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This document provides a technical definition of accessibility measurement as implemented with gravity models in urban travel forecasting models. It explains how zonal accessibility measures are used with gravity models to estimate impacts of transportation projects on trip distances and the spatial distribution of trips in a metropolitan area. 7) Handy, Susan. 1994. "Regional Versus Local Accessibility: Implications for Nonwork Travel." Transportation Research Record 1400. Washington, DC: Transportation Research Board, National Research Council, pp. 5866. This article shows the correlation between automobile-oriented transportation development and subsequent changes in patterns of accessibility to retail and service activity within metropolitan areas. It demonstrates how alternative land use and transportation patterns can affect trip distances, and it shows how local access and broader regional access can be affected differently. 8) Landis, Bruce. 1996. "Bicycle System Performance Measure." ITE Journal, Vol. 66, No. 2 (February), pp. 1826. This article describes relatively easy-to-use techniques for estimating potential bicycle travel demand (the Latent Demand Score) and evaluating roadway conditions for cycling in a particular area (the Interaction Hazard Score). These approaches are similar to other models used by traffic engineers that require demographic, geographic, and road condition information. 9) Schwartz, W.L., C.D. Porter, G.C. Payne, J.H. Suhrbier, P.C. Moe, and W.L. Wilkinson III. 1999. Guidebook on Methods to Estimate Non-Motorized Travel: Overview of Methods. Turner-Fairbanks Highway Research Center. FHWA-RD-98-166. Washington, DC: Federal Highway Administration. This guidebook describes and compares various techniques that can be used to forecast non- motorized travel demand and to evaluate and prioritize nonmotorized projects. It provides an overview of each method, including pros and cons, ease of use, data requirements, sensitivity to design factors, typical applications, and whether it is widely used. REFERENCES Los Alamos National Laboratory and Price Waterhouse Coopers. 2002. "TRANSIMS-3.0 Documents." U.S. Department of Transportation. Vol.3. Available at http://transims. tsasa.lanl.gov/. National Highway Institute. 1996. Pedestrian and Bicyclist Safety and Accommodation; Participants Handbook. National Highway Institute Course No. 38061. Washington, DC: Federal Highway Administration. RDC, Inc. 1995. "Activity-Based Modeling System for Travel Demand Forecasting: Travel Model Improvement Program." Washington, DC: U.S. Department of Transportation/U.S. Environmental Protection Agency. Available at http://tmip.fhwa.dot.gov/clearinghouse/ docs/amos/. 197

OCR for page 192
Richardson, A. 2001. "Never Mind the Data Feel the Model." Paper presented at the International Conference on Transport Survey Quality, Kruger National Park, South Africa. Available at http://www.tuti.com.au/PUBLICATIONS/2001/2001Kruger.pdf. Rietveld, P. 2000. "Nonmotorized Modes in Transport Systems: A Multimodal Chain Perspective for the Netherlands." Transportation Research, Vol. 5D, No. 1 (January), pp. 3136. Public Roads. 2000. "TRANSIMS Computer Software Improves Transportation Decisions." Public Roads. (November). Online Publication available at http://www.tfhrc.gov/pubrds/ nov00/along.htm. U.S. General Accounting Office (GAO). 2000. Highway Infrastructure: FHWA's Model for Estimating Highway Needs Is Generally Reasonable, Despite Limitations. Report to Congressional Committees. GAO/RCED-00-133 Washington, DC: GAO (June). U.S. General Accounting Office (GAO). 2001. Highway Infrastructure: FWHA's Model for Estimating Highway Needs Has Been Modified for State-Level Planning. Report to Congressional Committees. GAO-01-299, February. Available at http://www.gao.gov/ new.items/d01299.pdf. 198