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Modeling illegal pedestrian crossing behaviors at unmarked mid-block roadway based on extended decision field theory

Author

Listed:
  • Wang, Yongjie
  • Shen, Binchang
  • Wu, Hao
  • Wang, Chao
  • Su, Qian
  • Chen, Wenqiang

Abstract

There is a huge risk of accidents when pedestrians jaywalk across the road. It is a difficult problem to predict and reduce the behavior of jaywalking. This paper proposes a dynamic decision model of jaywalking for pedestrians based on the extended decision field theory. The model connects pedestrians’ perceptions in three dimensions such as efficiency, safety, and fairness with the dynamic traffic environment, and shows the evolution of pedestrians’ decision-making. The tester’s decision data is collected through a questionnaire survey for the purpose of model parameter estimation and validity testing. The established model is used to analyze the pedestrian’s behavior of jaywalking. The results show that the preference of pedestrians for jaywalking is inversely proportional to the traffic density. As the preference threshold increases, the probability of pedestrians choosing to jaywalk gradually decreases. For the distance between the site of jaywalking and the bus stop, the probability of jaywalking basically follows a normal distribution. The average distance gradually increases, with the increase of the car arrival rate. Additionally, location of the crosswalk has a great impact on the jaywalking behaviors. The potential application of this model is that it can help the traffic management department to predict the probability distribution of jaywalking events in the road network, which is conducive to the traffic management department to optimize the traffic infrastructure settings of potential road sections, and take effective regulatory measures to reduce the occurrence of jaywalkers.

Suggested Citation

  • Wang, Yongjie & Shen, Binchang & Wu, Hao & Wang, Chao & Su, Qian & Chen, Wenqiang, 2021. "Modeling illegal pedestrian crossing behaviors at unmarked mid-block roadway based on extended decision field theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    • Handle: RePEc:eee:phsmap:v:562:y:2021:i:c:s0378437120306993
    DOI: 10.1016/j.physa.2020.125327
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    1. Bi, Hui & Li, Aoyong & Zhu, He & Ye, Zhirui, 2023. "Bicycle safety outside the crosswalks: Investigating cyclists' risky street-crossing behavior and its relationship with built environment," Journal of Transport Geography, Elsevier, vol. 108(C).
    2. Ran Zhang & Zhonghua Wei & Heng Gu & Shi Qiu, 2021. "Behavior Evolution of Multi-Group in the Process of Pedestrian Crossing Based on Evolutionary Game Theory," Sustainability, MDPI, vol. 13(4), pages 1-17, February.
    3. Sun, Qipeng & He, Chen & Wang, Yongjie & Liu, Hang & Ma, Fei & Wei, Xiao, 2022. "Reducing violation behaviors of pedestrians considering group interests of travelers at signalized crosswalk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    4. Pérez Cruz, José Roberto & Lakouari, Noureddine & Marzoug, Rachid & Pérez Sansalvador, Julio César, 2023. "Pedestrian–vehicle interactions at unsignalized mid-block crosswalks: Effects on traffic flow, CO2 emissions, and energy dissipation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    5. Sun, Qipeng & Liu, Hang & Wang, Yongjie & Li, Qiong & Chen, Wenqiang & Bai, Pengxia & Xue, Chenlei, 2022. "Cooperation in the jaywalking dilemma of a road public good due to points guidance," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Chen, Binbin & Li, Wei, 2022. "Improving highway traffic performance: Merging pattern design of a departure zone after tollbooths or ETC," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    7. Sun, Qipeng & Cheng, Qianqian & Wang, Yongjie & Li, Tao & Ma, Fei & Yao, Zhigang, 2022. "Zip-merging behavior at Y-intersection based on intelligent travel points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).

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