This paper classified and defined traffic flows from various directions to achieve a refined estimation of vehicle inflow rates at the downstream intersections. Using sampled trajectory data from these flows, the paper analyzed vehicle arrivals, selected an exponential distribution as the prior distribution, and constructed the likelihood function for vehicle arrivals. The Maximum A Posteriori (MAP) estimation method was then applied to estimate the traffic arrival rate. Results have indicated that the Mean Absolute Percentage Error (MAPE) for arrival rate estimations from the proposed model has fluctuated around 13% under different penetration rates of connected vehicles, which is significantly lower than the average arrival rate estimation error. This has demonstrated the model's strong estimation performance. The proposed model has effectively adapted to varying penetration rate scenarios, achieving precise arrival rates and traffic volume estimates for traffic flows from different directions.


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    Titel :

    Downstream Intersection Entrance Flow Arrival Rate Estimation Using Sampled Trajectory Data


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Liu, Jun (Herausgeber:in) / Wang, Yongcai (Herausgeber:in) / Wu, Bin (Herausgeber:in) / Jiang, Zehao (Herausgeber:in) / Xiao, Yao (Herausgeber:in) / Bai, Yingjie (Autor:in) / Jiang, Shuyan (Autor:in) / Lin, Ke (Autor:in) / Zhou, Zhijie (Autor:in) / Lu, Kai (Autor:in)

    Kongress:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Erscheinungsdatum :

    16.03.2025


    Format / Umfang :

    12 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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