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Modeling pedestrians' road crossing behavior in traffic system micro-simulation in China

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  • Yang, Jianguo
  • Deng, Wen
  • Wang, Jinmei
  • Li, Qingfeng
  • Wang, Zhaoan

Abstract

In many Chinese cities, pedestrian's road crossing behavior is different from that of pedestrians in developed countries. This paper presents a pedestrian model for traffic system micro-simulation in China. Considering the high rate of signal non-compliance, we classify pedestrians into two types: law-obeying ones and opportunistic ones. Opportunistic ones decide whether to violate traffic signal during red man, depending on the states of some external factors (like policeman, vehicle flow and other pedestrians' behaviors). Questionnaires were used to determine the proportions of these two types of pedestrians under different circumstances. In addition, a time gap distribution extracted from videotape were used to determine the criterion for pedestrians to decide whether to walk or wait when they conflict with vehicle flows. However, simulation results deviate from the data extracted from videotape in some degree. By adjusting the parameters on the basis of analyzing the occurrence of the deviations, the simulation results agree with the field results better. This model has represented the high rate of pedestrians' red light running and the mixed characteristics of traffic flows in Chinese cities, and it may be applicable in the micro-simulation of traffic system in other developing cities.

Suggested Citation

  • Yang, Jianguo & Deng, Wen & Wang, Jinmei & Li, Qingfeng & Wang, Zhaoan, 2006. "Modeling pedestrians' road crossing behavior in traffic system micro-simulation in China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 280-290, March.
    • Handle: RePEc:eee:transa:v:40:y:2006:i:3:p:280-290
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    References listed on IDEAS

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    1. Ali SOLTANI & Samaneh MOZAYENI, 2013. "Factors Affecting The Citizen’S Trends To Use The Pedestrian Bridges In Iran," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 5(4), pages 5-18, December.
    2. Xin, Xiuying & Jia, Ning & Zheng, Liang & Ma, Shoufeng, 2014. "Power-law in pedestrian crossing flow under the interference of vehicles at an un-signalized midblock crosswalk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 287-297.
    3. Huang, Yue & Li, Dewei & Cheng, Jianhui, 2021. "Simulation of pedestrian–vehicle interference in railway station drop-off area based on cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
    4. Li, Xiang & Sun, Jian-Qiao, 2014. "Effect of interactions between vehicles and pedestrians on fuel consumption and emissions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 661-675.
    5. Li, Xiang & Sun, Jian-Qiao, 2016. "Effects of vehicle–pedestrian interaction and speed limit on traffic performance of intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 335-347.
    6. Jairus Odawa Malenje & Jing Zhao & Peng Li & Yin Han, 2019. "Vehicle yielding probability estimation model at unsignalized midblock crosswalks in Shanghai, China," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    7. Iliani Styliani Anapali & Socrates Basbas & Andreas Nikiforiadis, 2021. "Pedestrians’ Crossing Dilemma during the First Seconds of the Red-Light Phase," Social Sciences, MDPI, vol. 10(6), pages 1-10, June.
    8. Roja Ezzati Amini & Christos Katrakazas & Constantinos Antoniou, 2019. "Negotiation and Decision-Making for a Pedestrian Roadway Crossing: A Literature Review," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
    9. Yao Wu & Yanyong Guo & Wei Yin, 2021. "Real Time Safety Model for Pedestrian Red-Light Running at Signalized Intersections in China," Sustainability, MDPI, vol. 13(4), pages 1-11, February.
    10. Egbendewe-Mondzozo, Aklesso & Higgins, Lindsey M. & Shaw, W. Douglass, 2010. "Red-light cameras at intersections: Estimating preferences using a stated choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 281-290, June.
    11. Li, Xiang & Sun, Jian-Qiao, 2015. "Studies of vehicle lane-changing to avoid pedestrians with cellular automata," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 251-271.
    12. Shunqiang Ye & Lu Wang & Kang Hao Cheong & Nenggang Xie, 2017. "Pedestrian Group-Crossing Behavior Modeling and Simulation Based on Multidimensional Dirty Faces Game," Complexity, Hindawi, vol. 2017, pages 1-12, December.
    13. Chen, Qun & Wang, Yan, 2015. "Cellular automata (CA) simulation of the interaction of vehicle flows and pedestrian crossings on urban low-grade uncontrolled roads," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 432(C), pages 43-57.
    14. Li, Baibing, 2014. "A bilevel model for multivariate risk analysis of pedestrians’ crossing behavior at signalized intersections," Transportation Research Part B: Methodological, Elsevier, vol. 65(C), pages 18-30.
    15. Li, Xiang & Sun, Jian-Qiao, 2016. "Effects of turning and through lane sharing on traffic performance at intersections," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 622-640.
    16. Huilin Liang & Qingping Zhang, 2018. "Assessing the public transport service to urban parks on the basis of spatial accessibility for citizens in the compact megacity of Shanghai, China," Urban Studies, Urban Studies Journal Limited, vol. 55(9), pages 1983-1999, July.

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