Intelligent Transportation Systems (ITS) face critical challenges in mitigating the risks posed by traffic accidents, necessitating accurate traffic accident risk forecasting (TARF) to enhance safety and operational efficiency. Existing methods employ Spatiotemporal Graph Neural Networks (STGNNs) to predict traffic accident risks based on traffic dynamics. To address the sparsity of traffic accidents in TARF, these methods employ specialized data transformations or cost-sensitive loss functions, which require manual adjustments to parameters. Additionally, they fail to consider the asynchronicity between the occurrence probability and the potential severity when estimating risks. In this study, we propose DualRisk, a novel TARF model motivated by the observed asynchronous relationship between traffic accident occurrences and severity. DualRisk comprises a spatiotemporal encoding module that captures both recurring and non-recurring traffic patterns, and a two-branch prediction module that simultaneously estimates the occurrence probability and potential severity of accidents. We specifically address the sparsity issue by estimating zero-inflated distributions in the occurrence probability branch, thereby eliminating the need for manual parameter adjustments. Our approach demonstrates consistently superior performance, with the final results averaged over multiple experimental runs. Compared to the suboptimal method, our model reduces the average mean absolute error by 4.26% and improves the Recall metric by 2.86%. Additionally, case studies with visualizations illustrate DualRisk’s capability to capture the asynchronicity in TARF, underscoring its practical application value in real-world environments.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    DualRisk: A Two-Branch Model for Sparse Traffic Accident Risk Forecasting


    Contributors:
    Wang, Yupeng (author) / Luo, Xiling (author) / Zhang, Yitian (author) / Liu, Gang (author)

    Published in:

    Publication date :

    2024-12-01


    Size :

    2485050 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Accident Risk Forecasting using Contextual Vision Transformers

    Saleh, Khaled / Grigorev, Artur / Mihaita, Adriana-Simona | IEEE | 2022


    Accident Risk Forecasting

    L. Flynn | NTIS | 1977


    An Information Renewal GNN Model for Road Traffic Accident Forecasting

    Wang, Q. / Liu, S. / China Communications and Transportation Association; Transportation & Development Institute (American Society of Civil Engineers) | British Library Conference Proceedings | 2009



    Modelling and Forecasting of Highway Traffic Accident Trend

    Guirong, Z. / Fengyan, Y. / Li, W. et al. | British Library Conference Proceedings | 2010