Micro-Simulation Model for Assessing the Risk of Vehicle–Pedestrian Road Accidents

REFERENCES

• Atchley, P., & Dressel, J. (2004). Conversation limits the functional field of view. Human Factors: The Journal of the Human Factors and Ergonomics Society, 19, 664–673.
   Web of Science ®Google Scholar
• Balmer, M., Nagel, K.A. I., & Raney, B. (2004) Large-scale multi-agent simulations for transportation applications. Journal of Intelligent Transportation Systems, 8(1), 205–221.
   Google Scholar
• Benenson, I., Aronovich, S., & Noam, S. (2005). Let's talk objects: Generic methodology for urban high-resolution simulation. Computers, Environment and Urban System, 29, 425–453.
   Google Scholar
• Benenson, I., & Torrens, P.M. (2004). Geosimulation: Automata-based modeling of urban phenomena. London, UK: Wiley.
   Google Scholar
• Campbell, B.N., Smith, J.D., & Najm, W.G. (2003). Examination of crash contributing factors using national crash databases. Technical report DOT-HS 809 664. Washington, DC: National Highway Traffic Safety Administration, U.S. Department of Transportation.
   Google Scholar
• Chang, D. (2008). National pedestrian crash report. Technical report DOT HS 810 968. Washington, DC: National Highway Traffic Safety Administration, U.S. Department of Transportation.
   Google Scholar
• Doniec, A., Mandiau, R., Piechowiak, S., & Espie, S. (2008). A behavioral multi-agent model for road traffic simulation. Engineering Applications of Artificial Intelligence, 21, 1443–1454.
   Web of Science ®Google Scholar
• El-Tantawy, S., Abdulhai, B., & Abdelgawad, H. (2014). Design of reinforcement learning parameters for seamless application of adaptive traffic signal control. Journal of Intelligent Transportation Systems, 18, 227–245.
   Web of Science ®Google Scholar
• Fiorini, P., & Shiller, Z. (1998). Motion planning in dynamic environments using velocity obstacles. International Journal of Robotics Research, 17(7), 760–772.
   Web of Science ®Google Scholar
• Gettman D., & Head, L. (2003). Surrogate safety measures from traffic simulation models, Final report. Technical report FHWA-RD-03-050. Washington, DC: Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Gitelman, V., Balasha D., Carmel, R., Hendel, L., & Pesahov, F. (2012). Characterization of pedestrian accidents and an examination of infrastructure measures to improve pedestrian safety in Israel. Accident Analysis and Prevention, 44(1), 63–73.
   PubMed Web of Science ®Google Scholar
• Green, M. (2000). How long does it take to stop?—Methodological analysis of driver perception-brake times. Transportation Human Factors, 2(3), 195–216.
   Google Scholar
• Hakkert A.S., Gitelman, V., & Ben-Shabat, E. (2002). An evaluation of crosswalk warning systems: Effects on pedestrian and vehicle behavior. Transportation Research F, 5, 275–292.
   Google Scholar
• Hoogendoorn, S.P., Daamen, W., & Landman, R. (2005). Microscopic calibration and validation of pedestrian models: Cross-comparison of models using experimental data. In Pedestrian and evacuation dynamics (pp. 253–265). New York, NY: Springer.
   Google Scholar
• Hunter, W.W., Stutts, J.C., & Pein W.E. (1997). Pedestrian crash types: A 1990's informational guide. PublicationFHWA-RD-96-163. Washington, DC: Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Ishaque, M.M., & Noland, R.B. (2008). Behavioural issues in pedestrian speed choice and street crossing behaviour: A review. Transport Reviews, 28(1), 61–85.
   Web of Science ®Google Scholar
• Kiefer, R.J., Cassar, M.T., Flannagan, C.A., LeBlanc, D.J., Palmer, M.D., Deering, R.K., & Shulman, M.A. (2003). Forward collision warning requirements project: Refining the CAMP crash alert timing approach by examining last-second braking and lane change maneuvers under various kinematic conditions. Technical report DOT HS 809 574. Washington, DC: National Highway Traffic Safety Administration, U.S. Department of Transportation.
   Google Scholar
• Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., & Ramsey, D.J. (2006a). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-Car Naturalistic Driving Study data. Technical report DOT HS 810 594. Washington, DC: National Highway Traffic Safety Administration, US Department of Transportation.
   Google Scholar
• Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D., & Ramsey, D.J. (2006b). The 100-Car Naturalistic Driving Study, Phase II—Results of the 100-Car field experiment. Technical report DOT HS 810 593. Washington, DC: National Highway Traffic Safety Administration, US Department of Transportation.
   Google Scholar
• Ksontini, F., Espié, S., Guessoum, Z., & Mandiau, R. (2012). Traffic behavioral simulation in urban and suburban—Representation of the drivers’ environment. In Y. Demazeau, J.-P. Müller, J.M. Corchado Rodriguez, & J.B. Perez (Eds.), Proceedings of the 10th International Conference on Practical Applications of Agents and Multi-Agents Systems, PAAMS 12, Advances in Intelligent and Soft Computing: Highlights on Practical Applications of Agents and Multi-Agent Systems 155 (pp. 115–125). . Springer.
   Google Scholar
• Malta, L., Miyajima, C., & Takeda, K. (2009). A study of driver behavior under potential threats in vehicle traffic. IEEE Transactions on Intelligent Transportation Systems, 10, 201–210.
   Web of Science ®Google Scholar
• Markowitz, F., Sciortino, S., Fleck, J.L., & Yee, B.M. (2006). Pedestrian countdown signals: Experience with an extensive pilot installation. Institute of Transportation Engineers Journal, 76(1), 43–48.
   Web of Science ®Google Scholar
• Miller, G.A. (1956). The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.
   PubMed Web of Science ®Google Scholar
• Muter, P. (1980). Very rapid forgetting. Memory & Cognition, 8, 174–179.
   PubMed Web of Science ®Google Scholar
• Muter, P. (1995). Very rapid forgetting: Reply to Cunningham, Healy, Till, Fendrich, and Dimitry. Memory & Cognition, 23, 383–386.
   PubMed Web of Science ®Google Scholar
• Nagel, K., & Schreckenberg, M. (1992). A cellular automaton model for freeway traffic. Journal de Physique, I(2), 2221–2229.
   Google Scholar
• Najm, W., & Smith, D. (2004). Modeling driver response to lead vehicle decelertaing. doi: 10.4271/2004-01-0171
   Google Scholar
• Papadimitriou, E., Yannis, G., & Golias, J. (2009). A critical assessment of pedestrian behaviour models. Transportation Research F, 12(3), 242–255.
   Google Scholar
• Peterson, L.R., & Peterson, M.J. (1959). Short-term retention of individual verbal items. Journal of Experimental Psychology, 58, 193–198.
   PubMedGoogle Scholar
• Prato, C.G., Gitelman V., & Bekhor, S. (2012). Mapping patterns of pedestrian fatal accidents in Israel. Accident Analysis and Prevention, 44(1), 56–62.
   PubMed Web of Science ®Google Scholar
• Redmon, T. (2011). Evaluating pedestrian safety countermeasures. Public Roads Magazine, 74(5), FHWA-HRT-11-003.
   Google Scholar
• Robin, T., Antonini, G., Bierlaire, M., & Cruz, J. (2009). Specification, estimation and validation of a pedestrian walking behavior model. Transportation Research B, 43(1), 36–56.
   Google Scholar
• Schroeder B.J., & Rouphail N.M (2011). Empirical behavioral models to support alternative tools for the analysis of mixed-priority pedestrian–vehicle interaction in a highway capacity context. Procedia—Social and Behavioral Sciences, 16, 653–663.
   PubMedGoogle Scholar
• Smith, D.L., Najm, W.G., & Lam, A.H. (2003). Analysis of braking and steering performance in car-following scenarios. Proceedings of 2003 SAE World Congress, Detroit, MI, 01–0283.
   Google Scholar
• Snape, J., Van den Berg, J., Guy, S.J., & Manocha, D. (2011). The hybrid reciprocal velocity obstacle. IEEE Transactions on Robotics, 27(4), 696–706.
   Web of Science ®Google Scholar
• Strayer, D.L., & Johnston, W.A. (2001). Driven to distraction: Dual-task studies of simulated driving and conversing on a cellular telephone. Psychological Science, 12, 462–466.
   PubMed Web of Science ®Google Scholar
• Sun, D., Ukkusuri, S., Benekohal, R.F., & Waller, S.T. (2003). Modeling of motorist pedestrian interaction at uncontrolled mid-block crosswalks. Proceedings of the 82d Annual Meeting of the Transportation Research Board, 03-3340.
   Google Scholar
• Van den Berg, J., Lin, M., & Manocha, D. (2008). Reciprocal velocity obstacles for real-time multi-agent navigation. Proceedings of IEEE International Conference on Robotics and Automation USA, 1928–1935.
   Google Scholar
• Vlahogianni, E.I., Karlaftis, M.G., & Orfanou, F.P. (2012). Modeling the effects of weather and traffic on the risk of secondary incidents. Journal of Intelligent Transportation Systems, 16(3), 109–117.
   Web of Science ®Google Scholar
• Wang, T., Wu, J., Zheng, P., & McDonald, M. (2010). Study of pedestrians’ gap acceptance behavior when they jaywalk outside crossing facilities. Proceedings of 13th International IEEE Conference Intelligent on Transportation Systems, Portugal, 1295–1300.
   Google Scholar
• Zegeer, C.V., Stewart, J.R., Huang, H.H., Lagerwey, P.A., Feaganes, J., & Campbell, B.J. (2005). Safety effects of marked versus unmarked crosswalks at uncontrolled locations, Final report and recommended guidelines. Technical report FHWA-HRT-04-100. Washington, DC: Federal Highway Administration, U.S. Department of Transportation.
   Google Scholar
• Zhang, Y., & Duan, H. (2007). Modeling mixed traffic flow at crosswalks in micro-simulations using cellular automata. Tsinghua Science and Technology, 12(2), 214–222.
   Google Scholar
• Zhang, X., & Chang, G.-L. (2013) An optimization model for guiding pedestrian-vehicle mixed flows during an emergency evacuation. Journal of Intelligent Transportation Systems. doi:10.1080/15472450.2013.824763
   Web of Science ®Google Scholar