Volume 41 Issue 1
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CHU Zhaoming, CHEN Ruixiang, LIU Jinguang. A Model of Risk Classification and Forewarning for Pedestrian Crossing Behavior at Unsignalized Urban Roadways[J]. Journal of Transport Information and Safety, 2023, 41(1): 53-61. doi: 10.3963/j.jssn.1674-4861.2023.01.006
Citation: CHU Zhaoming, CHEN Ruixiang, LIU Jinguang. A Model of Risk Classification and Forewarning for Pedestrian Crossing Behavior at Unsignalized Urban Roadways[J]. Journal of Transport Information and Safety, 2023, 41(1): 53-61. doi: 10.3963/j.jssn.1674-4861.2023.01.006
shu

A Model of Risk Classification and Forewarning for Pedestrian Crossing Behavior at Unsignalized Urban Roadways

doi: 10.3963/j.jssn.1674-4861.2023.01.006
  • 1.

    Research Institute for Road Safety of MPS, Beijing 100062, China

  • 2.

    The School of Traffic Management, People's Public Security University of China, Beijing 100038, China

  • Received Date: 2022-04-21
    Available Online: 2023-05-13
    Abstract
  • To quantify the collision risk for pedestrian crossing at unsignalized urban roadways, a method for classifying such risk is proposed based on a K-means algorithm, and a forewarning model is also developed based on random forest technique. Three indicators, conflict time difference to conflict, potential collision distance, and potential collision energy, are selected to describe the real-world human-vehicle interactions by considering their temporal and spatial proximity and the severity of potential collisions. A K-means algorithm is applied to cluster the states of pedestrian crossing risk and classify the corresponding risk into different levels. Thirty indicators are proposed by analyzing the potential risk factors from the following five aspects, including weather, traffic facilities, behaviors of traffic participants, historical accidents, and others presented in the process of pedestrian crossing. An optimal set of forewarning indicators is extracted after screening the above indicators according to the Gini purity. Taking the optimal set as the model input, a hierarchical forewarning model which can refine and predict the pedestrian crossing risk is developed by using a random forest algorithm. The accuracy of the model is verified based on three pedestrian crossing datasets collected in a city of Shanxi Province. Experiment results show that quantitative classification is consistent with the real-world pedestrian crossing scenarios when the level of pedestrian crossing risk is divided into 5 levels. The overall accuracy of the hierarchical forewarning model reaches 86.67%. The accuracy of identifying level Ⅰ and level Ⅳ risk are even higher, as their accuracy reaches 100% and 94.7%, respectively. The proposed method also mitigates several issues from the models presented in the previous studies, such as incomplete risk indicators, unrealistic risk classification and unrefined warning level, and improves the accuracy of risk forewarning for pedestrians crossing streets.

     

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