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40 1. Kimley-Horn and Associates, Inc. Trip-Generation Rates for Urban Infill Land Uses in California, Phase 2: Data Collection, Final Report. California Department of Transportation. June 2009. 2. Arrington, G. and Cervero, R. TCRP Report 128: Effects of TOD on Housing, Parking, and Travel. Transportation Research Board of the National Academies, Washington, D.C., 2008. This research study collected vehicle trip data at 18 residential TODs in major metropolitan areas of the United States, including five residential apartment developments in the Washington, D.C., area. Based on daily trip generation, the research study presented a com- parison between empirical data and estimates using baseline ITE trip generation rates. The unweighted average percent difference between actual and ITE rate data was about 47%, with a range of 30% to 92%. 3. Institute of Transportation Engineers. Trip Generation Manual, 9th Edition, Volume 1: Userâs Guide and Handbook. Institute of Transportation Engineers. Washington, D.C., 2012. 4. Bochner, B., Chair, Subcommittee on Chapter 11, Trip Generation for Urban Infill/Redevelopment, for the third edition update to the Trip Generation Handbook. Institute of Transportation Engineers, Washington, D.C., 2012. 5. Institute of Transportation Engineers. Trip Generation Handbook: An ITE Recommended Practice. 2nd Edition. Washington, D.C.: ITE, 2008. 6. TRICS is a comprehensive database considered the national stan- dard for trip generation data and analysis methods in the United Kingdom and Ireland. It is used in their transport assessment processâa process similar to traffic impact analyses in the United States. The database is composed of studies of individual land use sites, similar to ITEâs Trip Generation Manual, but provides exten- sive details on the physical, travel, and contextual characteristics of each site. TRICS uses an interactive database allowing users to add data, view data for sites, and combine site data in order to conduct impact analyses. 7. Fehr & Peers Associates, Inc. Trip Generation for Smart Growth, San Diego Association of Governments, 2010. 8. Clifton, K. J., Currans, K. M., and Muhs, C. D. Contextual Influences on Trip Generation. Portland, OR; Oregon Transportation Research and Education Consortium Research Report (OTREC-RR-12-13), November 2012. 9. Shafizadeh, K., Schneider, R., and Handy, S. Methodology for Adjust- ing ITE Trip Generation Estimates for Smart Growth Projects, Out- line for Submission to ITE. California State University, Sacramento, Department of Civil Engineering, University of California, Davis, Institute for Transportation Studies, University of California, Davis, Department of Environmental Science and Policy. August 31, 2012. Handy, S., Shafizadeh, K., and Schneider, R. California Smart- Growth Trip Generation Rates Study, University of California, Davis for the California Department of Transportation, Draft, February 2013, pp. 1, 7â9, Appendix F. 10. The research team assessed data from the 2000 San Francisco Bay Area Travel Survey (Regional Travel Characteristics Report, Vol- ume I, Metropolitan Transportation Commission, August 2004) comparing person trips per household by context type (see Table 3.12.1C, 2000 Regional Weekday Trips per Household by Popula- tion Density Category). Five context types were defined by pop- ulation density and categorized as urban core, urban, suburban, rural-suburban, and rural. The research team conducted a statisti- cal analysis on the person-trip values and found the variation (all values, except one, fell within one standard deviation of the mean, while one fell within two standard deviations of the mean) to be statistically insignificant. 11. Daisa, James M., et al. Designing Walkable Urban Thoroughfares: A Context Sensitive Approach (An ITE Recommended Practice). Institute of Transportation Engineers, Washington, D.C., 2010. 12. For purposes of qualifying a context for infill development, it must be served by rail transit or high-frequency bus transit. (a) High-frequency bus transit is defined as service with a maxi- mum headway of 15 min for a minimum of 6 hours/day. This includes services commonly referred to as ârapid transitâ and âbus rapid transit.â A corridor served by multiple bus lines that serve the same corridor origins and destinations can meet the high-frequency definition if the collective headways of the lines equal a maximum of 15 min. (b) Rail transit is defined as a network of rail lines providing pas- sengers access to a greater geographic coverage of the region and the city being studied than a single line. Transfers from one line to another may be necessary, but transfers occur at stations requiring minimal deviation. Rail transit may also be defined as a single rail line serving one corridor. This type of rail service is typically termed âcommuter rail,â and it connects a city center with multiple suburban centers. Lines typically have one or two stops in each city being served. Systems generally attract more riders than lines do. 13. The MTC provides GIS files that include a layer file containing geo- coded bus stops and an attribute file with information (including Notes and Citations
41 headways) for each route serving the stop. The bus route data were from the year 2000, consistent with the 2000 BATS data. 14. Institute of Transportation Engineers. Transportation Impact Analy- ses for Site Development: An ITE Recommended Practice. Chapter 5: Site Traffic Generation. Washington, D.C., 2010. 15. Development sites containing the same land uses and located in very similar contexts can have different peak hours of trip genera- tion. Data collection for the purposes of validation needs to extend for a 2-hour period, or preferably a 3-hour period, during the tra- ditional morning and afternoon commute peaks at both the valida- tion site and the proxy site (if using the proxy site method). In this manner, a site with early peaking characteristics can be matched with a site having late peaking characteristics. 16. Association of Bay Area Governments, Kimley-Horn and Associates, Inc., and Economic & Planning Systems. Trip-Generation Rates for Urban Infill Land Uses in California: Phase 1: Data Collection Meth- odology and Pilot Application â Final Report. Sacramento, California: California Department of Transportation, 2008, and Kimley-Horn and Associates, Inc. Trip-Generation Rates for Urban Infill Land Uses in California, Phase 2: Data Collection, Final Report. Sacramento, California: California Department of Transportation, June 2009. 17. Arrington, G. and Cervero, R. TCRP Report 128: Effects of TOD on Housing, Parking, and Travel, Transportation Research Board of the National Academies, Washington, D.C., 2008. 18. Step 4, converting infill person auto trips to infill vehicle trips using Equation #3: Vehicle-TripsINFILL = Person-Vehicle-TripsINFILL/ VehOccINFILL= Persons per vehicle based on local data (or default value). See Draft Report: NCHRP Project 8-66, Trip Generation Rates for Transportation Impact Analyses of Infill Developments, Draft Revised Phase 1 Methodology, July 15, 2010. 19. As described in endnote 10, the research team determined the vari- ation in person trips between different contexts statistically insig- nificant. The team based this conclusion on data from the 2000 San Francisco Bay Area Travel Survey (Regional Travel Character- istics Report, Volume I, Metropolitan Transportation Commission, August 2004) comparing person trips per household by context type (see Table 3.12.1C, 2000 Regional Weekday Trips per House- hold by Population Density Category).