U.S. Department of Transportation
Federal Highway Administration
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Abdelghany, A., Adbelghany, K., Mahmassani, H.S., and Al-Zharani, A. (2012). Dynamic Simulation Assignment Model for Pedestrian Movements in Crowded Networks. Transportation Research Record 2316. pp. 95–105.
Alliance for Biking & Walking (2016). Bicycling and Walking in the United States: 2016 Benchmarking Report.
Aoun, A., Bjornstad, J., DuBose, B., Mitman, M., Pelon, M., and Fehr & Peers. (2015). Bicycle and Pedestrian Forecasting Tools: State of the Practice. DTFHGI-11-H-00024. Federal Highway Administration, Washington, DC.
Cai, Yiming, Li, Xiao, T. Fabusuyi, Molnar, L. & Hampshire, R.C. (2018). A Statewide Pedestrian Crash Risk Assessment for Michigan: An application of the Model of Pedestrian Demand (MoPED). Transportation Research Board Annual Meeting.
Cambridge Systematics, Inc. and CH2M Hill, Inc. (December 2016). Systemic Safety Project Selection Tool Supplemental Case Studies. Report FHWA-SA-17-002.
Castiglione, J., Bradley, M. and John G. (2014). Activity-Based Travel Demand Models: A Primer. Strategic Highway Research Program (SHRP2) Report S2-C46-RR-1. Transportation Research Board, Washington, DC.
Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016). Representing pedestrian activity in travel demand models: Framework and application. Journal of transport geography, 52, 111-122.
Clifton, K. J., Singleton, P. A., Muhs, C. D., & Schneider, R. J. (2016). Development of destination choice models for pedestrian travel. Transportation Research Part A: Policy and Practice, 94, 255-265.
Clifton, K.J., Burnier, C.V., Schneider, R., Huang, S., Kang, M.W. (2008). Pedestrian Demand Model for Evaluating Pedestrian Risk Exposure. Report prepared for the Maryland State Highway Administration, Office of Traffic and Safety.
Clifton, K.J., Davies, G., Allen, W.G., and Radford, N. (2004). Pedestrian Flow Modeling for Prototypical Maryland Cities. Technical report prepared for the Maryland Department of Transportation Division of Highway Safety Programs Hanover, MD, November 2004.
Clifton, K.J., Singleton, P., Muhs, C.D., Schneider, R.J., Lagerwey P. (2013). Improving the Representation of the Pedestrian Environment in Travel Demand Models – Phase I. Technical report prepared by the Oregon Transportation Research and Education Consortium.
DKS Associates, University of California, Irvine, University of California Santa Barbara, and Utah State University. (2007). Assessment of Local Models and Tools for Analyzing Smart-Growth Strategies. Final Report, Prepared for the California Department of Transportation.
Fagnant, D. J., and Kockelman, K. (2016). A Direct-Demand Model for Bicycle Counts: The Impacts of Level of Service and Other Factors. Environment and Planning B: Planning and Design, Vol. 43, No. 1, pp. 93–107.
Federal Highway Administration. (2016). FHWA New Non-Motorized Travel Analysis Toolkit. Available at http://catsip.berkeley.edu/news/fhwa-new-non-motorized-travel-analysis-toolkit.
Fitzpatrick, K., Park, E.S. (July 2010). Safety Effectiveness of the HAWK Pedestrian Crossing Treatment. Report FHWA-HRT-10-042.
Greene-Roesel, R., Diogenes, M.C., Ragland, D.R. (2007). Estimating Pedestrian Accident Exposure: Protocol Report. (In Estimating Pedestrian Accident Exposure: Final Report, California PATH Research Report UCB-ITS-PRR-2010-32, Final Report for Task Orders 5211/6211).
Hankey, S., and Lindsey, G. (2016). Facility-Demand Models of Peak Period Pedestrian and Bicycle Traffic: Comparison of Fully Specified and Reduced-Form Models. Transportation Research Record, No. 2586, pp. 48–58.
Hankey, S., Lindsey, G., Wang, X., Borah, J., Hoff, K., Utecht, B., and Xu, Z. (2012). Estimating Use of Nonmotorized Infrastructure: Models of Bicycle and Pedestrian Traffic in Minneapolis, MN. Landscape and Urban Planning, Vol. 107, No. 3, pp. 307–316.
Hankey, S., Lu, T., Mondschein, A., and Buehler, R. (2017). Merging Traffic Monitoring and Direct-Demand Modeling to Assess Spatial Patterns of Annual Average Daily Bicycle and Pedestrian Traffic. Transportation Research Board 2017 Annual Meeting.
Hong, J., Shankar, V.N. and Venkataraman, N. (2016). A Spatially Autoregressive and Heteroskedastic Space-time Pedestrian Exposure Modeling Framework with Spatial Lags and Endogenous Network Topologies. Analytic Methods in Accident Research. Vol. 10, pp. 26–46.
Hood, J. Sall, E., Charlton, B. (2011). A GPS-based Bicycle Route Choice Model for San Francisco, California. Transportation Letters: International Journal of Transportation Research. Vol. 3, No. 1., pp. 63-75.
Kononov, J., & Allery, B. (2003). Level of service of safety: Conceptual blueprint and analytical framework. Transportation Research Record: Journal of the Transportation Research Board, (1840), 57-66.
Kononov, J., Durso, C., Lyon, C., & Allery, B. (2015). Level of service of safety revisited. Transportation Research Record: Journal of the Transportation Research Board, (2514), 10-20.
Kuzmyak, J. R., Walters, J., Bradley, M., and Kockelman, K.M. (2014). Estimating Bicycling and Walking for Planning and Project Development: A Guidebook. NCHRP 770. Transportation Research Board, Washington, DC.
Lagerwey, P., Hintze, M., Elliott, J., Toole, J., and Schneider, R. (2015). NCHRP Report 803: Pedestrian and Bicycle Transportation Along Existing Roads—ActiveTrans Priority Tool Guidebook.
Lassarre, S., Papadimitriou, E., Yannis, G. and Golias, J. (2007). Measuring Accident Risk Exposure for Pedestrians in Different Micro-Environments. Accident Analysis and Prevention. Vol. 39, No. 6, pp. 1226–1238.
Lee, K. and Sener, I.N. (2017). Emerging Data Mining for Pedestrian and Bicyclist Monitoring: A Literature Review Report. Technical Report, prepared for the Safety through Disruption (Safe-D) National University Transportation Center. Texas A&M Transportation Institute, September 2017.
McDaniel, S., Lowry, M. and Dixon, M. (2014). Using Origin-Destination Centrality to Estimate Directional Bicycle Volumes. Transportation Research Record 2430. pp. 12–19.
Molino, J.A., Kennedy, J.F., Inge, P.J., Bertola, M. A., Beuse, P A., Fowler, N.L., Emo, A.K., Do, A. (2012). A Distance-Based Method to Estimate Annual Pedestrian and Bicyclist Exposure in an Urban Environment. Report No. FHWA-HRT-11-043.
Molino, J.A., Kennedy, J.F., Johnson, P.L., Beuse, P.A., Emo, A.K., Do, A. (2009). Pedestrian and Bicyclist Exposure to Risk: Methodology for Estimation in an Urban Environment. Transportation Research Record 2140. pp. 145-156.
Munira, S. and Sener, I.N. (2017). Use of the Direct-Demand Modeling in Estimating Nonmotorized Activity: A Meta-Analysis. Technical Report, prepared for the Safety through Disruption (Safe-D) National University Transportation Center. Texas A&M Transportation Institute, October 2017.
National Highway Traffic Safety Administration. (February 2017). Traffic Safety Facts: 2015 Data, Pedestrians. NHTSA Report DOT-HS-812-375.
Papadimitriou, E., Yannis, G., and Golias, J. (2012). Analysis of Pedestrian Exposure to Risk in Relation to Crossing Behavior. Transportation Research Record 2299. pp. 79–90.
Proulx, F. and Pozdnoukhov, A. (2017). Bicycle Traffic Volume Estimation using Geographically Weighted Data Fusion. Technical Paper.
Radwan, E., Abou-Senna, H., Mohamed, A., Navarro, A., Minaei, N., Wu, J., and Gonzalez, L. (2016). Assessment of Sidewalk/Bicycle-Lane Gaps with Safety and Developing Statewide Pedestrian Crash Rates. Prepared for the Florida Department of Transportation. Final Report, Contract No. BDV24-977-07.
Raford, N. and Ragland, D. (2004). Space Syntax: Innovative Pedestrian Volume Modeling Tool for Pedestrian Safety. Transportation Research Record 1878. pp. 66-74.
Raford, N. and Ragland, D.R. (2006). Pedestrian Volume Modeling for Traffic Safety and Exposure Analysis: Case of Boston, Massachusetts. TRB 2006 Annual Meeting Compendium of Papers.
Salon, D. (2016). Estimating Pedestrian and Cyclist Activity at the Neighborhood Scale. Journal of Transport Geography. Vol. 55, pp. 11–21.
Schmiedeskamp, P. and Zhao, W., (2016). Estimating Daily Bicycle Counts in Seattle, Washington, from Seasonal and Weather Factors. Transportation Research Record 2593. pp. 94–102.
Schneider, R. and Stefanich, J. (2015). Wisconsin Pedestrian and Bicycle Crash Analysis: 2011-2013. Prepared for the Wisconsin Department of Transportation (WisDOT), Bureau of Transportation Safety (BOTS). Final Draft.
Schneider, R.J., Henry, T., Mitman, M.F., Stonehill, L., Koehler, J. (2012). Development and Application of a Pedestrian Volume Model in San Francisco, California. Transportation Research Record 2299. pp. 65–78.
Seattle DOT. (September 2016). City of Seattle Bicycle and Pedestrian Safety Analysis.
Sener, I.N., Eluru, N. and Bhat, C.R. (2009). An Analysis of Bicycle Route Choice Preferences in Texas, U.S. Transportation, 36(5): 511-539.
Sener, I.N., Ferdous, N., Bhat, C.R., and Reeder, P.R. (2009). Tour-based Model Development for TxDOT: Evaluation and Transition Steps. Technical Report, prepared for the Texas Department of Transportation (TxDOT), October 2009.
Southern California Association of Governments (SCAG) (2016). SCAG Regional Travel Demand Model and 2012 Model Validation. March 2016.
Strauss, J., Miranda-Moreno, L.F., and Morency, P. (2013). Cyclist Activity and Injury Risk Analysis at Signalized Intersections: A Bayesian modelling approach. Accident Analysis and Prevention. Vol. 59, pp. 9–17.
Strauss, J., Miranda-Moreno, L.F., and Morency, P. (2014). Multimodal Injury Risk Analysis of Road Users at Signalized and Non-Signalized Intersections. Accident Analysis and Prevention. Vol. 71, pp. 201–209.
Tabeshian, M., and Kattan, L. (2014). Modeling Nonmotorized Travel Demand at Intersections in Calgary, Canada: Use of Traffic Counts and Geographic. Transportation Research Record, No. 2430, pp. 38–46.
Thomas, L., Lan, B., Sanders, R.L., Frackelton, A., Gardner, S., Hintze, M. (2017). Changing the Future? Development and Application of Pedestrian Safety Performance Functions to Prioritize Locations in Seattle, WA. TRB 2018 Annual Meeting Compendium of Papers.
Travel Forecasting Resources. http://tfresource.org/Travel_Forecasting_Resource.
Turner, S., Sener, I., Martin, M., Das, S., Shipp, E., Hampshire R., Fitzpatrick, K., Molnar, L., Wijesundera, R., Colety, M., Robinson, S. (January 2017). Synthesis of Methods for Estimating Pedestrian and Bicyclist Exposure to Risk at Areawide Levels and on Specific Transportation Facilities. Report FHWA-SA-17-041.
Wang, J., Lindsey, G., and Hankey, S. (2016). Exposure to Risk and the Built Environment, an Empirical Study of Bicycle Crashes in Minneapolis. TRB 2017 Annual Meeting Compendium of Papers.
Winters, M., Brauer, M., Setton, E. M., and Teschke, K. (2010). Built Environment Influences on Healthy Transportation Choices: Bicycling Versus Driving. Journal of Urban health, Vol. 87, No. 6, pp. 969-993.
Zimmermann, M., Mai, T., and Frejinger, E. (2017). Bike Route Choice Modeling Using GPS Data without Choice Sets of Paths. Transportation Research Part C: Emerging Technologies, Vol. 75, pp. 183–196.
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