Centers," this dataset had 
the disadvantage of lacking information on the specific travel demand 
management information that existed at each employment site.

An exploration of the feasibility of adding travel demand management variables 
to the NCHRP dataset indicated that it was very difficult for people to either 
remember or estimate what kind of travel demand management actions or programs 
had been in effect at the time the data on travel behavior was collected.  For 
the Regulation XV database, not only was it judged to be easier to assume that 
overall site variables had not changed since the travel data was collected, 
but the travel data was more recent and included different years of travel 
behavior and travel demand program information for each site.  Since the South 
Coast data promised more opportunities and fewer difficulties, a decision was 
made by FHWA and the project team that available resources would be best 
devoted to enhancement of the South Coast dataset by adding the land use 
variables to the already existing travel demand management and travel behavior 
information.  End Footnote)

Under the SCAQMD's Regulation XV program, employers having 100 or more 
employees at any work site within the Los Angeles Metropolitan area are 
required to develop and implement a trip reduction program aimed at obtaining 
a specified Average Vehicle Ridership (AVR) target.  AVR is defined as the 
ratio of the number of employees arriving between 6:00 a.m. and 10:00 a.m., to 
the number of motor vehicles used by the employees.  Regulation XV does not 
require employers to meet their AVR targets, but employers can be fined for 
not implementing the trip reduction programs.

The Regulation XV trip reduction ordinance was enacted by the South Coast Air 
Quality Management District in 1988 with implementation phased in over a 
period of years.  Employers having 500 or more employees were to submit plans 
during the first year.  Participation by employers of 200 or more was 
scheduled for 1989, and employers of 100 or more were phased in during 1990 
and later years.  In January 1994, the SCAQMD decided in the form of Rule 1501 
to undertake a review of implementation experience with Regulation XV.  The 
intent of the review is to identify options that could both reduce 
implementation costs to individual employers and increase effectiveness in 
terms of the magnitude of emission reductions that are being achieved.

As a part of the Regulation XV program, information was collected for each 
work site.  Data included work site characteristics, time series data 
describing initial and follow-up trip reduction measures implemented, mode 
share, and AVR information.  Data pertaining to individual employees, however, 
was not included.  The resulting information was incorporated into a database 
as part of the investigation completed by Giuliano, Hwang, and Wachs [1992] of 
the results of the first year of the Regulation XV program.  Two basic 
criteria were used for inclusion of a work site in the database:  1) the site 
had to have submitted and received approval for first and second year trip 
reduction plans as of August 1991; and 2) data for the site had to pass 
certain tests for logic and consistency.  The resulting dataset covered 1,110 
work sites, or 27 percent of the 4,032 total sites that had received approval 
for their plans as of August 1991.

Many employers and especially larger employers in Los Angeles County, already 
had implemented a variety of TDM measures prior to enactment of the 
Regulation XV ordinance by the SCAQMD.  Since all TDMs are not, therefore, a 
direct result of Regulation XV, the estimates of effectiveness derived from 
the SCAQMD database will be an underestimate, all else being equal, of actual 
impacts if true before and after data were available.

2.4  Survey Instrument Design
The primary aim in designing the survey instrument was to identify urban 
design and land use variables that would provide a comprehensive picture of 
the work site and its environs.  A secondary aim, though, was to supplement 
some of the data already contained in the Regulation XV dataset, particularly 
information on parking and transit accessibility.  Given the desire to test 
hypotheses about the relationship between urban design variables and work trip 
mode share, a particular emphasis in designing the survey instrument was 
placed upon identifying variables that would describe the 'friendliness' of 
the area to specific modes, particularly walking, bicycling, transit, and car 
and van pools.  Another emphasis was upon defining measures of accessibility 
to services, including both the mix and intensity of services within walking 
distance of the site.

To meet these data collection objectives, a survey instrument was designed to 
gather information at three distinct levels.  The first level of information 
focused upon the general environs of each work site ranging from one-half 
square mile to two square miles from the work site area.  Data collection at 
this level was focused upon understanding the 'friendliness' of the area to 
specific commute alternatives.  Specific data elements defined for collection 
at this level were grouped into two categories:  land use and street 
characteristics, as shown in Table 2.1.

The second level of data collection involved the area within one-quarter mile 
of the work place.  Information gathered at this more detailed level was 
designed to provide an understanding of the feasibility and desirability of 
satisfying midday trip needs by walking.  Data items collected described the 
accessibility of the site to services and the quality of the travel paths 
between the work place and services.  Although some information collected at 
this level covered topics similar to those addressed within the site environs, 
such as land use and street characteristics, the information collected at this 
level was much more detailed.  Examples of specific data elements collected at 
this level are shown in Table 2.2.

The third scale of data collection was the work site itself.  Data gathered at 
this level focused upon understanding the general ambiance of the site and its 
immediate surroundings.  The intent of work-site data collection was to 
understand the environment that employees would encounter both in arriving at 
a particular work site and in choosing to spend their lunch hour within the 
immediate vicinity of the site.  A summary of the data items collected for the 
work site is shown in Table 2.3.

A copy of the final survey instrument used for field data collection is 
provided as Appendix A to this report.  Given the volume and complexity of the 
data to be collected, an extensive instruction sheet was prepared for use by 
field staff along with the survey instrument.  A briefing also was held to 
instruct field staff.  Both the instructions and survey form were refined 
based upon a pretest that was conducted at several sites.  A copy of the the 
instructions provided to the field staff is provided as Appendix B.

2.5  Sample of Employment Sites Surveyed
The full Regulation XV database covers the entire South Coast AQMD regulatory 
area, a 13,000-square mile region encompassing Los Angeles County, Riverside 
County, Orange County and the non-desert portions of San Bernardino County.  
Given the extent of urban design data desired as well as the need for on-site 
data collection, collecting on-site data from firms across such a large area 
would have been prohibitively time consuming.  Consequently, it was necessary 
to focus the data collection efforts on a sample of the 1,110 sites in the 
Regulation XV database.  To determine the desired sampling approach, the 1,110 
sites were examined to determine the range in those factors thought to most 
strongly affect urban form:  location, land use, scale, and employment 
density.

The 1,110 sites were grouped by zip code and mapped by location to determine 
the extent of geographic clustering.  After the sites were mapped, the 
clusters and isolated sites were analyzed to determine representation along 
the other factors, including types of land uses, scale, and employment 
density.

The majority of the 1,110 sites, approximately 68 percent, contained in the 
Regulation XV database are located within Los Angeles County.  Twenty-one 
percent of the sites are located within Orange County, and the remaining sites 
are fairly evenly divided between San Bernardino and Riverside Counties.

Mapping of sites illustrated that there was substantial geographic clustering.  
Major clustering occurred within downtown Los Angeles, Hollywood, and in the 
major employment centers in the corridors stretching from the downtown Los 
Angeles area to western Los Angeles, to Santa Monica, and to the Los Angeles 
International Airport.  Other major clusters were found in Orange County, 
within the vicinity of Santa Ana and the Orange County Airport.

Given both the distribution of sites geographically and the logistical 
implications of on-site data collection, the decision was made to focus the 
data collection efforts on the sites within Los Angeles County.  Based upon 
the mapping of sites and the analysis of their representation of the range in 
land uses, scale and employment densities, a sample of 330 work sites was 
selected from the 761 work sites within Los Angeles County.

2.6  On-Site Data Collection
For the 330 selected employment sites, data collection was completed from 
March through July 1993.  Data were collected by graduate students in urban 
planning, urban design, and related fields from UCLA.  Since some of the data 
to be collected, such as pedestrian activity or traffic levels, were items 
that varied by time of day and day of the week, surveyors were instructed to 
observe this type of information during standard weekday business hours.  This 
ensured that observations made were consistent with the environment that an 
employee would encounter during the standard work day.  Data collection 
included completion of the survey form, diagrams, maps, and photographs of 
each site.

2.7  Data Quality Assessment
Three different means were utilized to evaluate the quality of the collected 
data.  Coding of the data provided the opportunity to examine maps of the 
sites along with the data and surveyor comments provided on the survey form.  
Through the coding process, a small number of data elements were identified 
where there were clearly differences in interpretation among field staff or 
simply a lack of understanding of the information desired.  Missing values for 
certain data elements also provided clues as to information that was not 
understood by the surveyors.

A second opportunity for assessing data quality was provided by independently 
sending two different teams of data collectors to collect the same information 
at five of the selected sites.  With the exception of certain data elements 
that could be expected to vary with time and day of observation, such as level 
of traffic or pedestrian activity, the data collected by one team of data 
collectors under an 'ideal' scenario would have exactly replicated the results 
of the other.  Although the size of the subsample, five, was too small to 
gauge data quality with any statistical significance, it did help identify 
particular data elements that could be of potential concern.

The final test of data quality was a series of logic tests undertaken to check 
specific variables, and the relationships between variables, for 
reasonableness and consistency.  For example, tests were completed to 
determine the number of sites identified both as having several different land 
uses within the vicinity and as 'single use.'  These types of tests, checking 
for multiple conditions that would not reasonably be expected to occur 
simultaneously, also helped to assess the quality of the data that had been 
collected.

Based upon these assessments of data quality, a small number of variables were 
identified as being of potential concern.  Even though some variables were 
thought to be reasonably objective to measure, there were some cases where it 
was clear data collectors did not understand the particularly information 
desired.  Examples of this first type of problem included items such as floor-
to-area ratio (FAR), block and parcel size, number of bus lines, street type, 
and street set-back.  For example, FAR values were missing in over 98 percent 
of the site observations.

Some data elements proved to be problematic simply because they proved to be 
more subjective to measure than originally expected.  Among this group of 
variables were items such as land use grain, and presence of trees.  Although 
the difficulty with some of these data elements, such as the presence of 
trees, was unexpected, it is possible that different data collectors set 
different thresholds for the frequency with which a characteristic needed to 
occur (e.g., number of trees) before they would indicate a characteristic was 
present at the site.

Certain data elements also proved to be more difficult to measure than 
anticipated, even though they are not inherently subjective or abstract 
concepts.  Examples include the presence of certain services within walking 
distance of a site.  Follow-up visits to sites indicated that, in some 
situations, landscaping obscured particular land uses.  To thoroughly observe 
the presence of all services within walking distance in large suburban 
locations having extensive landscaping proved to require an extensive amount 
of time to completely walk within one-quarter mile of each building and site 
and examine the specific services offered.  A quick review of signage and 
streetwall frontage proved insufficient for such site locations.

2.8  Data Analysis
Distribution of Site Characteristics
The first part of the data analysis involved an examination of the employer 
and transportation characteristics of the subsample of 330 sites used in the 
urban design survey.  As tabulated in Tables 2.4, 2.5, and 2.6, the 
characteristics examined include the degree of variation in AVR, site 
attributes, trip reduction incentives, mode share, and change in mode share 
between implementation and one year following implementation.  Table 2.4 
identifies the percentage of sites by AVR, location, industry type, and size 
classifications.  Table 2.5 tabulates the mix of trip reduction incentives 
utilized.  Table 2.6 examines and compares AVR and mode share.

These tables also present a comparison of the 330 and 1,110 site databases, 
noting statistical significance (at the 0.95 level), to provide an indication 
of the difference between the 330-site subsample and the larger 1,110-site 
sample.  However, just as the 1,110 work sites do not necessarily reflect a 
random sample of the employment sites subject to the Regulation XV ordinance, 
the 330 subsample was not intended to be fully representative of the 
characteristics of the full 1,100-site database.  While there are differences 
between the two datasets, as noted below, the overall conclusion is that the 
330 sample is approximately representative of the SCAQMD database as a whole.

As visible from Table 2.4, the 1.3 AVR target was under represented in terms 
of statistical significance and the 1.5 AVR target was statistically over-
represented within the 330-site subsample.  The AVR targets are based upon 
geographic location.  The AQMD assigned low-density areas an AVR target of 
1.3, while developed urban and suburban areas are targeted for 1.5 AVR and the 
Los Angeles CBD has an AVR target of 1.75.  Several geographic areas within 
the Regulation XV Los Angeles metropolitan area differ in their 
representativeness within the two datasets.  As discussed in Section 2.5, 
Sampling, the data collection effort was limited to locations within Los 
Angeles County.  Therefore, San Bernardino, Riverside, and Orange Counties are 
not represented in the 330-site subsample, and sites including north, 
southwest, east, and remote west Los Angeles County and Long Beach are over-
represented compared to the larger 1,110-site sample.  As indicated in 
Table 2.4, manufacturing was the only type of industry with a significant 
variation between the two samples.  Both the size of the work site, based upon 
number of employees, and trip reduction incentives for the 330-site subsample 
were representative of the 1,110-site sample.

The comparison between the 1,110-site and the 330-site subsample presented in 
Table 2.6 identifies statistically significant differences for bus share at 
implementation of plan, and for carpool share and bus share one year later.  
Based upon the calculated means for mode share, the bus share is over-
represented in the 330-site subsample for both implementation years.  This 
difference presumably is due to the restriction of the data collection to Los 
Angeles County.  Areas such as San Bernardino and Riverside Counties, that are 
contained in the larger 1,110-site sample, do not have comparable bus systems 
to those existing in Los Angeles County.

Overview of Analysis Methodology
The land use, urban design, and transportation incentive data collected for 
the 330 employment sites were analyzed using a combination of Principal 
Components analysis and standard analysis of variance statistical techniques.  
As shown in Figure 2.3, the first step in the analysis involved a refinement 
of the hypotheses concerning the effects of land use and urban design 
characteristics and TDM incentives on the commute trip mode choice.  Principal 
Components analysis was then used to identify groups of land use variables 
having similar impacts.  The intent was to derive a small number of composite 
variables that could be used to capture the overall characteristics of a site.

Standard analysis of variance techniques were used next to examine the effects 
of groups of land use and urban design characteristics and trip reduction 
incentives both individually and in combination.  The findings presented in 
Section 3.0 focus on this phase of the data analysis.  Finally, groups of 
employment sites having certain transportation characteristics in common were 
analyzed to determine if these sites also shared any land use or urban design 
characteristics.  It is in this context that the subset of sites having a high 
percentage of people that commute by either walking or bicycling are examined 
as a part of the Section 3.0 findings.

Identification of Composite Land Use and Urban Design Variables
It was hypothesized that the interactions of individual site characteristics 
could be very significant.  For instance, the presence of a sidewalk and the 
level of area traffic each may have some influence in measuring the 
accessibility of services.  A sidewalk may enhance accessibility slightly, 
while increased traffic may inhibit accessibility slightly.  However, an area 
which combines high traffic and no sidewalk may have much lower accessibility 
than would be expected given that each individual influence is slight.  Thus, 
an analysis methodology is required that allows for the many potential 
interactions of a site's  individual land use and urban design variables to be 
combined into a meaningful estimate of site characteristics.

The method of Principal Components was used to create composite variables.  
(Begin Footnote:  Morrison, D.F., Multivariate Statistical Methods, 2nd 
Edition, New York; McGraw-Hill Book Company, 1976.  End Footnote)  (Begin 
Footnote:  An alternative methodology considered was to test the individual 
land use and urban design characteristic variables using standard analysis of 
variance techniques.  One could determine which variables had the greater 
influence on mode share and the change in mode share after the implementation 
of TDM's.  These variables could then be called proxies for the groups of site 
qualities.  For instance, if sites that had vacant lots present nearby 
experienced a lower than average shift from driving alone to transit use, one 
might infer that the presence of vacant lots signifies an unsafe area.  It 
seems reasonable that commuters would be less likely to shift from driving 
alone if their safety is in jeopardy.  Similarly, variables that would be 
proxies of the other site characteristics could be tested.

It was concluded that there were serious difficulties with this technique 
because of the large number of variables involved.  With this many variables, 
it would be extremely difficult to test all potential interactions.  If only 
five variables are tested as proxies for each of five possible site qualities, 
there are more than 30 combinations of the variables and their interactions 
which would have to be tested for each quality.  There are also problems of 
severe multicollinearity (correlation among supposed independent variables) 
when testing high level interactions which make selection of the appropriate 
variables an unwieldy task.  Another problem is the high number of variables 
compared to the relatively small number of observations.

Another methodological option considered was to develop an index of each of 
the site qualities based on individual characteristics.  Creating an index a 
priori to measure the presence of groups of these features at a given site is 
not feasible for two reasons.  First, to do so requires assumptions about the 
relationships between the individual characteristics and the composite feature 
being examined.  Second, there is potentially a high degree of correlation 
among the individual characteristics.  Each poses serious difficulties to 
accurate identification of influential factors.  End Footnote)  Briefly, 
Principal Components analysis can reduce a large number of variables into a 
smaller set of uncorrelated composite variables.  The Principal Components 
method creates composite variables, called principal components, which are 
orthogonal linear combinations of the initial variables.  For p different 
initial variables, a total of p different principal components can be formed.  
The linear combinations are formed sequentially to explain as much of the 
variability in the initial variables as possible.  The first principal 
component explains the most variation in the initial variables; the second 
principal component explains the second most variation; etc.  Because there is 
correlation among the initial variables, this allows the bulk of the variation 
to be explained in relatively few principal components.

Principal Components analysis is perhaps most easily understood geometrically.  
Figure 2.4 shows a cluster of points for n observations of variables x and y.  
The first principal component explains the most variation, so it will be the 
linear combination that spans the line shown by P1.  Each of the n 
observations will receive a principal component score, p1i where i is one of 
the n observations, that is a linear combination of the values of each of the 
n values xi, yi.

The value of the first principal component, P1i, represents a point on the 
line P1.  Similarly, the second principal component (in this case the only 
other possible) explains the second most variation in the cluster, so it will 
be the result of the linear combination that spans the line shown by P2.  The 
value of the second principal component, p2i, will represent a place on the 
line P2.  Finally, the principal components by definition are orthogonal 
(perpendicular), and thus they will be perfectly uncorrelated.  This 
eliminates any potential problems with multicollinearity.

While this interpretation is applicable for any number of variables, it is 
most easily visualized in the two-variable instance.  If there are three 
initial variables, there is a three-dimensional cluster of points which would 
be spanned by three orthogonal principal components.  While Principal 
Components analysis cannot be visualized beyond the three-variable case, the 
same properties hold true.

Because a principal component is a linear combination of the individual 
variables, it can easily be seen how each variable influences the principal 
component by looking at each individual coefficient.  In the individual 
example shown in Figure 2.4, P1 = -.707x + .707y.  This means that as x 
increases, the value of the first principal component decreases.  The 
principal component increases in value when the value of y increases.  In this 
case, the coefficients are equal so each variable has the same impact on P1.  
In most cases, the coefficients would not be equal.  The variable with the 
larger coefficient has the greater impact on the value of the principal 
component.

The particular site characteristics determined to influence commuter mode 
choice are the perception of safety, the accessibility of services for walking 
on midday trips, the availability of convenience services, the mix of 
surrounding land uses, and the aesthetics of the area surrounding the work 
site.  Each of these site characteristics results from a mix of the individual 
land use and urban design characteristics of the area, contained in the data 
base, and also some unmeasured characteristics.  It is possible to estimate 
the general site characteristics by combining individual land use and urban 
design characteristics.  For example, the augmented Regulations XV data 
includes variables to help capture the "friendliness" of the area to various 
commute alternatives (i.e., variables that measure land use and urban design 
characteristics within two miles of the work site), the ease of satisfying 
midday trip needs by walking (i.e., variables that measure land use and urban 
design characteristics within one-quarter mile of each work site), and the 
general ambiance of the site and its immediate surroundings (i.e., variables 
that measure the land use and urban design characteristics of the work site 
itself).  By combining land use and urban design variables in meaningful ways, 
quantifiable variables of the more general site characteristics, such as 
safety, can be established.  These composite variables can then be used to 
quantify the influence of the various site characteristics on commuter mode 
choice.  Furthermore, the interaction of land use and urban design variables 
with TDM incentives can be tested to determine additional impacts of 
implementing TDM's in particular environments.

Table 2.7 lists the individual land use and urban design characteristics from 
which the principal components were developed.  These reduced variable groups 
were compiled based on a combination of statistical analysis and expert 
opinion.  They were the variables deemed most important to each of the 
principal components.  Diagnostic measures were performed on the independent 
component variables to test for the potential problems of heteroscedasticity 
and multicollinearity.  These tests indicated that the data were within 
acceptable parameters, and that remedial measures were not warranted.

The mix of land uses was represented by a principal component of variables 
which indicated the presence of residential, office, retail, and personal 
services within one-quarter mile of the work site.  As the variety of these 
land uses increases, the "mix" of land uses increases.

The presence of convenience services was measured by a principal component of 
variables that indicated the presence of restaurants, banks, child care 
centers, dry cleaning, drug stores, and post offices within one-quarter mile 
of the work site.  As the number of these services increase, the value of the 
component measure increases.

The accessibility of services was measured by a principal component of 
variables indicating the presence of four or more services within walking 
distance, sidewalks, transit service, and the level of traffic around the 
site.  As each of these variables increases, the value of the principal 
component increases.

The perception of safety was measured by a principal component of variables 
which indicate the presence of street lighting, vacant lots, and sidewalks, 
and the level of pedestrian activity in the area.  Street lighting, sidewalks 
and increased pedestrian activity raise the value of the principal component 
and the presence of vacant lots reduces it.

The aesthetic level of each site was measured by a principal component of 
variables indicating the presence of trees and shrubs, wide sidewalks and 
graffiti around the site.  Trees and shrubs and wide sidewalks enhance the 
level of aesthetics, while graffiti diminishes aesthetic appeal.

Determining Effectiveness of Commuting Behavior
With principal components developed, empirical testing of their impacts on 
mode choice was possible.  Standard analysis of variance techniques were used 
to determine if the prevalence of the different principal components 
corresponded to different mode shares.  (Begin Footnote:  Neter, John, 
Wasserman, William and Kutner, Michael H., Applied Linear Statistical Models:  
Regression, Analysis of Variance and Experimental Design, 3rd Edition, Boston:  
Richard D. Irwin, Inc., 1990.  End Footnote)  For example, the drive alone 
share was compared in areas with principal components that indicate high 
safety with those that were not as safe.  If the drive alone shares were found 
to be different by an amount that was statistically significant at a .95 level 
of confidence, then safety was considered a significant influence on the drive 
alone share.  (Begin Footnote:  The determination of statistical significance 
used throughout the analyses is based on a difference of means t-test having a 
95 percent level of confidence.  The difference in the means of two values is 
compared taking into account the standard errors in their respective values, 
where the standard error is an indication of the precision of the mean.  
Imprecise estimates of mean will have relatively large standard errors, 
whereas precise estimates will have relatively small standard errors.  By 
comparing the standard errors with the difference between the means, one can 
determine the likelihood of the difference being the result of random 
imprecision of the estimated means.  Testing with 95 percent confidence 
implies that the likelihood is less than five percent that the difference 
between two values is simply due to random error.

The test is applied by comparing the difference in the two means to the sum of 
their respective standard errors.  If the sum of the standard errors is larger 
than the difference between the means, the difference in the means may be due 
to random errors in estimation.  If the size of the difference is larger, then 
it is unlikely (less than five percent likely in this case) that the 
difference is due to random error, and is instead an indication that with a 95 
percent level of confidence that real differences exist between the two 
factors being compared.  End Footnote)

Similar analysis of variance techniques were employed to measure the impact of 
the TDM incentives.  Mode shares were compared in areas with and without 
different types of transportation incentives.  TDM incentives were categorized 
into one of four groups:  financial incentives; assistance programs; flexible 
work hours; or awards programs.  Significant differences in the mode share 
were attributed to the impact of a TDM.  Measures which did not cause a 
statistically-significant shift in mode share were considered unsuccessful.  
Trip reduction incentives were analyzed both individually and in combination.

Finally, the interaction affects of land use characteristics and TDM 
incentives were evaluated.  An interaction effect captures impacts that may 
not have been found by combining the land use with the TDM impacts in a simple 
additive manner.  Mode shares in areas having both particular land use 
features and TDM incentives were compared to their expected levels given the 
features and incentives taken individually.  Statistically significant 
differences were attributed to the interaction of the land use of urban design 
features with the trip reduction program.
3.0  Findings
This section presents findings related to the impact of land use and urban 
design characteristics when combined with transportation demand management 
(TDM) strategies on work trip mode share.  To understand this combined impact, 
it is important first to identify the impact of transportation demand 
management measures alone, as well as land use and urban design 
characteristics alone.  Section 3.1 identifies, for the entire sample, the 
change in mode share between Year One of the analysis (i.e., pre-Regulation 
XV) and Year Two of the analysis.  Section 3.2 describes the impacts of 
individual TDMs as well as groups of TDMs on mode share across the 330-site 
dataset.

Hypotheses regarding how land use and urban design characteristics interact 
with different categories of TDMs to affect work trip mode choice are 
evaluated in Section 3.3, examining five different categories of land use and 
urban design characteristics.  Mode shares in areas that display particular 
land use and urban design characteristics are compared to sites that do not 
have these characteristics.  In addition, changes in mode share between sites 
that share the same land use characteristics but have adopted different types 
of TDMs are identified.  Shifts in mode share when TDMs are introduced in 
areas that exhibit similar land use and urban design characteristics are 
documented.  These shifts are compared to changes in mode share in areas that 
do not exhibit these characteristics.  A summary of these findings is 
presented in Section 3.4.

The chapter concludes with an examination of sites having two special 
characteristics.  Section 3.5 evaluates conditions at employment sites having 
a higher than average walk or bicycle mode share in order to identify the land 
use and urban design characteristics of these sites that may be unique.  
Section 3.6 then examines sites having a low level of single-occupant vehicle 
commuting.

3.1  Change in Mode Share After TDM Implementation
Table 3.1 displays the average change in work trip mode share between the base 
year 1988, and the first year after implementation of TDMs for the 330-site 
dataset.  Over the period, the drive alone share decreased from 76.2 to 71.4 
percent, an absolute change of 4.8 percent.  This shift was more than 
accounted for by an absolute increase of 5.4 percent for ridesharing, which 
accounted for 13.4 percent of all work trips in the base year, and 18.8 
percent in Year Two.  In relative terms, ridesharing increased by 40.3 
percent, a notable increase which can be attributed to the introduction of 
TDMs.

Both transit and walk/bicycle mode shares decreased (-0.2 percent and -0.4 
percent respectively) over the period.  (These shifts were not significant at 
the 95 percent confidence level.)  It appears that commuters using these modes 
switched to ridesharing.  This shift may be accounted for by the fact that 
there were many more categories of incentives offered exclusively for 
carpooling (10) versus transit (2) and walk/bicycle (4).  The rideshare 
incentives also may have provided bigger financial rewards than those offered 
for transit and walk/bicycle.  (The database does not provide information 
regarding the magnitude of individual financial incentives.)  (Begin Footnote:  
Analysis of variance tests indicate that seasonal changes (i.e., weather) do 
not significantly affect the bike/walk or transit mode shares, or AVR.  End 
Footnote)

The average vehicle ridership (AVR) increased slightly from 1.22 in the base 
year to 1.25 after employer implementation of the their TDM programs.  This is 
a relative increase of 2.5 percent.

3.2  Impacts of Transportation Demand Management Strategies
A total of 31 different transportation demand management strategies were 
implemented by employers within the survey sample.  As shown in Table 3.2, 
these strategies can be grouped into five categories:  financial incentives, 
flexible work schedules, assistance programs, award programs, and other 
strategies.  Financial incentives include all TDM strategies that provides an 
employee with a financial reward for participation.  TDM strategies that 
provide employees with opportunities to alter work schedules to avoid a five 
day per week rush hour commute are classified under the category of flexible 
work schedule.  Strategies included in the category of assistance programs are 
those that provide information regarding alternative modes, and help with ride 
matching.  Award programs include prize drawings and recognition in company 
publications.  The category of "other" includes three strategies that do not 
logically fall under any of the first four categories.  Financial 
disincentives in the form of pricing measures were not implemented at any of 
the employment sites, and thus are not listed in Table 3.2.

As a group, financial incentives were the only strategies that showed a 
statistically significant impact on mode share.  Table 3.3 displays the shift 
in drive alone and in ridesharing when financial incentives were absent 
compared to when they were present.  At sites where financial incentives were 
absent, the average drive alone share decreased by 1.7 percent in the period 
after implementation of TDM strategies compared to the before period.  In 
contrast, the drive alone share decreased by 6.4 percent when financial 
incentives were offered.  The carpool share increased by 3.2 percent in the 
absence of financial incentives, compared to 5.7 percent when financial 
incentives were present.  Financial incentives did not have a statistically 
significant impact on the shift in transit or other modes.

Individual financial incentives that resulted in a statistically significant 
shift from the drive alone share were bicycle subsidies, vanpool seat 
subsidies, vanpool subsidies, transit subsidies and other employee benefits.  
The differences in the drive alone share with and without these subsidies are 
shown in Table 3.4.  Two individual financial incentives, vanpool seat 
subsidies and transit subsidies, are statistically significant in influencing 
a shift to carpooling over the period (Table 3.5).  It is unlikely, though, 
that transit subsidies, per se, are actually increasing the carpool share.  
Instead, the increase in carpool share is likely being caused by carpool 
incentives which are correlated with the offering of a transit subsidy.  That 
is, firms offering transit subsidies are also providing ridesharing subsidies.  
While vanpool subsidies probably are actually increasing the vanpool share, 
there are indications in the data that some employers report vanpooling as 
part of carpooling.

Individual incentives in the categories of assistance programs, award 
programs, flexible work schedules, and other had small impacts on mode share 
that were not statistically significant.  Assistance programs as a group, when 
offered in conjunction with financial incentives, did have a statistically 
significant impact on the change in AVR over the study period, leading to the 
conclusion that assistance programs help to facilitate the effectiveness of 
financial incentives.

3.3  Impacts of Land Use and Urban Design Characteristics on Work Trip Mode 
Choice
As described in the previous chapter, the technique of principal components 
analysis was used to develop composite variables to describe areas with land 
use and urban design characteristics.  Composite variables derived using 
principal components analysis were then used in subsequent analyses to 
identify the impacts of these individual land use characteristics on work trip 
mode choice, as well as the impacts of combinations of land use 
characteristics and TDM strategies on mode choice.  Each of the identified 
land use characteristics was matched with each category of TDM (e.g., mix of 
land uses and financial incentives; preponderance of convenience services and 
assistance programs; etc.)  It was hypothesized that the change in mode share 
away from drive alone would be significantly greater when TDMs and land 
use/urban design characteristics were combined than when TDMs were implemented 
at sites that did not exhibit the land use characteristics.  Analysis of 
variance techniques were used to test the interactive impacts of the composite 
variables describing land use characteristics and TDM strategies on mode 
share.  The results of these analyses are described below.

Areas Characterized by a Mix of Land Uses
It is commonly hypothesized that as the number and mix of land uses in close 
proximity to a work site (i.e., within one-quarter mile) increase, work trip 
mode shares may shift away from the single-occupant vehicle toward alternative 
modes.  This shift may occur because workers are able to make midday trips 
(both business and personal) by foot, bicycle, or public transit.  In 
addition, employees who live within walking distance of their work may be able 
to walk or bicycle to their jobs.  It also is hypothesized that the relative 
effectiveness of TDMs is increased at sites with a diverse mix of uses.  As 
the number and strength of TDMs are increased, the mode share may shift even 
further away from the single-occupant vehicle toward alternative modes.

The analysis conducted partially support these hypotheses.  Statistical 
analysis reveals financial incentives are the only category of TDMs that 
significantly affect mode shifts in areas having a mix of land uses.  (Begin 
Footnote:  Reference the discussion of statistical significance on page 2-23.  
End Footnote)  Table 3.6 shows the difference in share for each mode based on 
changes in land use mix, and whether or not financial incentives were offered.  
(Begin Footnote:  In Tables 3.6 through 3.11 and throughout the analysis, 
flexible work schedules are treated as a "mode of travel" consistent with 
their treatment in the original SCAQMD Regulation XV dataset.  The percentages 
contained in the five cells - drive alone; transit; car and van pool; flexible 
work schedules; and bicycle, walk, other - sum to 100 percent.

For the TDM incentives identified in a particular table, the results reflect a 
comparison of post- and pre-implementation data.  For the land use or urban 
design characteristic identified, the comparison is between sites with and 
without that particular characteristic.  End Footnote)  For each mode share 
and for Average Vehicle Ridership (AVR), the table shows the percentage of 
workers using that mode at sites with:

*	A limited mix of land uses where no financial incentives were offered;
*	Sites with a limited mix of uses where financial incentives were offered;
*	Sites with a broad mix of land uses, but no financial incentives; and
*	Sites with both a broad mix of land uses and financial incentives 
available.

The table also shows the standard error associated with each mode share.

As shown in Table 3.6, when the absence or presence of financial incentives is 
held constant, land use mix does not impact drive alone mode share to a degree 
that is statistically significant.  That is, in areas with no financial 
incentives offered, there is no significant difference in the drive alone mode 
share between sites with a limited mix of land uses and sites with a broad mix 
of land uses.  This is also true for sites where financial incentives are 
offered.

Conversely, when land use mix is held constant, the introduction of financial 
incentives does have a significant impact on drive alone mode share.  At sites 
with a minimal mix of land uses, the drive alone share decreases by 5.5 
percent from 77.2 to 71.7 percent when financial incentives are offered.  
Similarly, when financial incentives are offered at sites characterized by a 
diverse mix of land uses, the drive alone mode share decreases by 4.4 percent 
from 75.2 to 70.8 percent.  While in terms of the percentage change in drive 
alone mode share, the impact of incentives is greatest in areas without a mix 
of land uses, it is important to realize that the drive alone mode share in 
mixed uses areas is smaller to begin with.  Thus, while the percentage change 
is smaller, financial TDMs offered at sites with a mix of land uses will 
result in a lower drive alone mode share than when offered at sites without a 
mix of uses.  (Begin Footnote:  In interpreting the results of Table 3.6 (and 
also Table 3.7-3.11), it is important to note that the impacts of financial 
TDMs and of a particular land use or urban design characteristic are not 
cumulative.  For example, in Table 3.6, the difference in mode share between 
the upper left cell (sites without either financial incentives or a mix of 
land uses) and the upper right cell (sites without financial incentives but 
with a mix of land uses) cannot be simply added to the values of the lower 
left cell (sites with financial incentives but without a mix of land uses) to 
determine the impact of the lower right cell (sites having both a mix of land 
uses and offering financial incentives).  For Tables 3.6-3.11, there are some 
instances when the difference in mode share between sites without either TDMs 
or a particular land use/urban design characteristic and sites with both TDMs 
and that land use/urban design characteristics exceeds the sum of the parts, 
while in other cases this difference is less than the sum of the parts.  
Factors such as the size of financial incentive offered, as well as unmeasured 
urban design and land use variables (e.g., density) may influence these 
differences.  End Footnote)

When the drive alone mode share at sites with neither a mix of land uses nor 
financial incentives is compared to the drive alone mode share at sites with 
both a mix of uses and financial incentives, the difference in drive alone 
share is 6.4 percent (77.2 percent versus 70.8 percent, respectively).  This 
difference indicates that there is indeed an interactive effect between land 
use mix and financial incentives that result in a smaller work trip drive 
alone mode share when both are present.

While land use mix alone does not significantly impact the drive alone share, 
it does result in shifts in the transit mode share.  Among sites that do not 
offer financial incentives, sites with a mix of land uses have a 1.9 percent 
higher transit mode share than do sites without a mix of land uses.  Among 
sites where financial incentives are offered, sites with a mix of land uses 
have a 3.5 percent greater transit mode share for work trips than areas 
without a mix of land uses.

For sites characterized by both a limited mix of land uses and no financial 
incentives, the transit mode share is 2.8 percent lower than at sites with 
both a diverse mix of uses and the presence of financial incentives.  In areas 
characterized by a limited mix of land uses, the introduction of financial and 
TDM incentives appears to shift trips from transit to rideshare and flexible 
work schedule, resulting in a lower transit mode share than if no incentives 
are offered.  One reasonable explanation for this shift is that more of the 
incentives encourage ridesharing than transit use.  In addition, it is likely 
that transit service is more limited and less convenient in areas having a 
limited land use mix.  Thus, when financial incentives are introduced at sites 
with a limited mix of land use, transit riders are induced to shift to carpool 
options.  When both a broad range of land uses and financial incentives are 
present, transit regains a portion of the ridership lost to ridesharing in 
areas with limited land uses.  When land use mix is held constant, financial 
incentives do not significantly impact transit mode share.

As with the drive alone mode share, differences in the land use mix alone do 
not significantly impact shifts in ridesharing.  The addition of financial 
incentives does result in significant shifts toward ridesharing both at sites 
with a limited land use mix (+5.3 percent) and at sites with a mix of land 
uses (+4.7 percent).  The rideshare mode share was greatest (18.7 percent) at 
sites with limited land uses and financial incentives.  The slightly lower 
share (17.7 percent) at sites with both a mix of land uses and financial 
incentives can be accounted for by the higher share of transit mode share (6.4 
percent).  Thus, the interactive effect of both land use mix and incentives on 
ridesharing results in a significant positive change relative to limited use 
sites with no financial incentives, but the impact of the interaction is 
tempered by shifts from ridesharing to transit.

The share of workers using flexible work hours is significantly higher at 
limited land use sites where financial incentives are offered (4.0 percent) 
than at either sites with a similar land use mix but no financial incentives 
(2.4 percent) or sites with both a mix of land uses and financial incentives 
available (1.9 percent).  It is possible that these results occur because in 
areas with a limited land use mix, transportation options are also limited so 
that flexible hours becomes a more attractive options for both employers and 
employees.  As land use mix intensifies, other transportation options (such as 
transit and rideshare opportunities) increase, and workers opt for these 
option at the expense of flexible work hours.

An analysis of the bicycle/pedestrian mode share for different combinations of 
land use mix and financial incentives does not reveal any statistically 
significant differences in mode share.  While the percentage of people walking 
and biking to work is higher where land use is mixed, the differences are not 
significant with a 95-percent level of confidence.  The use of bicycling, 
walking and other travel modes actually decreases with the introduction of 
financial incentives and other TDMs.  This is an indication that the 
particular mix of incentives selected by employers had the unintended impact 
of increasing AVR by reducing the tendency of people to bike and walk.

In areas without financial incentives and a limited land use mix, the average 
vehicle ridership is 1.218.  This increases to 1.271 in areas with both a 
diverse land use mix and financial incentives.  Since this is a statistically 
significant increase, the interaction between land use mix and financial TDMs 
can be considered positive in reducing the dependence on the single-occupant 
vehicle.

Areas Characterized by the Availability of Convenience-Oriented Services
The hypothesis tested was that at sites characterized by the presence of 
convenience-oriented services, workers will commute using alternative modes of 
transportation more frequently than at sites where convenience services are 
absent.  It was reasoned that when at least four types of convenience-oriented 
services (such as restaurants, banks, child care centers, dry cleaners, drug 
stores and post offices) are present, workers will be able to conduct personal 
business and run errands during the work day without the use of an automobile.  
It was further hypothesized that when TDMs are provided in an area having a 
number of convenience-oriented services, the drive alone mode share will 
further decrease in favor of transit, ridesharing, bicycling and walking.

Statistical analysis revealed that two categories of TDMs, financial 
incentives and assistance programs, each significantly affected mode shifts at 
sites characterized by convenience-oriented services.  Table 3.7 shows 
differences in share for each mode based on the availability of convenience 
services and the level of financial incentives offered.  For each mode share 
and for AVR, the table shows the percentage of workers using that mode at 
sites with:

*	Limited availability of convenience services and where no financial 
incentives are offered;
*	Sites with limited convenience services, but where financial incentives are 
offered;
*	Sites having a mix of convenience services, but no financial incentives; 
and
*	Sites with both a mix of convenience services and financial incentives 
available.

The table also shows the standard error associated with each mode share.

When the absence or presence of financial incentives is held constant, there 
is no significant difference in the drive alone mode share between sites 
without convenience-oriented services and sites having a mix of convenience-
oriented services.  However, the introduction of financial incentives does 
significantly shift the work trip mode share away from drive alone both at 
sites with few convenience-oriented services (-4.3 percent) and at sites 
characterized by the presence of convenience-oriented services (-5.6 percent).  
The drive alone mode share is 7.1 percent higher at sites without either 
convenience services or financial incentives than at sites that both offer 
financial incentives and have a preponderance of convenience services.  This 
indicates that the interaction effect of financial incentives and the presence 
of convenience-oriented services results in the greatest overall shift from 
drive alone to other modes.

The shift from drive alone can be accounted for by gains in both transit and 
rideshare.  Interestingly, that portion of the shift that accrues to transit 
results from changes in the land use mix.  Across all sites without financial 
incentives, the transit mode share increases from 3.7 to 6.1 percent (+2.4 
percent) when the land use mix shifts from limited convenience-oriented 
services to a mix of convenience-oriented services.  Across sites where 
financial incentives are available, the difference in transit mode share 
between sites without and with services is 3.7 percent.  Differences in the 
availability of financial incentives do not significantly impact transit mode 
share when the availability of convenience-oriented services is held constant 
across sites.

Conversely, the absence or presence of convenience services by itself does not 
significantly affect ridesharing.  Instead, the availability of financial 
incentives significantly affects ridesharing both at sites characterized by 
limited convenience-oriented services, and at sites characterized by a mix of 
convenience-oriented services.  At sites without convenience services, the 
rideshare mode share increases from 13.4 to 18.6 percent (+5.2 percent) when 
financial incentives are present.  When incentives are made available at sites 
with a selection of convenience-oriented services, the rideshare mode share 
increases by 5.0 percent from 12.5 to 17.5 percent.  The slightly lower share 
for rideshare at sites with convenience-oriented services can be explained by 
the higher percentage of transit share at these sites.

The impact of TDMs on alternative mode shares is influenced by the land use 
characteristics of the sites.  At work sites with limited convenience-oriented 
services, TDMs favor ridesharing.  At sites with both financial incentives and 
convenience-oriented services, both ridesharing and transit benefit from the 
combination.  While the impact on ridesharing is less than at sites without 
convenience services, the combined affect of the shift to transit and 
rideshare results in the greatest decrease in the drive alone share.

At sites without financial incentives, the bike/walk mode share does increase 
significantly when convenience oriented services are present.  However, this 
increase is lost when financial incentives are offered, suggesting that 
financial incentives encourage ridesharing at the expense of biking and 
walking.  The presence of convenience-oriented services and financial 
incentives has no significant impact on the percent of people working a 
flexible hours schedule.

Neither the availability of convenience services nor the availability of 
financial incentives alone significantly alter the AVR.  However, the 
interactive affect of the presence of convenience services and financial 
incentives does result in a significant shift from 1.224 at sites without 
either convenience services or financial incentives, to 1.286 at sites with 
both.

For employment sites that provide a mix of convenience-oriented services, TDMs 
that provide assistance to employees in identifying feasible alternatives to 
driving alone significantly affect shifts in mode share.  Table 3.8 displays 
the impacts of assistance programs on mode share when combined with 
convenience-oriented services.

The table shows that assistance programs result in a small but significant 
shift away from the drive alone mode share (-2.6 percent) when offered at 
sites with limited convenience-oriented services.  At sites with a mix of 
convenience services, the drive alone mode share changes from 75.2 percent 
without assistance programs to 69.9 percent with assistance programs, a 
decrease of -5.3 percent.  A total of 6.8 percent fewer employees drive alone 
at sites having both assistance programs and convenience services than in 
areas with neither assistance programs nor convenience services.  This 
indicates an interactive effect between convenience oriented services and 
assistance programs causing a shift of trips away from the single-occupant 
vehicle.  Both transit and rideshare realize statistically significant gains 
to account for this shift from drive alone.  Changes in bike/walk and flexible 
work hours occur, but with one exception are not statistically significant.  
The mode shifts that occur when assistance programs are offered parallel those 
that occur when financial incentives are offered, with similar changes in AVR.

Sites Characterized by a High Level of Accessibility
It was hypothesized that work sites providing easy access for transit users, 
pedestrians and bicyclists, and with easy access to nearby services may 
realize a smaller drive alone mode share than sites with lower accessibility.  
Moreover, when TDMs are offered at sites having high accessibility, the drive 
alone share will decrease further.  Statistical analysis revealed that the 
only category of TDMs to significantly impact changes in work trip mode share 
when combined with site accessibility are financial incentives.  The impacts 
of financial incentives and accessible sites on work trip mode share are 
displayed in Table 3.9.

The table shows that accessibility alone does not statistically affect drive 
alone share.  That is, when the availability of financial incentives is held 
constant, there is no significant difference in the drive alone mode share 
between sites characterized as lacking accessibility and those characterized 
as providing accessibility.  Conversely, at sites where financial incentives 
are available, there is a significant reduction in the drive alone mode share 
both for sites not characterized as accessible (-4.3 percent), and at sites 
characterized as having a high level of accessibility (-5.5 percent).  When 
sites without either access or financial incentives are compared to sites with 
both access and financial incentives, the drive alone share decrease from 76.4 
to 70.5 percent, a change of -5.9 percent.  This change is greater than that 
realized simply by the addition of financial incentives, and indicates that 
accessibility and financial incentives interact to produce a greater impact on 
the reduction in the drive alone mode share.

Accessibility appears to impact transit share significantly regardless of the 
presence of TDMs.  When financial incentives are not present, the transit 
share is 2.0 percent greater for accessible sites than for inaccessible sites.  
When TDMs are available, this difference increases to 3.3 percent.  This gain 
in transit mode share occurs at the expense of drive alone, rideshare and 
flexible work hour shares.  When land use characteristics are held constant, 
financial incentives alone do not create a significant shift in the transit 
mode share.  However, sites that combine both financial incentives and a high 
level of accessibility show the highest transit mode share (6.3 percent).

As with other land use characteristics analyzed, when the availability of 
financial incentives is held constant, a difference in accessibility by itself 
is not significant in altering the amount of ridesharing.  It is the 
introduction of financial incentives, whether at low or high access sites, 
that impacts ridesharing.  At low access sites, when financial incentives are 
present the rideshare mode share increases from 13.8 to 18.8 percent (+5.0 
percent).  At accessible sites, the addition of financial incentives results 
in a change in ridesharing from 13.0 to 17.7 percent (+4.7 percent).  At high 
access sites, ridesharing is less than at low access areas because transit 
captures a greater share.  Ridesharing and transit combine to create a 
cumulative reduction in driving alone that is greatest for high access sites 
with financial incentives.

Financial incentives result in a reduction in the bike/walk mode share in low 
access areas.  The likely reason for this reduction is that the financial 
incentives are geared toward ridesharing and transit.  Areas with poor access 
are not particularly pedestrian or bicycle friendly, and it appears that the 
financial incentives offered in these areas are sufficient to induce cyclists 
and pedestrians to switch to alternative modes.  In areas that are accessible, 
this shift away from the walk/bike mode is not statistically significant when 
financial incentives and other TDMs are introduced.  The percent of workers on 
flexible work hour schedules decreased at sites having a higher level of 
accessibility, and was not affected in a statistically significant manner by 
financial incentives 

There is a significant increase in AVR between areas having low accessibility 
and no incentives (1.225 AVR) and sites having both high accessibility and 
financial TDMs (1.272).  This change appears to reflect an interactive effect 
between accessibility and financial TDMs.

Areas Characterized as Safe
It was hypothesized that in areas perceived as safe, the drive alone mode 
share would be smaller than in areas where safety concerns may exist.  This 
would occur because employees would be more willing to walk from transit to 
their job sites, and to make midday trips by foot or transit.  For the 
purposes of this analysis, sites were considered to have a higher level of 
safety if they were characterized by sidewalks, street lighting, pedestrian 
activity, and by the absence of vacant lots.

As with many of the other land use categories analyzed, financial incentives 
are the only category of TDMs that result in significant shifts in work trip 
mode shift when combined with the perception of safety.  Table 3.10 shows 
difference in mode share based on changes in the perception of safety and the 
level of financial incentives offered.

When financial incentives are not available, there is a 3.9 percent difference 
in the drive alone share between areas with low safety compared to areas that 
are perceived as safe.  The availability of TDMs helps this shift.  Both at 
sites that lack the perception of safety, and those that are perceived as 
safe, the introduction of financial incentives leads to a decrease in the 
drive alone mode share (-5.8 percent and -4.5 percent, respectively).  The 
interactive affect between the perception of safety and the availability of 
financial incentives result in an even larger shift away from the single-
occupant vehicle for commuting to work (-8.4 percent).  Sites that are not 
perceived safe and that do not offer financial incentives have an above-
average drive alone share of 79.0 percent, while sites perceived as safe at 
which financial incentives are available have a 70.6 percent drive alone mode 
share.

At sites having a low level of safety, the shift away from drive alone when 
financial incentives are offered is accounted for by the 5.6 percent increase 
in ridesharing (from 12.8 to 18.4 percent).  At sites perceived as safe, there 
is a 4.4 percent change in ridesharing when financial incentives are offered.  
None of the other modes show any significant shifts when the perception of 
safety is held constant and financial incentives and other TDMs are 
introduced.

When the level of TDMs is held constant, changes in the perception of safety 
result in a significant change for the transit and walk/bike mode shares.  
When TDMs are available, the transit mode share increases by 1.8 percent from 
3.6 to 5.4 percent.  The bike/walk mode share increases by 1.5 percent between 
sites not characterized as safe and those that are perceived as safe.  This is 
a large shift, given that the bike/walk mode share accounts for less than 4.0 
percent of all trips even at sites perceived as safe.

There is an 8.4 percent decrease in the drive alone mode share between sites 
having a low level of safety and without TDMs, and sites that are perceived as 
safe and that offer TDMs.  This change is accounted for by significant 
increases in mode shares for the transit, rideshare, and walk/bike.  The 
perception of safety is the only land use characteristics that results in 
positive shifts in share for more than two of the model alternatives.   The 
perception of safety and the presence of TDMs seem to interact to achieve the 
larger shift away from the drive alone mode share.

There is also a significant increase in AVR between areas without financial 
TDMs and a low level of perceived safety compared to sites that are perceived 
as safe and that also have financial TDMs available (AVR of 1.206 versus 
1.263).

Areas Characterized by an Aesthetically Pleasing Urban Setting
It was hypothesized that sites located in an aesthetically pleasing 
environment would have a lower than average drive alone mode share.  Using the 
Principal Components analysis technique, a group of variables including street 
noise, lots of signs, an aesthetic appearance, and landscaping combined into a 
single composite variable that characterized a site as aesthetically pleasing.  
Only financial incentives were found to have a statistically significant 
impact on mode share when combined with aesthetic characteristics.  Table 3.11 
shows the difference in share for each mode based on aesthetics and the level 
of financial incentives offered.

The drive alone mode share was statistically affected by both financial 
incentives and by the presence of an aesthetically pleasing work site.  For 
those sites not characterized as aesthetically pleasing, the drive alone mode 
share is 4.7 percent lower at work sites where financial incentives are 
offered than at sites without such incentives (72.3 percent and 77 percent, 
respectively).  Similarly, for sites that are aesthetically pleasing, the 
presence of financial incentives decreases the drive alone mode share from 
72.5 percent to 66.6 percent.

When the presence of financial incentives is held constant, site aesthetics 
has a statistically significant influence on drive alone mode share.  At sites 
without incentives, the drive alone mode share is 77 percent for "less 
aesthetic" urban sites, and 72.4 percent for "more aesthetic" sites.  The 
combined impacts of both financial TDMs and aesthetically pleasing urban sites 
is of particular note.  Sites without either financial incentives or an 
aesthetically pleasing quality have an average drive alone mode share of 77.0 
percent, while sites with both a high level of aesthetics and financial TDMs 
have an average drive alone mode share of only 66.6 percent.  This is the 
lowest drive alone mode share for any of the land use and urban design 
characteristics evaluated in this analysis.

Once again, introduction of TDMs has no significant impact on the transit mode 
share when land use characteristics are held constant.  The transit mode 
share, however, does shift significantly when the presence of TDMs is held 
constant and the level of aesthetics changes.  When no TDMs are available, the 
transit share increases from 3.9 to 7.8 percent.  At sites that offer 
financial TDMs, the average transit share increases from 4.2 to 8.3 percent 
for sites that are aesthetically pleasing.  This is the highest transit mode 
share identified for any of land use or urban design characteristics.

As was true with the other land use and urban design characteristics 
evaluated, ridesharing varies with the level of financial incentives 
available.  At sites that are not aesthetically pleasing, ridesharing 
increases from 13.3 to 17.9 percent when financial incentives are introduced.  
In aesthetically pleasing urban areas, ridesharing changes from 13.9 to 18.9 
percent when financial incentives are introduced.  As with transit, 
ridesharing is higher for aesthetically pleasing sites than for any of the 
other categories of land use and urban design characteristics evaluated.

Unlike the other land use and urban design characteristics analyzed, there 
does not appear to be a tradeoff between transit usage and ridesharing when 
financial incentives are present at aesthetically pleasing sites.  Instead, 
the interactive affect between aesthetics and financial incentives yields 
increases in both transit and ridesharing, thus reducing the drive alone share 
to the low of 66.6 percent.  

Due to the large shift away from the drive alone mode share, sites 
characterized as aesthetically pleasing and where financial incentives are 
offered achieve an AVR of 1.337.  This AVR is higher than that of any other 
combination of financial incentives and land use characteristics.  It also 
appears to be a result of the interaction between the land use characteristics 
and the TDMs, as opposed to either one individually.  One can conclude, 
therefore, that the presence of an aesthetically pleasing setting is important 
in improving the effectiveness of TDMs.

3.4  Summary of Impacts of Land Use and Urban Design Characteristics
To further understand the impact of individual land use and urban design 
characteristics, the differences in the drive alone share are compared between 
sites without each land use characteristic and sites exhibiting that land use 
characteristic (Table 3.12).  In this analysis, financial incentives are 
present at all sites.

The biggest change in the drive alone share (-5.7 percent) occurs between 
sites that lack an aesthetic urban quality and sites having an aesthetic urban 
quality.  Sites with an aesthetic urban quality realize the lowest drive alone 
share (66.6 percent) of sites displaying one of the urban design 
characteristics under study.  The next lowest drive alone share is achieved at 
sites with a preponderance of convenience services (69.6 percent).  The drive 
alone share at sites having a high accessibility to services is 70.5 percent, 
while at sites perceived as safe the drive alone share is 70.6 percent.  At 
sites with a mix of land uses, the drive alone share is 70.8 percent.

Since land use and urban design characteristics cannot always be easily 
changed, it may be difficult to change mode share simply by introducing a new 
land use or urban design characteristic to an existing employment site.  
However, given that this analysis reveals that land use and urban design 
characteristics do impact commute mode share, communities may wish to 
encourage developers of new employment sites to incorporate land use and urban 
design characteristics that support a lower drive alone mode share into their 
site designs.  These characteristics also can be introduced as part of a major 
site rehabilitation or modernization project.

It also is important to note that precise causality cannot be measured due to 
the limitations of the database.  For example, areas with a mix of land uses, 
good accessibility, and lots of pedestrian traffic may also be areas with 
higher than average density and transit service.  However, density and level 
of transit service cannot be adequately measured with the existing data so the 
impacts of these factors on mode share cannot be evaluated separately.  Other 
factors that may influence the results but for which information was not 
available include parking costs and availability at work sites, weather, and 
the magnitude of employer-provided financial incentives.

3.5  Land Use and Urban Design Characteristics of Sites with a High Walk or 
Bicycle Mode Share
The share of work trips made by walking and bicycle as a percentage of the 
total work trips in the data set is small.  This makes identification of work 
site characteristics that encourage utilization of these modes difficult.  To 
understand the characteristics of sites that have a high walk or bike mode 
share, sites with a walk or bike mode share of greater than 10 percent were 
identified and evaluated separately.

Table 3.13 displays the land use characteristics and TDM measures available at 
sites having a combined bicycle and walking mode share that is greater than 10 
percent either before or after implementation of Regulation XV.  Twenty-five 
sites or 7.6 percent of all observations, spread throughout the study area had 
a walk/bike mode share greater than 10 percent prior to implementation of the 
Regulation XV trip reduction measures.  After implementation, though, the 
walk/bike mode share declined to 8.3 percent, 6.2 percent lower than the pre-
Regulation XV figure of 14.5 percent.  As a whole, this group displays a 
greater percentage of sites having land use and urban design characteristics 
that encourage alternative modes of travel for the work trip than is true of 
the entire 330-site data set.  The level of TDMs offered at theses sites, 
however, is below the average for the entire data set.  Furthermore, a smaller 
percentage of these sites offer financial subsidies for walking and bicycling 
than is true for the entire data set.  The walk/bike share at these sites may 
have declined because incentives promoting ridesharing and transit were 
offered to employees, with corresponding fewer incentives supporting walking 
and bicycling being available.

The second column of Table 3.13 summarizes characteristics of sites having a 
walk/bike mode share greater than 10 percent after implementation of the 
Regulation XV measures.  In contrast to the column one sites, the percentage 
of employees walking or biking increased at these locations from an average of 
12.3 to 13.9 percent.  The land use and urban design characteristics of these 
sites more closely parallel those found to encourage alternative modes than 
either the data set as a whole or the sites with a pre-Regulation XV walk/bike 
mode share of 10 percent or more.  The percentage of sites offering financial 
incentives was comparable to that for the entire data set.  Furthermore, the 
percentage of these sites that offered walk and bike subsidies was well above 
the average for the complete data set.  Bicycle racks were also more common at 
these sites.  It appears that both land use and urban design characteristics 
that encourage alternative modes and the provision of TDMs that are 
specifically designed to be supportive of bicycling and walking can be 
effective.

3.6  Sites with Low Single-Occupant Vehicle Use
The analysis identified 21 work sites at which 50 percent or fewer of the 
employees commuted by single-occupant vehicle (SOV).  Within the context of 
the 330 work sites sampled, these sites provide one indicator of a practical 
"upper bound" for AVR and utilization of non-drive alone travel modes for the 
trip between home and work.  As such, they serve as a point of reference 
against which the potential of other sites may be judged.  Table 3.14 shows 
the average of the commute mode shares for these 21 sites.  Ridesharing 
accounts for the majority of non-SOV commuting, having a 36 percent modal 
share.  Transit also carries a large proportion (12.8 percent) of non-SOV 
commuters, a level that is almost three times the transit mode share for the 
sample as a whole.  Average vehicle ridership is 1.68, compared to the base 
year average of 1.22.

These modal shares, however, do not appear to be the result of a particular 
mix of land use design, and TDM strategies.  While employees at these 21 sites 
are the least reliant on single-occupant automobiles, the TDM incentive levels 
and the land use/urban design characteristics at the sites are not 
significantly different from the average for all sites in the sample.  Factors 
other than those analyzed apparently account for the low SOV mode share.

4.0  Conclusions
The findings presented in the previous section lead to the following general 
conclusions that can guide implementation of land use, urban design, and 
transportation demand management strategies in urban settings.

1.	Financial Incentives are Important as Part of a TDM Strategy	
A successful travel demand management strategy should be built around a 
core of financial incentives, regardless of the land use and urban design 
characteristics of a particular site.  As a group, financial incentives are 
the only TDM strategies that consistently result in a statistically 
significant reduction in the drive alone mode share.  At sites where 
financial incentives are not included among the TDMs offered, the drive 
alone mode share decreased by 1.7 percent over the study period compared to 
6.4 percent decrease in the drive alone mode share at sites where financial 
incentives are included among the TDMs offered.  For each land use 
category, financial incentives account for the majority of the reduction in 
the drive alone share.  Individual financial incentives that resulted in a 
statistically significant shift from driving alone were transit, vanpool, 
and bicycle subsidies and other employee benefits.

2.	Specific Land Use and Urban Design Characteristics Influence Mode Choice
Urban design and land use characteristics that can be controlled by public 
officials and private business working in a cooperative partnership can 
influence a person's choice of commuting mode.  The findings demonstrate 
that the availability of TDM strategies and transportation alternatives, 
combined with opportunities to accomplish midday errands without having to 
drive, reduce the use of single-occupant vehicles for commuting.

The data reveal that when financial incentives are present, the greatest 
reduction in the drive alone share is realized in areas with an 
aesthetically pleasing urban character.  The drive alone mode share at 
these sites is at least three percent less than at sites exhibiting any of 
the other land use characteristics analyzed.  This appears to be a result 
of the availability of alternative modes (e.g., transit service), and the 
quality of the environment.  Sites with a preponderance of convenience-
oriented services realize the next greatest reduction in the drive alone 
mode share, followed by sites with good access to services, sites with the 
perception of safety, and sites with a mix of land uses.

3.	A Positive Interactive Effect Exists Between Land Use Characteristics and	
Financial Incentives	
Travel demand management strategies have a larger influence on reducing the 
drive alone mode share than do land use characteristics, when each is 
considered individually.  However, the findings further reveal 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.  When 
both are present, the increase in AVR is always greater (by at least 2.5 
percent) than when TDMs are present in an area without any of the land use 
characteristics, or when TDMs are absent from sites where the land use 
characteristics are present.

The impacts on mode share, however, are not linearly additive as further 
TDMs as well as land use and urban design characteristics are included at a 
site.  The cumulative effect is less than the sum of the parts.

In implementing a regional TDM strategy, efforts should focus on areas that 
exhibit at least one of the land use characteristics studied as there is a 
greater potential for increases in the AVR in these areas.  Consideration 
of this interactive effect when designing a TDM strategy may result in a 
more effective and efficient program.  Adoption of policies that support 
compatible development of work sites with the land use and urban design 
characteristics found to encourage alternative modes is warranted.

4.	Tradeoffs Exist Between Ridesharing, Transit, and Walk/Bike	
Modal decisions are made not only between driving alone and alternative 
modes, but also among available alternative modes.  The TDM programs 
examined are most beneficial in increasing the level of ridesharing.  This 
increase in ridesharing, however, results not only from a decrease in 
driving alone mode, but also comes at the expense of transit, walking, and 
bicycling.  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 TDMs.

From a policy standpoint, it is important to understand these tradeoffs 
when designing a transportation management program.  For example, a TDM 
strategy that increases ridesharing at the expense of transit, walking, or 
bicycling may not be supportive of broader regional transportation policies 
or goals.  By understanding the tradeoffs that may occur given particular 
land use characteristics, a TDM program can be designed to strengthen 
incentives that will encourage the full range of available non-drive alone 
modes.  It may be effective to focus TDM strategies on ridesharing in areas 
that do not exhibit land uses that are supportive of transit, walking, and 
bicycling, TDMs that support transit and walk/bike should be featured in 
areas where the land uses are supportive of these modes.

5.	Employer-Provided Transportation Assistance Programs are Most Helpful	
at Sites Having a Variety of Convenience-Oriented Services	
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 were not found, 
by themselves, to have a significant impact on either the drive alone share 
or AVR at sites with other land use characteristics.  For sites having a 
high level of convenience-oriented services, a TDM strategy featuring 
assistance programs should be successful in helping to achieve increases in 
AVR.  A program that includes assistance programs but not financial 
incentives, though, will have a smaller positive impact than a program that 
includes financial incentives.

6.	Selected Individual Sites Attain High Levels of Non-Drive Alone Commuting      
While the average level of walking and biking over all the sites surveyed 
is 5.4 percent, selected sites have post-implementative mode shares that 
are two and one-half times this level.  These sites are characterized by 
land use and urban design characteristics that encourage alternative modes 
of travel for the work trip.  Furthermore, these sites offer financial 
incentives in the form of walk and bicycle subsidies that are well above 
the average for all the sites analyzed.

Twenty-one of the 330 sites examined have less than half of their employees 
commuting by driving alone, leading to an average vehicle ridership of 1.68 
compared to the overall average of 1.25.  Ridesharing accounts for the 
majority of alternate mode commuting, achieving a share of 36 percent, with 
transit accounting for 12.8 percent of the work trips at these sites.  
These figures provide one indication of the practical upper bound that may 
be achievable in terms of the distribution of commuting mode shares and the 
level of average vehicle ridership.

7.	Transferability of Results	
The impetus for the implementation of TDM strategies at many employment 
sites within the Los Angeles metropolitan area has been the Regulation XV 
trip reduction ordinance enacted by the South Coast Air Quality Management 
District.  Many Los Angeles area employers, though, had implemented a 
diverse range of TDMs for a variety of reasons prior to enactment of the 
Regulation XV ordinance.  An evaluation of TDM strategies at these 
particular locations based only on a Regulation XV "before" condition, 
therefore, may understate their level of effectiveness since the TDM 
measures already would have been in place.

The impetus for the provision of TDM measures or supportive urban design 
characteristics is not relevant to an analysis of their effectiveness.  
Similar results should be obtained independent of the factors motivating 
their implementation.

The data used in this analysis are specific to Los Angeles county, and thus 
reflect the particular socioeconomic and geographic characteristics of that 
particular portion of the Los Angeles metropolitan area.  There are, 
however, numerous urban areas in the U.S. that are similar to this portion 
of Los Angeles in terms of their land use characteristics, densities, 
socioeconomic characteristics, and commute trip travel characteristics.  
The results of this study are directly applicable to the development of TDM 
programs for these areas.

It is recognized, though, that the drive alone mode share is higher and 
that the development density is lower in the Los Angeles metropolitan area 
than in many older areas in the United States.  For these areas, the 
results of this study are considered a conservative estimate of the 
interactive effects of land use and transportation demand management 
strategies on mode choice.  Areas having land use characteristics that are 
more supportive of alternative modes of transportation could have higher 
levels of effectiveness than reported here.  In addition, the results are 
transferable to other urban areas in terms of the relative ranking of 
importance of the land use and TDM factors analyzed.

Table 2.1    Data Elements # General Environs of the SiteLand Use

7	Land use mix
7	Predominant single land use
7	Special features or notable 
sites
7	Building types

Street Characteristics

7	Identification of the main 
streets
7	Traffic levels
7	Presence of sidewalks
7	Landscape quality

Table 2.2   Data Elements # Site Area (One-quarter mile radius)Land Use

	Land Use Mix
	7	Horizontal
	7	Vertical

	Presence of Specific Land 
Use Types
	7	Residential
	7	Office
	7	Retail
	7	Heavy industrial
	7	Light industrial
	7	Auto-related
	7	Institutional
	7	Open space
	7	Parking (off-street)
	7	Personal services
	7	Business services


Services

	Presence, Frequency, and 
Distance to Specific 
Services
	7	Restaurants/coffee shops
	7	Groceries
	7	Banks/ATM machines
	7	Parks/open space
	7	Child care
	7	Dry cleaning/laundry
	7	Drug stores
	7	Entertainment:  
movies,videos, etc.
	7	Haircuts
	7	Health 
club/exercise/dance
	7	Copies
	7	Post office
	7	Travel agent
	7	Parking lot
	7	Parking structure

	Street Characteristics
	7	Street type
	7	Median
	7	On-street parking
	7	Level of traffic
	7	Street layout
	7	Mix of Traffic
	7	Noise level

	Streetwall Characteristics
	7	Building set-back
	7	Quality of streetwall
	7	Adjacent uses 
	7	Signage
		-	Parcel use
		-	Unrelated to use (e.g., 
billboards, graffiti)

	Sidewalk Characteristics
	7	Presence of sidewalk
	7	Pavement type
	7	Level of maintenance
	7	Sidewalk zones
		-	Tree/shrub planting 
strip
		-	Arcades/awnings
		-	Street furniture

	Pedestrian Characteristics
	7	Types of pedestrians
	7	Extent of pedestrian 
activity

	Landscaping Characteristics
	7	Presence of trees
	7	Tree size and spacing 	
	7	Shade effect

Table 2.3   Data Elements # Work Place CharacteristicsParcel and Block 
Characteristics
7	Block form
7	Block density
7	Floor-to-area ratio (FAR)
7	Parcel size
7	Number of parcels in block
7	Block dimensions


Building Characteristics
7	Building size
7	Architectural style
7	Aesthetic appearance
7	Building materials
7	Building set-back
7	Orientation
7	Scale
7	Building maintenance


Transportation Characteristics
7	On/Off-street parking
7	Distance to bus stop
7	Distance to rail transit
7	Width of sidewalk at main 
entrance
7	Typical sidewalk distance in 
area
7	Number of cyclists

Table 2.4   Distribution of AVR, Location, and Industry Characteristics	Percent of Sites
			Statistically
	330 Subsample	1,110 Sample	Significant

AVR Target
1.3	0.3	2.7	Yes
1.5	97.0	93.4	Yes
1.54	0.0	0.1	No
1.63	0.0	0.1	No
1.75	2.7	3.7	No

Sub Areas
(1) Los Angeles County Central	13.2	11.7	No
(2) Los Angeles County West	14.1	6.0	Yes
(3) Los Angeles County South	4.0	4.6	No
(4) Los Angeles County Southwest	16.0	8.1	Yes
(5) Los Angeles County East	12.6	5.4	Yes
(6) Los Angeles County Remote West	7.1	3.4	Yes
(7) Los Angeles County Remote Northeast	7.4	9.3	No
(8) San Fernando Valley	5.8	6.5	No
(9) Burbank,Glendale,Pasadena	6.1	7.9	No
(10) Long Beach	13.8	4.8	Yes
(11) San Bernardino County	0.0	5.9	Yes
(12) Riverside County	0.0	4.7	Yes
(13) Orange County North	0.0	13.0	Yes
(14) Orange County South	0.0	8.1	Yes
(15) Remote Area	0.0	0.5	Yes

Area
L.A. Central (1)	13.2	11.7	No
L.A. County (2,3,4,5,7,9,10)	73.9	46.2	Yes
Suburb (6,8,11,12,13,14,15)	12.9	42.1	Yes

Industry (SIC Code)
Ag/Fo/Fi/Mi	0.6	0.6	No
Construction	0.0	0.2	No
Manufacturing	30.5	36.3	Yes
Tran/Comm/Util	19.7	17.2	No
Whole/Retail	13.8	13.5	No
Fire	7.4	5.4	No
Services	23.7	22.6	No
Public Office	4.3	4.2	No

Business
Manufacturing	30.5	36.3	Yes
Service/Service Related	49.2	45.7	No
Others	20.3	18.0	No
Table 2.5   Distribution of Employer Size and Trip Reduction Incentives
	Percent of Sites
			Statistically
	330 Subsample	1,110 Sample	Significant


Size
250 or less employees	27.4	28.6	No
251 or greater employees	72.6	71.4	No

Incentives
Preferential Parking Area	71.6	67.7	No
Transit Subsidy	53.2	49.0	No
Guaranteed Ride Home	52.0	47.8	No
Prize Drawings	47.7	48.2	No
Bike Racks	41.0	43.0	No
Regional Commuter Management
  Agency Matching	40.7	36.9	No
Information Booths	36.1	31.9	No
Flexible Work Hours	34.3	31.7	No
Commuter Information Center	32.4	27.1	Yes
Other Marketing Elements	28.7	24.7	No
Carpool Subsidy	27.8	29.0	No
New Hire Orientation	27.8	25.8	No
Employer Based Matching Service	25.7	26.3	No
Compressed Work Week Program	25.1	21.4	No
Other Employee Benefit	24.2	23.7	No
Showers and Lockers	22.9	21.9	No
Other On-Site Services	19.9	16.2	No
Cafeteria/ATM/Postal/Fitness Center	19.6	19.2	No
Company Owned/Leased Vanpool	19.0	15.9	No
Walk Subsidy	18.7	18.6	No
Preferential Parking - Carpool	17.8	16.4	No
Bike Subsidy	17.1	17.7	No
Vanpool Subsidy	14.7	13.9	No
Auto Service	13.8	13.8	No
Special Interest Group	11.3	12.8	No
Recognition in Newsletter	10.4	12.9	No
Commuter Fairs	9.5	11.7	No
Telecommuting Program	8.9	8.8	No
Other Financial Subsidy	8.0	8.0	No
Additional Time Off With Pay	6.1	7.1	No
Rideshare Parking Subsidy	4.3	2.5	No
Introductory Transit Pass Subsidy	4.0	5.5	No
Vanpool Seat Subsidy	3.7	3.6	No
Other Facility Improvement	3.4	3.2	No
Passenger Loading Area	2.4	1.7	No
Preferential Parking - Vanpool	1.2	1.8	No
Other Parking Management	1.2	2.1	No
Transportation Allowance	1.2	0.5	No
Employee Parking Subsidy	0.9	0.7	No
Childcare Services	0.9	1.2	No
Table 2.7    Composite Land Use/Urban Design Variables	Independent	Principal
	Variables	Component


Offices within 1/4 mile of site
Residential development within 1/4 mile of site
Retail development within 1/4 mile of site	Mixed Use
Personal services within 1/4 mile of site
Open space (parks) within 1/4 mile of site

Restaurant(s) within 1/4 mile of site
Bank(s) within 1/4 mile of site
Child care within 1/4 mile of site
Dry cleaners within 1/4 mile of site	Convenience Services
Drug store(s) within 1/4 mile of site
Post office within 1/4 mile of site

Presence of numerous services (4 or more)
Frequency with which certain services are present
Presence of sidewalk	Accessibility of Services
Traffic volume
Transit stop

Absence of graffiti
Presence of trees and shrubs in the sidewalk zone	Aesthetics
Wide sidewalks
Minimal building setbacks

Absence of vacant lots
Pedestrian activity	Safety
Sidewalks
Street lighting

Table 3.1    Change in Work Trip Mode Share		Implementation	Absolute	Relative
		Period	Percent	Percent
Mode	Base Year	Year Two***	Change	Change


Drive Alone	76.2	71.4	-4.8	6.3
Rideshare*	13.4	18.8	5.4	40.3
Transit	4.6	4.4	-0.2	-4.3
Other**	5.8	5.4	-0.4	-6.9
Average Vehicle
Ridership (AVR)	1.22	1.25	0.03	+2.5

  *	Carpool and vanpool.
 **	Bicycle, walk, etc.
***	The second year varies for each site within the sample, based on when TDMs were 
implemented at the site.  All second year data were collected between 1990 and 1992.

Table 3.2    Categories of TDM Strategies
Financial Incentives7	Transportation allowance7	Bike subsidy
7	Carpool subsidy
7	Introductory transit pass 
subsidy
7	Other financial subsidy
7	Vanpool seat subsidy
7	Transit subsidy
7	Vanpool subsidy
7	Walk subsidy
7	Rideshare parking subsidy
7	Additional time off with pay7	Other employee benefits


Flexible Work Schedules7	Flexible work hours7	Telecommuting program7	Compressed work week program7	Compressed work week program

Assistance Programs
7	Commuter information center7	Commuter fairs7	New hire orientation
7	Other marketing elements
7	Special interest group
7	Regional commuter management 
agency matching
7	Employer-based matching 
service
7	Information booths7	Company owned/leased vanpool7	Other parking management

Award Programs
7	Prize drawing - free meal 
certificate7	Recognition in news letter


Other7	Child care service7	On-site services (e.g., 
cafeteria, health club, post 
office)7	Auto service
Table 3.3    Impact of Financial Incentives on Mode Share
	Shift in Commute Mode Shares
	1989-1991
	Percent Change	Percent Change
	When Financial	When Financial	Difference
	Incentives are	Incentives are	(Incentives Present #
Mode	Absent	Present	Incentives Absent)


Drive Alone	-1.7	-6.4	-4.7

Carpool/Vanpool	3.2	5.7	2.5



Table 3.4    Impact of TDM Strategies on Drive Alone Mode Share
1989-1991*	Percent Change	Percent Change	Difference
	in Drive Alone	in Drive Alone	(TDMs Percent #
Strategy	When Absent	When Present	TDMs Absent)

Bicycle Subsidy	-4.4	-7.1	-2.7

Vanpool Seat
Subsidy	-4.7	-10.1	-5.4

Transit Subsidy	-3.2	-6.3	-3.1

Vanpool Subsidy	-4.4	-7.7	-3.3

Other Employee
Benefits	-3.9	-8.0	-4.1


*	Includes strategies that, when present, result in a statistically significant shift from the 
drive alone mode share at the 95 percent confidence level.
Table 3.5    Impact of TDM Strategies on Ridesharing1989-1991*	Percent Change	Percent Change	Difference
	in Carpool/Vanpool	in Carpool/Vanpool	(TDMs Present #
Strategy	When Absent	When Present	TDMs Absent)


Vanpool Seat
Subsidy	4.7	10.3	5.6

Transit Subsidy	4.0	5.6	1.6


*	Includes TDM strategies, that when present, result in a statistically significant shift from the 
drive alone mode share at the 95 percent confidence level.
Table 2.6    Distribution of Transportation Mode Shares at Implementation and One
Year Later
	At Implementation	One Year After Implementation
	Mean	Mean
			Statistically			Statistically
	330 Subsample	1,110 Sample	Significant	330 Subsample	1,110 Sample	Significant

Average Vehicle Ridership	1.213	1.208	No	1.245	1.243	No
Drive Alone Share	0.762	0.766	No	0.714	0.714	No
Carpool Share	0.125	0.134	No	0.174	0.188	Yes
Vanpool Share	0.009	0.007	No	0.013	0.011	No
Bus Share	0.046	0.038	Yes	0.043	0.036	Yes
Other Mode Share	0.033	0.031	No	0.028	0.028	No
Telecommuting Share	0.005	0.004	No	0.003	0.003	No
Compressed Work Week Share	0.020	0.018	No	0.024	0.020	No

Table 3.12    Comparison of Drive Alone Shares among Sites with Financial Incentives
and Alternative Land Use Characteristics
	Percent Drive Alone
	Sites with Land	Sites with Land	Absolute
	Use Characteristics	Use Characteristics	Percent
Land Use Characteristics	Missing	Present	Change


Mix of Land Uses	71.7	70.8	-0.9
Accessibility to Services	72.1	70.5	-1.6
Preponderance of Convenient Services	72.4	69.6	-2.8
Perception of Safety	73.2	70.6	-2.6
Aesthetic Urban Setting	72.3	66.6	-5.7

Table 3.13    Characteristics of Sites with a High Walk or Bicycle Mode Share
	Sites with Pre-Regulation	Sites with Post-Regulation
	XV Walk/Bike Share	XV Walk/Bike Share	All
	>10%	>10%	Sites


Numbers of Observations	25.0	14.0	330.0
Pre-Reg XV Walk/Bike Share	14.5%	12.3%	3.3%
Post-Reg XV Walk/Bike Share	8.3%	13.9%	2.8%
Difference # Pre-Reg to Post-Reg XV	-6.2%	+1.6%	-.5%

Site Characteristics
	Aesthetic Urban Environment	35.0%	24.0%	16.0%
	Perceived Safe	88.0%	92.0%	72.0%
	Access to Services	68.0%	79.0%	53.0%
	Mix of Land Uses	68.0%	71.0%	52.0%
	Numerous Convenience-Oriented Services	56.0%	64.0%	35.0%
	Financial Incentives	56.0%	64.3%	66.1%
	Assistance Programs	72.0%	71.4%	82.1%
	Flexible Work Schedules	44.0%	35.7%	47.6%
	Award Programs	36.0%	42.9%	50.3%
	Bicycle Subsidies	12.5%	23.1%	17.1%
	Walk Subsidies	16.7%	38.5%	18.7%
	Bike Racks	45.8%	61.5%	41.0%


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