This study focuses on the importance of spatiotemporal correlation when predicting the traffic status of urban expressways in the short term. Firstly, by analyzing the data of detectors in the upstream and downstream of the expressway, we can understand the changes in traffic flow and speed, and use an ordered sample optimal segmentation algorithm to divide the day into different time periods in order to identify the time periods when the traffic status is relatively stable. Subsequently, a spatial vector autoregressive model considering the impact of upstream and downstream road segments was established to predict traffic flow and speed at target locations at different time periods. The research results indicate that this is particularly true during peak hours, where the traffic status of downstream sections has a significant impact on the upstream. During peak hours, the traffic status of the target location is mainly determined by the upstream section, while the impact of the downstream section is relatively small. Therefore, in response to this situation, sufficient prediction accuracy can be achieved by solely utilizing the traffic status of the upstream section. In addition, compared with traditional ARIMA prediction models and historical mean prediction models, the VAR model proposed in this study can more comprehensively consider the spatiotemporal dynamic characteristics of traffic status, thereby more accurately predicting the traffic status of urban expressways.


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    Title :

    Short term OD prediction of urban rail transit based on vector autoregression


    Contributors:
    Chen, Hao (editor) / Shangguan, Wei (editor) / Zhou, Lin (author) / Yao, Shengyong (author) / Li, Shuning (author) / Xue, Fei (author)

    Conference:

    Fourth International Conference on Intelligent Traffic Systems and Smart City (ITSSC 2024) ; 2024 ; Xi'an, China


    Published in:

    Proc. SPIE ; 13422


    Publication date :

    2025-01-20





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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