4.7 Article

Lane-based Distance-Velocity model for evaluating pedestrian-vehicle interaction at non-signalized locations

期刊

ACCIDENT ANALYSIS AND PREVENTION
卷 176, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.aap.2022.106810

关键词

Pedestrian-vehicle interaction; Lane-based Distance-Velocity model; Pedestrian safety; Traffic conflict techniques; Yielding behaviour

资金

  1. National Natural Science Foundation of China (NSFC) [71801020]
  2. National Key Research and Development Plan [2021YFE0203600]
  3. Humanities and Social Science Fund of Ministry of Education of China [18YJC630168]
  4. Natural Science Foundation of Shaanxi Province [2022JQ-731]
  5. China Scholarship Council [202006565010]

向作者/读者索取更多资源

The Lane-based Distance-Velocity model (LDV) is proposed to investigate pedestrian-vehicle interaction at non-signalized crosswalks, revealing that taxi drivers have a deliberate violation rate double that of private car drivers, and taxis are more likely to create dangerous or risky crossing situations for pedestrians.
Pedestrian vehicle conflicts at non-signalized crosswalks are a world-wide safety concern. Although the pedestrian priority policy is applied in some regions to improve pedestrian safety, its effect needs further investigation. This study proposes the Lane-based Distance-Velocity model (LDV) to investigate pedestrian-vehicle interaction at non-signalized crosswalks. Compared with the DV model, the LDV model considers the lateral distance between vehicles and pedestrians. Therefore, the LDV model extends the application of the DV model by allowing it to be applied not only on one-lane streets to multi-lane streets. The conflict severities of pedestrian-vehicle interaction in the LDV model are classified into four categories: safe-passage, mild-interaction, potential-conflict and potential-collision. Based on that, pedestrian crossing decisions are graded as safe-crossing, risky-crossing, and dangerous-crossing. The experimental data are collected at a non-signalized crosswalk through drone footage collected in Xi'an City (China) with a Machine Vision Intelligent Algorithm. The model is tested through a case study to evaluate pedestrian crossing safety when interacting with private cars and taxis. Results from the case study suggest that the proposed model works well in the pedestrian-vehicle interaction analysis. Firstly, 87.9% of drivers are willing to provide right-of-way to pedestrians when they have enough time to react and yield. Then, both the DV model and LDV model have reached consistent conclusions: the deliberate violation rate (DVR) of taxi drivers is 22.64%, which is double that of private car drivers. Last, taxis commit a higher percentage of pedestrians' dangerous or risky crossing situations than private cars. Relevant government departments can utilize the results of this study to manage urban traffic better and improve pedestrian safety.

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