Pedestrian Road Safety Analysis Based on Macro-level Collision Prediction Models
ID:1799 View Protection:ATTENDEE Updated Time:2021-12-17 14:30:46 Hits:244 Poster Presentation

Start Time:2021-12-17 08:00(Asia/Shanghai)

Duration:1min

Session:P2 Poster2021 » P2T3Track 3 Transportation Planning and Policy

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Abstract
Worldwide, road safety of pedestrians has been recognized as a public health concern. Previous studies have been conducted to analyze pedestrian safety at macro-level with only a few were conducted in China. This study aims at investigating the impacts of potential contributing factors on the occurrence of pedestrian-involved collisions at zonal level in an urban area in China. 138 traffic analysis zones were included in the study. Both generalized linear regression and full Bayesian macro-level collision prediction models were developed to examine the correlations between pedestrian crashes and multiple variables. Results show that pedestrian crashes are positively correlated with exposure, population density, density of 4-way signalized intersections and percentage of arterials, while negatively correlated with density of 3-way intersections and percentage of collectors. The findings of this study could help decision makers, planners and safety engineers gain better understanding of pedestrian crashes and develop countermeasures to enhance pedestrian safety.
Keywords
CICTP;Pedestrian Road Safety
Speaker
Tian Zhun
Chang’an University

Submission Author
Zhun Tian Chang'an University
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Important Date
  • Conference Date

    Dec 17

    2021

    to

    Dec 20

    2021

  • Dec 16 2021

    Contribution Submission Deadline

  • Dec 24 2021

    Registration deadline

Sponsored By
Chinese Overseas Transportation Association
Chang'an University
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