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Development and Application of Trip Generation Rates - Final Report January 1985
Click HERE for graphic. Notice: This document is disseminated under the sponsorship of the Department of Transportation in the interest of information exchange. The United States Government assumes no liability for the contents or use thereof. Click HERE for graphic. ACKNOWLEDGMENTS Kellerco is most appreciative for the assistance rendered by the U.S. Department of Transportation, the Institute of Transportation Engineers, and the staffs of several agencies throughout the country. U.S. DOT staff who provided assistance included Ms. Louise Skinner, Mr. Leroy Chimini, Mr. Carl Shea, Mr. Christopher Fleet and Mr. George Schoener. Mr. Mark Norman of ITE provided valuable assistance. Dr. Everett Carter and Mr. Abdulla Meer acted as consultants to Kellerco. Mr. Sanjeev Malhotra provided research assistance on the study. Mr. Joe Mehra was the Principal Investigator and Mr. C. Richard Keller was the Principal- In-Charge. TABLE OF CONTENTS Page No. I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . .1 OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . .1 II. ANALYSES OF TRIP RATE DATA . . . . . . . . . . . . . . . . . . .5 DATA SOURCES . . . . . . . . . . . . . . . . . . . . . . . . . .5 UPDATING TRIP RATES. . . . . . . . . . . . . . . . . . . . . . .5 III. TRIP GENERATION RATES. . . . . . . . . . . . . . . . . . . . . .9 USE OF TRIP RATES. . . . . . . . . . . . . . . . . . . . . . . .9 IV. OTHER FACTORS INFLUENCING TRIP RATES .. .... . . . . . . . . . 30 MULTI-USE DEVELOPMENT . .... . . . . . . . . . . . . . . . . . 30 SITE DEVELOPMENT CAPTURE RATES FOR PASS-BY TRAFFIC . . . . . . 36 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 APPENDICES A List of Agencies. . . . . . . . . . . . . . . . . . . . . A-1 B Trip Rate Analysis Methodology. . . . . . . . . . . . . . .B-1 C Recommendations for Further Research. . . . . . . . . . . .C-1 iii LIST OF FIGURES Figure No. Page No. 1 NORTHERN VIRGINIA PUD . . . . . . . . . . . . . . . . . . . 31 2 RICHMOND, VIRGINIA PUD. . . . . . . . . . . . . . . . . . . 33 3 ILLUSTRATIVE RELATIONSHIP BETWEEN PERCENTAGES OF INTERNAL TRIPS AND RATIO OF COMMERCIAL SPACE TO NUMBER OF RESIDENTIAL UNITS . . . . . . . . . . . . . . . . . . . . . 35 iv LIST OF TABLES Table No Page No. 1 DATA SOURCES. . . . . . . . . . . . . . . . . . . . . . . . .6 2 DAILY TRIP RATES AS A FUNCTION OF TRANSIT AVAILABILITY. . . . . . . . . . . . . . . . . . . . .8 3 TRIP GENERATION RATES . . . . . . . . . . . . . . . . . .10-23 3a ADJUSTMENT FACTORS FOR RESIDENTIAL CHARACTERISTICS . . . . . . . . . . . . . . . . . . . . . . 27 4 TYPICAL LAND USE DENSITIES. . . . . . . . . . . . . . . . . 28 5 APPLICATION OF ADJUSTMENT FACTORS . . . . . . . . . . . . . 29 6 EXTERNAL TRIPS GENERATED BY USES IN A PUD . . . . . . . . . 34 7 SITE DEVELOPMENT CAPTURE RATE FROM PASS-BY TRAFFIC. . . . . . . . . . . . . . . . . . . . 37 APPENDICES B-1 TEST OF SIGNIFICANT DIFFERENCES BETWEEN PRE-1973 AND POST-1973 DATA . . . . . . . . . . . . . .B-4 B-2 TEST OF SIGNIFICANT DIFFERENCES BY LOCATION BETWEEN PRE-1973 AND POST-1973 DATA . . . . . . . . . .B-5 B-3 MULTIPLE REGRESSION ANALYSIS STATISTICS FOR RESIDENTIAL USES. . . . . . . . . . . . . . . . . . .. B-7 B-4 TRIP RATES AS A FUNCTION OF MARKET VALUES OF SINGLE FAMILY DETACHED RESIDENTIAL DWELLING . . . . . .B-8 v DEVELOPMENT AND APPLICATION OF TRIP GENERATION RATES I. INTRODUCTION Local agencies are continually facing the need to address the physical condition and service capabilities of the streets and highways in their jurisdictions. Recently this concern has turned to the rapidly developing suburbs of metropolitan areas and the access needs of new development. Related to this are the issues of zoning variances and joint public/private funding for highway improvements to support these developments. Regional planning agencies are being called on more frequently to provide technical assistance and service to sub-regional areas and local jurisdictions. Local cities and counties face the need for accurate technical procedures to analyze 'the potential impacts of new development. This publication and its companion document, the 'Site Impact Traffic Evaluation (S.I.T.E.) Handbook". provide guidance on site access analysis procedures. This report presents updated 'trip generation rates along with factors for adjusting trip rates due to variations in residential characteristics The use of trip rates is also described. The S.I.T.E. Handbook presents a seven phased site access study process including a trip generation rate development procedure (50). Four case studies are presented that demonstrate the use of trip generation rates and also analyze the sensitivity of site-related traffic to trip rates, trip distribution patterns and other key variables. Additional and related publications include: - The ITE trip rates publication: 'Trip Generation - An Informational Report", Third Edition, 1982 (45) - "Using the ITE Trip Generation Report" prepared by Carl Buttke for ITE, July 1984 (5) - NCHRP Report 187 "Quick Response Urban Travel Estimation Techniques and Transferable Parameters: User's Guide" 1978. (83) These publications should be collectively used for guidance and not relied upon' as the sole source of information for trip rate information in site access analyses. Where local data and procedures are available, they should be used if the analyst considers them to be more accurate. OVERVIEW There are many methods for collecting trip generation rates, ranging from driveway (ground) vehicular counts to regional home interview surveys. Driveway vehicular counts of traffic 1 entering and leaving development sites have been collected for many land uses. Manual counts or automatic traffic recorders are used to collect traffic data on driveways during peak hours of adjacent street traffic and/or the generator and sometimes over a twenty-four hour period. The traffic data for the cordoned site along with the background information on each site (such as dwelling units, gross floor area, number of employees and acres of land) are utilized to estimate vehicle trip rates per dwelling unit (or other independent variable). Most of these ground count based rates are compiled in such documents as the Institute of Transportation Engineers (ITE) "Trip Generation - An Informational Report" (45) and numerous locally developed documents. These rates, when applied to future land uses, result in an estimate of future daily and peak hour trips. Regional home interview surveys are not covered in this report. They provide information at the individual household level and are generally used to model trip generation relationships with various socioeconomic factors and land use characteristics. These trip generation relationships are generally used for long range comprehensive planning. Several concerns have been raised regarding existing trip generation rate data: - Variability among trip generation rate sources and geographic locations as well as differences between these rates and other national data sources, such as the 1977 Nationwide Personal Transportation Survey (NPTS). - Effects of older data (collected in the 1960's) included in the more current trip generation rates. - Lack of detailed guidelines on the use of existing trip generation rate data. This publication provides guidance on the use of trip generation rates in light of these concerns. In addition three related issues are also addressed: - The effect of socioeconomic variables on residential trip generation rates. - Reduced external trips generated by multi-use centers (i.e. a percentage of the trips generated by a multi-use center are internal and remain on site). - Capture rates for "pass-by" traffic (i.e. trips attracted to the development from traffic normally passing-by the site). This technical concern for trip rate accuracy has emerged coincidentally with increased emphasis on site access studies. To illustrate this emerging issue, the FHWA has completed a 2 study to: 1) investigate the existing uses of private funds for highway improvements 2) evaluate the mechanisms-used to obtain private funding and, 3) to recommend improvements for which private funding may be used (52). A key issue in the technical process is trip generation rates and their subsequent role in the estimation of traffic impact and needed road improvements. Since trip rates are so important to local zoning regulations it is essential-for the success of this new concept of private/public cost sharing to have accurate trip rate information. In most areas the ITE Trip Generation Report is considered the reference manual on trip generation. Accurate trip rates will enhance the application and accuracy of the quick response techniques and significantly aid site access analyses in the United States -- and also facilitate equitable cost sharing negotiations between public and private interests. The findings of this study have implications for the public and private sectors in achieving cost effective roadway improvements. The trip rates and their adjustment factors developed in this study can be used to: - conduct site access studies including the estimation of traffic generated by either a single use! multi-use or planned unit development. - forecast daily and peak hour traffic volumes for the geometric design of traffic circulation and access plans. - evaluate on-site alternative land use development conditions to optimize or minimize the traffic impact on the adjacent highway network. - aid in the determination of the private developer's share in local transportation improvements. - estimate daily and peak hour trip rates and traffic flows for transportation corridor and sub-area analyses. The S.I.T.E. Handbook presents details on the uses of trip generation rates (50). 3 [THIS PAGE INTENTIONALLY LEFT BLANK] 4 II. ANALYSES OF TRIP RATE DATA DATA SOURCES The major data source for the trip rates presented in this report was the ITE data base. An extensive literature review was conducted, and agency contacts were made to identify trip generation data that were collected since the latest update of the ITE trip rates publication.(45) This search resulted in the identification of over 500 references. Approximately seventy of these references included relevant trip generation data. Accordingly, it was decided to augment the ITE data base with the new data collected. All new data collected were checked against the existing ITE data base to avoid duplication of data sources. The data sources that were not duplicative of the ITE data base are presented in Table 1. It should be noted that NCHRP Report 187, Table I (83), incorporates ITE trip rate data as well as other sources. The other sources in NCHRP 187 were neither described in the NCHRP Report nor available through the Transportation Research Board. Therefore, NCHRP 187 trip rate data could not be included in the data base for this report. Because of concerns about travel habits changing due to the energy crisis, analyses comparing older data (pre-1973) and the newer data (post-1973) were performed. Based on statistical tests such as t-test and f-ratios, it was concluded that there were no significant differences between the mean trip rates of older data (pre-1973) and the newer data (post-1973) for all land uses analyzed. (See Appendix B for a detailed analysis.) In some cases, the mean trip rates appeared to be different but due to a large standard deviation, the statistical tests indicated no significant difference. Accordingly, all data regardless of age were used to develop the updated trip rates. Data on land uses not included in ITE were collected and analyzed. These land uses include high technology office buildings, townhouse office buildings, bowling alleys, department stores, drug stores, beauty salons, dry cleaners and printing shops. Some of these land uses have limited sample sizes and the trip rates are not included in this report. UPDATING TRIP RATES Trip generation rates for non-residential uses were estimated for each of the three variables: location, auto occupancy and transit usage. Location was categorized as urban, suburban and rural. Data on location were generally available for industrial parks, hotels, hospitals, office buildings, and shopping centers in the range of 100,000 to 499,999 square feet of gross leasable area (GLA). 5 TABLE 1 DATA SOURCES SOURCE SURVEY TYPE REFERENCE [1] Prince George's County, Maryland Driveway 47 Maryland-National Capital Park and Planning Commission Driveway 48 East-West Gateway Coordinating Council Driveway 29 Anne Arundel County, Maryland Driveway 90 Palm Beach County, Florida Driveway 51,53 Virginia Department of Highway & Transportation Driveway 80 Connecticut DOT Home Interview 21 Southeast Michigan COG Home Interview 79 Metropolitan Transportation Commission Home Interview 54 Maryland DOT Driveway 58 California DOT Driveway 11 San Diego Association of Driveway/ Governments Home Interview 20,43,74 Delaware DOT Driveway 27 Kellerco Data Files Driveway 49 New Hampshire Department of Public Works and Highways Driveway 64 West Virginia DOT Driveway 94 Chicago Driveway/ Home Interview 16,17 Cincinnati, Ohio Home Interview 14 Richmond, Virginia Driveway/ Home Interview 70 Washington COG Driveway 63 Virginia Highway and Transportation Research Council Driveway 92,93 Fairfax County, Virginia Driveway 89 Baltimore Disaggregate Data Set Home Interview 31 [1] See list of references at the end of the Report 6 Auto occupancy data were available for manufacturing land use only. However, the sample size was not adequate for any statistical tests. Transit availability data for industrial/manufacturing uses and shopping centers in the range of 100,000 to 499,999 square feet of GLA were included in the data base. The summary of mean daily trip rates with and without transit availability are presented in Table 2. For the industrial park, the mean daily, trip rates for sites not served by transit were higher than sites served by transit. This is contrary to what is generally expected; however, data were not available to determine the reasons for this anomaly. T-tests were carried out to determine if a significant difference existed between the mean trip rates of sites with and without transit available. In all cases the statistical tests showed that the means are not significantly different. It should be noted that although. the means are intuitively different, the high standard deviations result in the finding of no statistical differences. The results of the updating analyses are presented in the next Chapter along with the use of the updated trip fates. 7 TABLE 2 DAILY TRIP RATES AS A FUNCTION OF TRANSIT AVAILABILITY Click HERE for graphic. 8 III. TRIP GENERATION RATES This chapter presents the results of the trip generation analyses as the updated Table I of NCHRP Report 187 (83). The updated trip rates are presented in Table 3. For each land use, the following data are included: - Corresponding ITE land use code(s) - The weighted mean daily trip rates for one or more independent variables, such as dwelling units, acres, employees and square- feet of gross floor area - The minimum daily trip rate in the sample data - The maximum daily trip rate in the sample data - The standard deviation about the mean trip rate - The standard error of mean which is estimated as (standard deviation)/(square root of number in sample) - The weighted mean trip rates for the AM and PM peak hour of the adjacent street traffic. The directional distribution of trips is also presented - The weighted mean trip rate for the peak one hour of the generator is included along with the directional distribution of trips - Additional adjustment factors are provided for residential use characteristics such as household size, vehicle ownership and residential density. These adjustment factors are presented in Table 3a. it should be noted that the adjustment factors for residential characteristics are to be added (or subtracted) from the daily trip rates. The application of these factors is described in a later section. USE OF TRIP RATES The trip generation rates presented in Table 3 should be used with care, If local data are available for a similar site. then the local data should be used. Table 3 can be used to estimate the amount of traffic that may be generated by a specific land use or site. Appropriate adjustment factors for residential characteristics may be applied. Further adjustments due to increased ridesharing or proximity to transit may be applied using other techniques such as the office trip generation rate analysis technique, described in the SITE.Handbook, (50) and/or Using The ITE Trip Generation Report (5). This reference (5) describes the uses of trip generation rates including methodologies for adjusting trip rates for Transportation Systems Management (TSM) actions such as ridesharing, etc. The following sections briefly describe the use of trip rates presented in Table 3. 9 TABLE 3 VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------ --------------------------- --------------------- STD ERROR# OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE PORTS & TERMINALS(000) Water Ports BOSBER 171.52 38.60 338.57 112.98 42.70 7 010 ACRE 11.95 4.95 19.47 5.45 2.06 71 Air Ports CFL/DY 70.85 51.33 78.44 13.59 7.85 3 020 FLT/DY 3.05 0.96 31.38 8.83 2.66 11 EMP 21.45 11.55 284.29 102.29 38.66 7 ACRE 4.77 0.99 24.89 8.25 2.49 11 Comm Airport CFL/DY 122.21 99.50 138.74 22.55 13.02 3 021 FLT/DY 8.34 1.62 122.97 60.71 35.05 3 EMP 15.39 14.11 22.94 6.25 4.42 2 ACRE 11.48 9.13 16.22 3.63 2.10 3 Gen Avi Airport FLT/DY 2.50 * * NA NA * 022 EMP 6.50 * * NA NA * ACRE 3.60 * * NA NA * Truck Terminals 1K SF 9.86 NA NA NA NA 1 030 EMP 6.99 4.22 47.29 30.45 21.53 2 ACRE 81.86 66.20 100.08 23.96 16.94 2 INDUSTRIAL(100) Gen Lght Indus 1K SF 6.98 1.58 16.88 4.44 1.05 18 110 EMP 4.50 1.53 10.42 2.12 0.49 19 ACRE 76.03 5.21 159.38 43.90 10.07 19 Gen Heavy Indus 1K SF 1.50 0.58 1.84 0.69 0.40 3 120 EMP 2.05 0.75 11.05 4.99 2.50 4 ACRE 15.62 1.66 55.13 24.71 12.36 4 Indus Park 1K SF 7.00 0.91 36.97 7.71 1.12 47 130 EMP 3.59 1.37 8.80 1.92 0.29 45 ACRE 62.82 13.87 1272.63 209.24 32.68 41 Manufact 1K SF 3.85 0.50 52.05 6.90 0.89 60 140 EMP 2.09 0.60 6.66 1.21 0.16 60 ACRE 38.88 2.54 396.00 69.43 9.28 56 Warehouse 1K SF 4.88 1.51 17.00 3.76 0.97 15 150 EMP 3.89 1.47 15.71 3.74 0.97 15 ACRE 56.08 20.23 255.80 59.64 15.94 14 ____________________________________________________________ LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT-OR SHIP BERTH DU DWELLING UNIT 10 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 11 TABLE 3 , continued VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ --------------------------------------------------- STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE RESIDENTIAL(200) S-F Det Hous DU 10.03 4.31 21.90 2.37 0.13 313 210 ACRE 26.18 1.82 275.19 31.15 2.82 122 Urban DU 11.28 ACRE 29.45 Suburban DU 9.06 ACRE 23.64 Rural DU 9.73 ACRE 25.40 Apartment DU 6.11 0.54 12.34 1.92 0.17 122 220 ACRE 23.79 1.82 361.83 67.98 8.37 66 Urban DU 6.87 ACRE 26.76 Suburban DU 5.52 ACRE 21.48 Rural DU 5.93 ACRE 23.08 Condomin DU 5.40 0.57 11.79 2.28 0.31 55 230 ACRE 68.04 14.81 337.66 74.29 17.04 19 Urban DU 6.08 ACRE 76.55 Suburban DU 4.88 ACRE 61.44 Mobile Home DU 4.78 2.29 7.60 1.44 0.28 26 240 ACRE 9.13 15.86 85.89 17.19 3.19 29 Retire Comm DU 3.30 2.80 9.90 NA NA 3 250 Plan Unit Dev DU 7.49 5.23 14.38 2.62 0.70 14 270 ACRE 46.78 41.85 50.80 4.24 2.12 4 (Suburban) LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 12 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 13 TABLE 3 , continued VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ ---------------------------------------------- STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE LODGING(300) Hotel ROOM 8.70 5.31 9.58 1.58 0.60 7 310 EMP 14.34 8.85 24.47 6.13 2.74 5 ACRE 1430.19 755.38 1663.55 395.72 197.86 4 Urban ROOM 8.68 EMP 14.31 ACRE 1427.33 Suburban ROOM 9.34 EMP 15.39 ACRE 534.59 Motel ROOM 6.13 4.17 10.04 2.54 0.90 8 320 EMP 12.81 7.20 41.00 10.69 3.38 10 ACRE 180.71 38.41 364.44 106.57 32.13 11 Resort Hotel ROOM 18.40 7.11 52.41 14.33 5.07 8 330 EMP 10.27 NA NA NA NA 1 ACRE 237.96 33.42 1811.11 568.51 201.00 8 RECREATION(400) Parks PRKSPC 7.81 2.93 24.28 6.74 2.25 9 410 EMP 96.17 42.35 183.62 59.56 29.78 4 ACRE 30.37 2.99 214.55 62.22 16.07 15 City Parks PRKSPC 6.50 1.91 12.55 5.51 3.18 3 411 EMP 51.10 47.06 66.67 9.97 5.76 3 ACRE 3.66 1.04 129.83 55.36 24.76 5 County Parks PRKSPC 2.18 0.42 21.00 5.58 1.61 12 412 EMP 26.46 23.33 183.33 50.32 13.96 13 ACRE 5.09 0.17 81.24 21.12 5.12 17 State Parks PRKSPC 1.15 0.40 3.13 0.97 0.34 8 413 EMP 60.20 21.93 183.33 67.14 20.24 11 ACRE 0.69 0.05 16.67 6.51 1.81 13 Marinas BOSBER 2.96 1.91 0.4 2.33 0.70 11 420 EMP 251.47 231.50 276.67 24.13 12.06 4 ACRE 20.92 10.32 75.45 32.64 18.84 3 Golf Course PRKSPC 5.32 1.75 16.39 3.47 0.87 16 430 EMP 20.63 10.90 75.00 18.27 5.07 13 ACRE 6.91 2.33 22.78 4.42 0.94 22 LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 14 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 15 TABLE 3continued VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ ------------------------------------------------- STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE INSTITUTIONS(500) Military Base EMP 1.80 NA NA 501 DEFEMP 2.20 NA NA CIVEMP 7.10 NA NA Day Care Cen STDNT 4.98 4.10 7.10 1.22 0.55 5 511 1K SF 79.14 57.20 125.10 26.40 11.81 5 (Suburban) EMP 33.20 25.60 50.40 12.73 5.70 5 Elem School STDNT 1.02 0.45 1.82 0.35 0.06 40 520 EMP 13.10 4.47 26.37 5.28 0.84 40 ACRE 33.69 3.72 123.80 28.41 4.49 40 High School STDNT 1.38 0.71 2.49 0.52 0.10 27 530 EMP 16.79 4.28 32.87 6.52 1.26 27 ACRE 23.81 1.02 103.20 26.71 5.97 20 Jr Comm Coll STDNT 1.58 0.94 27.52 5.65 1.23 21 540 EMP 10.06 NA NA NA NA 1 ACRE 11.90 NA NA NA NA 1 Universit STDNT 2.41 1.40 3.89 0.92 0.37 6 550 EMP 14.35 NA NA NA NA 1 ACRE 107.28 NA NA NA NA 1 Libraries EMP 49.51 36.80 81.91 19.65 9.83 4 590 ACRE 343.78 221.65 909.00 296.91 148.46 4 MEDICAL(600) Hospital BED 11.84 3.00 32.83 7.46 1.49 25 610 EMP 5.03 2.17 11.11 2.35 0.49 23 ACRE 167.73 24.07 1012.50 229.97 51.42 20 Urban BED 13.08 EMP 5.56 ACRE 185.34 Suburban BED 11.21 EMP 4.76 ACRE 158.86 Nurs Home BED 2.60 1.88 3.97 0.57 0.13 18 620 EMP 4.03 2.53 9.69 1.99 0.47 18 Clinics BED 15.96 NA NA NA NA 1 630 EMP 5.89 NA NA NA NA 1 ACRE 91.19 NA NA NA NA 1 LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 16 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 17 TABLE 3, continued VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ ------------------------------------------------------ STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE OFFICE(700) Gen Off Bldg 1K SF 12.43 3.60 28.80 6.03 0.95 39 710 EMP 3.54 2.42 6.22 1.16 0.24 23 ACRE 250.64 50.75 299.70 1580.16 116.03 25 Urban 1K SF 10.33 EMP 2.94 ACRE 208.28 Suburban 1K SF 14.81 EMP 4.22 ACRE 298.64 Med Off Bldg 1K SF 39.83 38.68 42.55 2.74 1.94 2 720 EMP 12.20 NA NA NA NA 1 ACRE 6666.67 NA NA NA NA 1 Urban 1K SF 33.10 EMP 10.14 ACRE 5540.00 Suburban 1K SF 47.46 EMP 14.54 ACRE 7943.34 Gov Off Bldg 1K SF 67.72 NA NA NA NA 1 730 EMP 11.95 NA NA NA NA 1 ACRE 66.25 NA NA NA NA 1 Urban 1K SF 56.28 EMP 9.93 ACRE 55.05 Suburban 1K SF 80.69 EMP 14.24 ACRE 78.94 Civic Center 1K SF 25.00 NA NA NA NA 1 740 EMP 6.09 NA NA NA NA 1 Off Parks 1K SF 20.65 9.40 30.30 11.68 6.74 3 750 EMP 3.33 2.92 3.53 0.32 0.19 3 ACRE 276.38 153.68 340.87 93.86 54.19 3 Urban 1K SF 17.16 EMP 2.77 ACRE 229.67 Suburban K SF 24.60 EMP 3.97 ACRE 329.31 LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 18 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 19 TABLE 3 , continued VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ ------------------------------------------------------- STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE Research Cen 1K SF 5.34 1.78 12.98 4.02 1.42 8 760 EMP 2.37 0.96 5.33 1.29 0.43 9 ACRE 57.25 15.61 1323.08 525.95 214.72 6 Urban 1K SF 4.44 EMP 1.97 ACRE 47.57 Suburban 1K SF 6.36 EMP 2.82 ACRE 68.21 Hi-Tech Off Bldg 1K SF 7.28 4.08 8.71 2.18 1.26 3 770 EMP 2.76 2.39 3.27 0.47 0.27 3 Urban 1K SF 6.05 EMP 2.29 Suburban 1K SF 8.67 EMP 3.29 Twnhs Off Bldg 1K SF 23.47 19.06 24.78 4.94 2.85 3 780 Urban 1K SF 19.50 Suburban 1K SF 27.96 RETAIL(800) Disc Shop Ctr 1K SF 70.13 25.53 106.88 27.83 10.52 7 815 EMP 32.53 28.08 35.46 3.10 1.38 5 ACRE 456.31 127.64 480.63 302.57 135.31 5 Shp Ctr(<100k sf) 1K SF 83.43 18.10 270.89 45.47 5.29 74 820,821 EMP 38.18 17.68 82.05 16.71 3.28 26 ACRE 786.72 303.55 2277.27 528.2599,83 28 Sh Ctr(100k.500k) 1K SF 49.64 2.80 116.14 22.26 1.84 146 822,823,824,825 EMP 28.40 15.45 53.05 10.13 1.85 30 ACRE 462.40 46.94 1431.67 1268.74 40.06 45 Sh Cr(500k.1000k) 1K SF 34.44 10.30 61.18 11.38 1.42 64 826 EMP 16.72 3.20 53.62 11.35 2.32 24 ACRE 405.22 119.10 1178.12 1251.48 51.33 24 Sh Ctr(>1000k sf) 1K SF 29.59 11.99 72.82 16.13 3.80 18 827,828 EMP 12.50 6.14 42.41 14.60 5.16 8 ACRE 268.31 62.17 1259.74 1376.87 125.62 9 LEGEND FOR UNITS: 1K SF 1,000 SQ. FT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 20 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 21 TABLE 3 , continued TABLE 3 VEHICLE TRIPS PER DAY LAND USE TO & FROM LAND USE GENERATOR (Rate/Unit as noted) STATISTICS ------------------ ------------------------------------------------------- STD ERROR # OBS. DESCRIPTION STD OF IN & ITE CODE UNITS MEAN MIN MAX DEV. MEAN SAMPLE Qual StDwn Rest SEAT 2.95 1.77 5.50 1.16 0.32 13 831 1K SF 97.27 48.56 139.33 30.81 8.54 13 EMP 14.53 9.16 29.98 5.93 1.65 13 ACRE 478.44 223.21 806.32 1201.42 60.73 11 Fast Food Restau SEAT 22.25 8.88 35.78 8.21 2.28 13 833 1K SF 685.61 284.00 359.50 280.14 77.70 13 EMP 54.78 28.40 90.63 22.05 6.37 12 ACRE 2985.22 2772.22 3298.57 268.22 154.86 3 New Car Sales 1K SF 47.52 15.45 79.00 36.15 20.87 3 841 EMP 24.04 10.82 38.55 13.94 8.05 3 ACRE 385.57 162.25 526.67 1206.84 119.42 3 Service Stations PUMP * * * NA NA * 844 STN * * * NA NA * Food Store 1K SF * * * NA NA * 850 ACRE * * * NA NA * Conv Market 1K SF 756.44 396.00 1438.00 334.23 118.17 8 851 EMP 275.07 158.40 359.50 24.02 67.95 8 ACRE 289.70 221.33 419.50 74.37 33.26 5 SERVICES(900) Walk-in-Bank 1K SF 169.00 NA NA NA NA 1 911 EMP 44.47 NA NA NA NA 1 ACRE 1056.25 NA NA NA NA 1 Drive-in-Bank 1K SF 291.11 134.67 1520.00 1391.06 117.91 11 912 EMP 79.79 31.85 380.00 101.75 30.68 11 ACRE 849.30 414.00 1647.50 1545.77 272.88 4 Walk-in Sv & Ln 1K SF 61.00 NA NA NA NA 1 913 EMP 30.50 NA NA NA NA 1 ACRE 261.42 NA NA NA NA 1 Drive-in Sv & Ln 1K SF 74.17 NA NA NA NA 1 914 EMP 49.44 NA NA NA NA 1 ACRE 1483.33 NA NA NA NA 1 LEGEND FOR UNITS: 1K SF 1,000 SQ. PT. GFA CFL/DY COMMERCIAL FLIGHT PER DAY ACRE ACRE CIVEMP CIVILIAN EMPLOYEE BED HOSPITAL BED DEFEMP DEFENSE FORCES EMPLOYEE BOSBER BOAT OR SHIP BERTH DU DWELLING UNIT 22 TRIP GENERATION RATES Click HERE for graphic. Vehicle Trips Per... EMP EMPLOYEE ROOM HOTEL/MOTEL ROOM FLT/DY FLIGHT PER DAY SEAT RESTAURANT SEAT PRKSPC PARKING SPACE STDNT STUDENT PUMP GAS(OR DIESEL) PUMP STN GAS(OR DIESEL) STATION 23 Applicable Uses of Trip Rates The most common use of trip rates is for site access studies. A site access study describes how traffic generated by either new land use(s) or replacement land use(s) will be served by an existing or future road network (50). The analyses allow for the effect of site generated traffic to be compared with the traffic on the adjacent road network. The site access study is being used more and more as a basis for establishing a developer's share of roadway improvements and therefore trip rates play a critical role in the process. Many local jurisdictions are using trip rates as the basis for assessments in local transportation improvement districts. The design hour volumes in the vicinity of sites can be forecast using Table 3 trip rates for design of the roadway improvements. Alternative land use scenarios can be tested as part of site access studies, to determine the optimization land density and mix with respect to traffic flow. In some cases, a reverse analysis can be conducted to determine the density and mix of land uses that can be accommodated by a given roadway network (50). In addition to site access studies, Table 3 can also be used for corridor and sub-area analyses (84). Quick response techniques, both manual and micro-computer, for transportation modelling have been developed that can use peak hour or daily trip rates from Table 3 (6, 83). Some techniques utilize highway networks in the trip distribution/assignment procedure. In these cases the trip rate data for residential uses is converted into trip productions and trip attractions as in the four step Urban Transportation Planning process. Selection of Trip Rates The weighted mean daily vehicle trip rates along with the minimum and maximum trip rates measured in the sample are included in Table 3. The weighted mean trip rates are presented for the peak hours (AM, PM and peak hour of generator). The weighted mean trip rates are recommended for use by planners. The standard deviation is provided for a measure of how the individual trip rates in the sample are spread out from the mean, A large standard deviation indicates that the individual trip rates are distant from the mean trip rate. The standard error of mean helps to determine the potential degree of discrepancy between the sample mean and the usually unknown population mean. Deviations from the mean trip rates may be dependent on the values of unknown variables such as the extent of ridesharing, proximity to transit, or parking costs. 24 The selection of the appropriate time period of analysis is related to the peak generation periods of the subject site and the adjacent street traffic characteristics (5). In general, the time periods selected should result in the maximum impact of the site generated traffic on the adjacent street traffic. In most cases, the trip rates for the AM and PM peak hours of the adjacent street traffic would be utilized for conducting the site access studies and estimating roadway needs. Some sites such as shopping centers have a considerable impact during the PM peak hour of the adjacent street system, but may have an even greater impact during the evening hours or on Saturdays. Therefore, in such cases the generator may have to be analyzed for all three time periods (PM peak hour e.g. 5-6 PM Friday; evening peak hour e.g. 7-8 PM Friday; Saturday peak hour e.g. 1 to 2 PM Saturday) to determine the design requirements and the impact on the adjacent street traffic flow (5). Selection of Independent Variable Trip rates for land use generators in Table 3 are presented for more than one independent variable. In each case, the recommended independent variable is listed first. This independent variable is recommended based on the sample size, the general data availability and the correlation between trips and the independent variable. Selection of the independent variable is critical for determining the total trips generated. Consider for example, an office building while the number of employees is an excellent indicator of trip rates. This information is generally not available. Further more, the number of employees may change over time due to new tenants or change in tenant mix. Therefore, gross building area is listed as the first choice in Table 3. The S.I.T.E. Handbook presents a discussion on the office trip generation rate including the square feet per employee typically found in office buildings. The other independent variable presented in Table 3 is acres. This information is generally available. Due to the variations in floor area ratio or buildable area, correlation between trips and acres is not as good as that between trips and employees or gross building area. In some cases (generally, in the planning stage), only the parcel size is known. In these cases, common land use densities can be used to determine an estimate of the independent variable with a higher correlation trip rates. For industrial uses, employee densities per acre of land and trip rates per employee may be utilized. For shopping centers and office buildings, 25 building density can be estimated from parcel size using the Floor Area Ratio, and the trip rate per 1000 gross square feet can be utilized. For residential uses, the applicable zoning code can be used to determine the number of dwelling units per acre of land. Table 4 presents some land use densities (5). Application of Adjustment Factors Adjustment factors for residential characteristics (household size, vehicle ownership and density) are presented in Table 3a. The adjustment factors are to be added (or subtracted) from the daily trip rates with dwelling units as the independent variable. Furthermore, any combination of adjustment factors may be applied. If specific residential characteristic data are unavailable, then the mean trip rate should be utilized. The. application of adjustment factors to peak hour trip rates, requires the computation of the ratio of the daily adjusted trip rate to daily mean trip rate. The procedure is illustrated in Table 5. 26 TABLE 3a ADJUSTMENT FACTORS FOR RESIDENTIAL CHARACTERISTICS Click HERE for graphic. ___________________________ (1) Adjustment factors to be added (or subtracted) from the mean daily trip rate per dwelling unit. TABLE 4 TYPICAL LAND-USE DENSITIES Land Use Density 110 General Light Industrial 16.4 employees per acre 1.7 employees per T.G.S.F. 120 General Heavy Industrial 7.6 employees per acre 1.6 employees per T.G.S.F. 130 Industrial Park 18.0 employees per acre 2.0 employees per T.G.S.F. 140 Manufacturing 18.5 employees per acre 1.9 employees per T.G.S.F. 150 Warehouse 14.0 employees per acre 1.25 employees per T.G.S.F. 711 General Office, Under 100 T.G.S.F 4.7 employees per T.G.S.F. 712 General Office, 100-199.9 T.G.S.F. 4.2 employees per T.G.S.F. 713 General Officer Over 200 T.G.S.F. 3.1 employees per T.G.S.F. 720 Medical Office Building 3.7 employees per T.G.S.F. 770 High Tech Electronics 40-100 employees per acre 814-828 Retail Center 10-14 T.G.L.S.F. per acre NOTE: T.G.S.F. = thousand gross square feet; T.G.L.S.F. = thousand gross leasable square feet. SOURCE: Reference (5) Reported with permission from ITE. 28 TABLE 5 APPLICATION OF ADJUSTMENT FACTORS PROBLEM: Determine daily and PM peak hour trip rates per dwelling unit for a proposed single family detached housing development located in a suburban area. Residential density = 3.5 d.u./acre Average household size = 2.5 persons Average vehicle ownership per household = 2.5 SOLUTION: Mean Daily Trip Rate Refer to Table 3 for trip rates by land use type Land use generator "single family detached" (210). Daily trip rate/d.u. in the suburban area = 10.03 Adjustment Factors for Mean Daily Trip Rate Refer to Table 3a for adjustment factors due to residential characteristics. Household Size (2.5) = 1.8 Vehicle Ownership (2.5) = + 2.9 Density (3.5 d.u./acre) = 0.0 ------------------------------- -------- Current time adjustment factor = + 1.1 Adjusted daily trip rate = 10.03 + 1.1 = 11.13 trips/d.u. PM Peak Hour Trip Rate Adjustment factor for PM peak hour adjusted daily trip rate for suburban location = 11.13/10.03 = 1.11 mean daily trip rate for suburban location The AM peak hour trip rates. if desired, should also be factored by this adjustment factor. Refer to Table 3. Land Use 210. for PM Peak Hour Inbound/Outbound/Total vehicle trip rates. PM peak hour trip rates/d.u. In = 0.64 x 1.11 = 0.71 trips/d.u Out = 0.36 x 1.11 = 0.40 trips/d.u. Total = 1.00 x 1.11 = 1.11 trips/d.u. 29 IV. OTHER FACTORS AFFECTING TRIP RATES The previous chapter presented updated trip rates for a wide range of land uses; including the development and application of adjustment factors for residential use. In addition to the application of the trip rate process to individual land uses, there are two other conditions which require trip rate adjustments. - Multi-use developments (MUD) which consist of a complimentary mix of land uses but for which individual land use trip rates cannot be simply added without adjustment. - Development located along major travel corridors where current pass-by traffic will be 'captured' by the new land use. A straightforward application of Table 3 will make the trips rates too high. MULTI-USE DEVELOPMENT A Multi-Use Development (MUD) may be described as a concentration of compatible land uses which are physically integrated by means of internal pedestrian or roadway network system. The multi-use development was initially a concept of private developers who were aware of its market potential. They were also influenced by public planning agencies which became aware of the need to encourage Planned Unit Development (PUD). A PUD is usually defined as a variety of land use types with a predominance of residential development. A PUD by definition is different from a multi-use development (MUD) which consists of more retail and office uses Because MUD/PUD land use components tend to complement each other, it reduces the need for persons to make vehicular trips beyond the development. The composition of a MUD/PUD determines the amount of interaction among its land use components. The trips on the roadway network, external to the development, vary depending on the mix of land uses within the development. Two studies on PUDs have been conducted recently (70,92). Both PUDs were located in suburban areas. One PUD consisted of a total of 2,330 residential units including 1,138 single family detached units, 1000 townhouses and 192 garden apartments. (See Figure 1). Also included is the PUD were the following land uses: - two elementary schools - a middle school - a day care center 30 FIGURE 1 NORTHERN VIRGINIA PUD Click HERE for graphic. 31 - 9,000 square feet of retail area with a convenience store, beauty salon, florist, dry cleaner, a restaurant and a bank. - six pump self-service gas stations fire and rescue station - community center with a swimming pool Based on the ground count data for this PUD, it was estimated that approximately 28 percent of the residential trips occur within the development as internal trips. This leaves only 72 percent of the trips generated by the residential units within the PUD impact the external roadway network. Another study analyzed external trips generated by uses in a PUD (located in Richmond, Virginia) utilizing home interview surveys, roadside origin-destination surveys, ground counts and turning movement counts (70). This PUD, illustrated in Figure 1. contains approximately 2300 occupied dwelling units. A vast majority are single family detached units with some multi-family townhouse type units. There are two primary areas of commercial development. The following land uses are located in the Richmond PUD: - 85,000 gross square feet of primary commercial center with a grocery store, a drive-in savings and loan, a convenience food mart, a drug store, several small offices and a variety of small-shops - 16,600 gross square feet of medical center a small computer store - 63,000 square feet of business park - recreational facilities including a golf courser tennis courts, swimming facilities and several lakeside recreational facilities Table 6 presents an estimate of the number of external trips generated by residential and commercial uses in the PUD. The percentage of external trips varies between 30 percent and 65 percent depending on the use and time period considered. The residential uses resulted in 50 percent daily external trips; however, these percentages may vary depending on the quantity of commercial use in the PUD. A development with little or no commercial use may be 10 percent higher, whereas, a development with more commercial use may be 5 percent lower (70). More data are necessary to verify these estimates. Figure 3 presents relationships between percentage of internal trips and the composition of a PUD based on one study (70). Curve A relates the percentage of internal home-based work trips to a ratio between office area (in gross square feet) and the number of residential units. 32 FIGURE 2 RICHMOND, VIRGINIA PUD Click HERE for graphic. 33 TABLE 6 : EXTERNAL TRIPS GENERATED BY USES IN A PUD Click HERE for graphic. ___________________________ (1) Rates are for street peak hours (7-8 AM and 4-6 PM). Peak hours of generator closely coincide with peak hours of adjacent street traffic. (2) External percentages for residential development may vary depending on quantity of commercial use in PUD. (3) Estimated based on student population and trip purpose data from residential survey. (4) Not available. Number of daily trips was approximated for this use, based on ratio of peak to daily from other sources. (5) Percent external for office based on employee surveys and does not include visitors. (6) School trip rate based on no. students. (7) Daily external % for non-residential uses based on weighted average of midday and peak period results Source: Reference (70) 34 FIGURE 3 ILLUSTRATIVE RELATIONSHIP BETWEEN PERCENTAGES OF INTERNAL TRIPS AND RATIO OF COMMERCIAL SPACE TO NUMBER OF RESIDENTIAL UNITS Click HERE for graphic. NOTE: Relationships are approximated based on one PUD data. More data necessary before relationships can be widely applied. These relationships should be reasonably applicable to PUDs not exceeding 3000 to 4000 residential units. SOURCE: Reference (70) 35 Curve B relates internal home-based non work trips to a ratio of commercial area and the number of residential units. It should be noted that both curves are approximate, but should be generally applicable to PUDs not exceeding 3000 to 4000 residential units. SITE DEVELOPMENT CAPTURE RATES FOR PASS-BY TRAFFIC Site access studies generally assume that all trips to the new development are new trips which were not made prior to the development being completed. This is incorrect since a portion of the new trips are already being made to other similar and existing developments. In this case a route diversion occurs. A second assumption for site access studies is that all of the trips are primary trips being made for a specific purpose; to return directly to their place of origin. Several land use generators such as shopping centers, drive-in (fast food) restaurants, service stations, convenience markets and other support services (banks, etc.), capture trips from the normal traffic passing-by the site. For many of these trips, the stop at the site is a secondary part of a linked trip such as from work to shopping center to home. in all of these cases, the driveway volumes at the site are higher than the actual amount of traffic added to the adjacent street system, since some of the site generated traffic was already counted in the adjacent street traffic. Table 7 presents the limited information available on the capture rates for pass-by traffic. In the case of shopping centers, the trip rates from Table 3 can be reduced by 25 percent (from Table 7) to determine the actual traffic to be added to the adjacent street network: the total driveway volumes as well as the traffic on the internal roadway network should be based on Table 3 rates without any reduction due to capture rates. Information on trips "diverted' from a nearby roadway based on one study are also presented in Table 7. The results in Table 7 should be used cautiously since they are based on a limited number of studies. Since MUD or PUD developments may include various modes of travel, the user may also wish to refer to the ITE publication 'Using the ITE Trip Generation Report(s)" for methodologies for adjusting trip rates to reflect the use of alternative modes of transportation (5) . 36 TABLE 7 SITE DEVELOPED CAPTURE RATE FROM PASS-BY TRAFFIC PERCENT OF SITE TRAFFIC Land Use Primary From Pass-by Diverted From Trips Traffic Another Route [2] [3] --------------- ------ ------ ------ 820-828 Shopping 35% 25% 40% Centers 833 Fast Food Restaurant 45% [1] [1] 844 Service Station 26% [4] 58% 16% [4] 851 Convenience Market [1] 45% [1] SOURCE: Reference (5), except as noted Reprinted with permission from ITE. [1] Not measured [2] These are trips that were made for a-specific purpose and returned directly to their place of origin. [3] These are trips in which the stop at the site land use is part of the current sequence of stops. This involves trip chaining of a series of trip times. Furthermore the stop requires a significant route diversion from the route that would be followed otherwise, if this particular stop were not made. [4] Source: Reference (49) 37 An extensive literature review and data collection effort was carried out as part of the study. A detailed work plan was developed to analyze the data collected including the development of trip rate adjustment factors for site location availability of transit and vehicle occupancy. However, data on these factors were generally missing from the data base. In some cases where data was available, the sample size was not large enough to conduct statistical tests. Trip rates by location were developed for land uses with adequate samples. For residential uses, trip rate adjustment factors were developed for certain residential characteristics. In order to fill the data gaps, recommendations for further research are presented in Appendix C. 38 REFERENCES 1. 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Ohio - Kentucky - Indiana Regional Council of Governments, Long Range Plan, Cincinnati, Ohio, November, 1983. 68. Pas, Eric, I. "An Empirical Comparison of Zonal, Household and Personal Models of Home-Based Trip Generation". Traffic Engineering and Control. Volume: 19, 1978. 69. Reid, Fred A. "Critique of ITE Trip Generation Rates and an Alternative Basis for Estimating New Area Traffic. TRB 874. Washington, D.C., 1982. 70. Richmond Regional Planning District Commission, "Planned Community External-Internal and Internal-Internal Traffic Generation Study", Richmond, Virginia, June, 1984. 71. Roads and Transportation Association of Canada, "A Review of Trip Generation Analysis Procedures Used In Canadian Urban Transportation Planning Studies", Technical Publication Number 4, Toronto, 1974. 72. Ruiter, Earl R. "The Development of Home-based Trip Production Models and their Comparison with 1978 Data", Cambridge Systematics, Inc., October, 1979. 73. Sachdev, Labh S. and Leonhardt, Karlfritt, "Trip Generation Update", Puget Sound Governmental Conference, 1972. 74. San Diego Association of Governments, "San Diego Traffic Generators", May, 1979. 75. Southwestern Pennsylvania Regional Planning Commission, "Person Trip Production Rates per Household". Unpublished. 76. SPSS Inc. SPSS User's, Guide, McGraw-Hill Book Company, New York, 1983. 77. Sterns, M.D. "Social Impacts of the Energy Shortage Behavior and Attitude Shift". 44 92. Virginia Highway and Transportation Research Council "Residential Trip Generation in Northern Virginia". Unpublished. 93. Virginia Highway & Transportation Research Council, "Special Land Use Trip Generation at Special Sites", January, 1984. 94. West Virginia Department of Transportation, "Trip Generation Rates", Unpublished. 95. Zevin, I., Trip Generation Study of Various Land Uses. Connecticut Department of Transportation: June, 1974. 96. Zevin, I., Trip Generation Study of Various Land Uses. Supplement A. Connecticut Department of Transportation: March, 1975. 46 APPENDIX A LIST OF AGENCIES CONTACTED 1. Washington Council of Governments, Washington, D.C. 2. Southwestern Pennsylvania Regional Planning Commission Pittsburgh, Pennsylvania 3. Metropolitan Transportation Commission, San Francisco, California 4. Southeast Michigan Council of Governments, Detroit, Michigan 5. Ohio-Kentucky-Indiana Council of Governments, Cincinnati, Ohio 6. Chicago Area Transportation Study, Chicago, Illinois 7. CALTRANS, California 8. Maryland Department of Transportation 9. Connecticut Department of Transportation 10. Delaware Department of Transportation 11. Arizona Department of Transportation 12. Virginia Department of Highways and Transportation 13. San Diego Association of Governments 14. Virginia Highway and Transportation Research Council 15. Maryland National Capital Park and Planning Commission 16. District of Columbia Department of Public Works 17. Fairfax County, Virginia 18. Prince George's County, Maryland 19. Anne Arundel County., Maryland 20. Baltimore Regional Planning Commission A-1 21. Transportation Research Board 22. Montgomery County, Maryland 23. Delaware Valley Regional Planning Commission 24. Metropolitan Government of Nashville and Davidson County, Tennessee 25. City of Pittsburgh, Pennsylvania A-2 APPENDIX B TRIP RATE ANALYSIS METHODOLOGY DATA SOURCES An extensive literature review was performed to identify relevant trip generation (rate) data. This literature review included direct contacts with state and local agencies, a TRIS computerized reference search, and review of U.S. Department of Transportation and University of Maryland library information. Initially, an extensive list of references pertaining to trip rates was developed. In addition, the TRIS computerized search revealed 497 references. The reference list was then screened for relevancy to this study and a selected number of references were obtained and reviewed. In addition to the library search, a number of state and local agencies responsible for transportation planning were contacted. The agencies contacted are shown in Appendix A. Several of these agencies were able to provide reports or data summaries pertaining to trip generation rates. The data sources included data from home interview survey's as well as driveway counts collected within the last five years and represent the current socioeconomic conditions and the post 1973 energy crisis travel behavior.' The data sources on driveway counts included land uses such as single family and multi-family residential; high-technology, townhouse and general office buildings; industrial plants; shopping centers of different sizes; hotels; hospitals and clinics; fast food restaurants; and miscellaneous services such as banks, beauty salons, dry cleaners, and printing shops. Some sources include data for peak hours and daily trips, others include data for peak hours or daily trips. In most of the cases,, the location of the site within the Standard Metropolitan Statistical Area (SMSA) was identified. The home interview surveys provide travel data for all members of the household for a given day. For each trip made, travel data collected through home interviews generally include: type of vehicle, trip origin and destination vehicle occupancy, trip purpose and time of trip origin. In addition to the travel data, household data, such as location, number of persons in the household, number of licensed drivers, household income range, number of vehicles and type of housing structure are also collected. In this study, the home interview data was used to study the impact of residential characteristics on trip generation rates. For residential uses, location data as well as residential characteristics (household size, vehicle ownership and density) data were available for several samples located in the suburban B-1 areas. However, residential characteristics data for other locations (urban and rural) were generally not available. other residential characteristics, such as rent, value of dwelling, and number of workers in the household were generally not available in the ground count data base. These characteristics were available from home interview surveys such as from Detroit and Baltimore. Efforts to correlate ground count data from these cities to the home interview survey were not successful due to lack of ground count data for the matching locations. METHODOLOGY Trip rate analyses were also conducted on the following: 1) determining the effects of older data in the ITE data base; 2) residential trip rate analysis as a function of residential characteristics. Effects of Older Data Much of the data for the existing trip generation rates including the ITE data base date back to 1960. over the period of twenty years from 1960 to 1980 several changes have occurred that may have changed the vehicle trip rates. In 1973-74 a serious energy crisis occurred. This crisis resulted in severe shortfalls in gasoline as well as significant increases in gasoline prices. During the energy crisis period, the increase in transportation costs and the energy constraints resulted in a reduction in vehicular travel and changes in travel patterns such as increased ridesharing and trip chaining. The ITE data base, augmented by the data collected in this study, was used to determine the effects of older data. The data base was split into two groups: (1) pre-1973; and (2) post-1973 based on the assumption that the 1973 energy crisis was the major reason for the changes in travel behavior and the associated changes in trip rates. Based on data availability, and the frequency of use of data, the following land uses were analyzed: - Industrial/manufacturing - general light industrial, heavy industrial, industrial park, manufacturing and warehousing. - Residential - single family, apartment (low-rise and high-rise) and planned unit developments. - Hotel - Hospital - Offices - general, medical office building, office park and research center. B-2 - Shopping Center - regional, community neighborhood and central area, and quality restaurant. - Drive-in bank Trip generation rates (simple arithmetic means) for pre-1973 and post- 1973 groups were estimated using the Statistical Package for Social Sciences (SPSS) Version 9. The mean trip rates from the two groups were compared to determine significant .differences using student "T" test and "F" ratios. Analysis of Variance (ANOVA) was used to test significance of differences between group means. If the group means are not significantly different, then the pre-1973 data would be usable under today's conditions. If the test fails - that is, if the means are found to be significantly different, then the pre-1973 data would not represent the current travel behavior. Table B-1 presents the results of the tests of significant differences between the pre-1973 and post-1973 data for selected land uses. For each land user the number of cases, mean daily trip rate and standard deviation are presented for the pre-1973 and post-1973 conditions. It should be noted that the mean trip rates are simple arithmetic means. The student "T" values, as computed, are presented along with the values from "T" tables for the 5 percent and 1 percent level of significance on the basis of a two-tailed test. It should be noted that for some land uses, sample sizes were not sufficient to perform meaningful T-tests. In all cases? based on the T-tests, it can be concluded that there is not significant difference between the two means at a 1 percent level of significance. On the basis of a two-tailed test at a 5 percent level of significance, the mean trip rates for all land uses, except apartments are not significantly different. For the apartments, since the differences between means are significant at the 5 percent level but not at the 1 percent level, it can be concluded that the means are probably different. For some land uses, such as industrial parks and hospitals, the mean trip rates for the post-1973 data were intuitively different than the pre-1973 data. In these cases, the F-value was computed and compared with the tabular values for the F-distribution. A 5 percent significance level was selected for comparison with the computed values. If the computed F is larger than the value reported in the F table, the null hypothesis that the means are equal can be rejected. if it is smaller, the null hypothesis cannot be rejected. In all cases analyzed, the computed F value was smaller than the table value. The null hypothesis that the means are equal cannot be rejected. This analysis indicated that the trip rate means between the pre-1973 and post-1973 period were not significantly different. B-3 TABLE B-1 TEST OF SIGNIFICANT DIFFERENCES BETWEEN PRE-1973 AND POST-1973 DATA Click HERE for graphic. NOTE: NA - Not Applicable B-4 TABLE B-2 TEST OF SIGNIFICANT DIFFERENCES BY LOCATION BETWEEN PRE-1973 AND POST-1973 DATA Click HERE for graphic. NOTE: NA - Not Applicable B-5 Further tests were conducted for some land uses to verify the differences between the means based on location of the land use within an SMSA. The results are presented in Table B-2 in a similar format as Table B-1. T-tests and F-tests were conducted for each of the land uses listed. The results showed that the mean trip rates for the pre-1973 and post-1973 time periods were not significantly different by location within an SMSA. These analyses showed that the mean-trip rates for the older data (pre- 1973) were not significantly different than the newer data (post-1973). Therefore, it was decided to include the pre 1973 data in the updating of the trip generation rates. Residential Analysis The impact of residential characteristics on trip generation rates was estimated using the updated ITE data base, as well as the home interview survey data. The ITE data base includes residential characteristics such as household size, household income, residential density, vehicle ownership and location. A cross-classification analysis of the variables was carried out to determine the sample sizes in each cell. This analysis found that an insufficient number of observations existed to study the income data, as well as all locations other than suburban areas. Accordingly, a multiple regression analysis was conducted with daily vehicle trips per dwelling unit as the dependent variable and household size, vehicle ownership and residential density as the independent variables. Three residential land uses were analyzed: single family detached, apartments and condominiums. The results of the analyses are presented in Table B-3, along with associated statistics. As noted in the table, the correlation coefficients are greater than 0.9 in all cases. These regression relationships were utilized in developing trip rate adjustment factors for the three residential land uses. In addition to the regression analysis, cross-classification of trip rate data from other sources was carried out. The Arizona Department of Transportation conducted a study on the value of dwellings as a residential characteristic of trip rates (2). The trip rate summary for three areas in the U.S. (Delaware, Wisconsin and Ohio) are presented in Table B-4. The weighted average trip rate for low value single family dwelling (less than $250,000 in 1976 dollars) is 9.96. The trip rates increase as the market value of the dwelling increases (11.09 for market values between $25,000 and $50,000 and 14.72 for market value over $50,000). A two-tailed T-test test indicated that the means were significantly different at the 5 percent level of significance. B-6 TABLE B-3 MULTIPLE REGRESSION ANALYSIS STATISTICS FOR RESIDENTIAL USES SINGLE FAMILY RESIDENTIAL DETACHED Multiple R .98087 Analysis of Variance R Square .96210 DF Sum of Squares Mean Square Adjusted R Square .96027 Regression 3 5577.62697 1859.20899 Standard Error 1.88251 Residual 62 219.71817 3.54384 F = 524.63098 Signif F = .0000 VARIABLES IN THE EQUATION Dependent Variable - Trip Rate Per Dwelling Unit Variable B SE B BETA T SIG T Household Size 1.55 .35206 .55420 4.043.0001 Vehicle Ownership 2.93 .81953 .43579 3.333.0015 Density -0.14 .08642 -.00600 -.149.8820 APARTMENTS Multiple R .96831 Analysis of Variance R Square .93762 DF Sum of Squares Mean Square Adjusted R Square .93337 Regression 3 1929.41209 643.13736 Standard Error 1.70797 Residual 44 128.35441 2.91715 F = 220.46803 Signif F = .0000 VARIABLES IN THE EQUATION Dependent Variable - Trip Rate Per Dwelling Unit Variable B SE B BETA T SIG T Household Size 1.93 .68199 .64271 2.959.0050 Density 0.03 .02292 .09882 1.207.2339 Vehicle Ownership 1.10 1.16138 .24229 .991 .3269 CONDOMINIUMS Multiple R .96166 Analysis of Variance R. Square .92478 DF Sum of Squares Mean Square Adjusted R Square .91762 Regression 2 848.74140 424.37070 Standard Error 1.81308 Residual 21 69.03272 3.28727 F = 129.09509 Signif F = .0000 VARIABLES IN THE EQUATION Dependent Variable - Trip Rate Per Dwelling Unit Variable B SE B BETA T SIG T Household size 3.86 .68371 .92105 5.643.0000 Vehicle ownership 0.13 .47232 .04350 .267 .7924 B-7 TABLE B-4 TRIP RATES AS A FUNCTION OF MARKET VALUES OF SINGLE FAMILY DETACHED RESIDENTIAL DWELLINGS Medium Value(1) High Value Low Value(1) (Mkt. Value between, (Mkt. Value > (Mkt. Value < $25,000) $25,000 & $50,000) $50,000) Trip No. of Trip No. of Trip No. of Rate Dwellings Rate Dwellings Rate Dwellings Delaware DOT 10.9 1700 11.6 770 13.8 304 Wisconsin DOT 8.5 1148 11.3 1198 16.0 256 Ohio Section ITE 10.1 506 10.2 715 14.3 12 Weighted Average 9.96 3354 11.09 2683 14.72 6,112 Computed Mean Trip Rate 9.96 11.09 14.72 Standard Deviation 2.15 2.31 3.05 No. of Studies 29 39 8 ___________________________ (1) In 1976 dollars B-8 The ITE data base does not include "value of dwelling unit". Therefore, a direct correlation between the data presented in Table B-4 and the ITE data base cannot be made. However, the trip rates can be used for correlating the two sources. The ITE data base correlates well with the low market value database. This would result in adjustment factors of 1.10 and 1.50 for medium value and high value dwelling units to be applied to the Table 3 trip rates of 10.03 trips per dwelling unit. It should be noted that the market values of dwelling units vary for locations within the region as well as by geographic areas of the country. Therefore, this adjustment factor should be applied based on low, medium and high values for the particular region rather than the dollar value. Home interview trip rate survey data from Detroit, Baltimore and California were categorized by variables such as household size, vehicle ownership, location, and income (12, 31, 79). In the case of Detroit and Baltimore, no comparable data from the augmented ITE data file could be found to correlate the home interview surveys with the driveway counts. Trip rates based on driveway or ground counts data are more applicable to site specific studies including determination of roadway/intersection improvement needs. For regional or areawide studies, trip rates based on home interview (origin-destination) surveys are more applicable. The California DOT survey included summaries of vehicle trip rates by household size as well as vehicle ownership for single family and multi-family dwelling units. T-tests were conducted to determine the significance of difference of mean trip rates as a function of household size. The mean trip rates by household size were found to be significantly different for single family dwelling units but not for multifamily dwelling units. B-9 APPENDIX C RECOMMENDATIONS FOR FURTHER RESEARCH The ITE Permanent Trip Generation Committee (6A-32) has collected extensive trip data since 1972. In those cases where the sources of data are known? it is recommended that the Committee request the sources to provide data on trip location of the sites within the SMSA. In addition new data collection efforts are recommended, primarily in urban areas, to develop trip rates by location for the appropriate land uses in Table 3. Another area needing additional ground counts relates to the residential uses in Baltimore and Detroit. As discussed in Appendix B, home interview data from these two sources are available, however, adequate ground count data are not available for correlation of the two sources. The potential impact of new multi-use developments and planned unit developments on the adjacent roadway network was previously discussed on the basis of only two studies (70, 92). More research is needed in this area to validate/refine the study results. Information on capture rate of pass-by traffic is also very weak. More research is needed to identify the percentage of trips captured by a site from passing traffic as well as the traffic diverted from another route to the new development. The research on multi-use centers and capture rates from pass-by traffic would require origin-destination survey questionnaires of patrons visiting the potential site uses (shopping centers, restaurants banks, service stations, convenience markets and multi-use centers). Certain land uses such as shopping centers, restaurants and banks exhibit significant daily and seasonal variations in trip rates. For many of these uses Friday trips are greater than the average weekday trips. Shopping centers exhibit seasonal peaks with January/February the lower seasonal months and November/December the peak seasonal months. In some cases, Fridays or seasonal trip rates should be used as the design or analysis period rather than the average weekday. More research is needed to develop this information. U.S. Government Printing Office: 1985-461-816/20506 C-1
