The emerging connected vehicle (CV) technology provides an opportunity to formulate an ambulant data platform that allows the actual data transfer among multiple vehicles and roadside infrastructure. However, the challenge to achieve such an envisioned cooperation lies in the connectivity and interoperability of the CV data and the signal control infrastructure. To address this challenge, we developed distributed computing data intelligence algorithms to make roadside units (RSU) more intelligent. To test the efficiency of the developed algorithms, a simulation-based test bed was established within the VISSIM platform, and a corridor consisting of four signalized intersections has been evaluated at different CV penetration rates under three different traffic conditions (light, mild, and heavy traffic conditions). The simulation results indicate that average vehicle delay and queue length can be reduced by 46.04% and 56.15%, respectively, under a 50% CV penetration rate after optimization. The benefits can be foreseen from improved mobility performance, including increased throughput, reduced travel time reduction, and reduced fuel consumption and emissions.


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    Title :

    V2I-Based Intelligent Mobility Data Fusion for Self-Adaptive Traffic Signal Control


    Contributors:
    Wei, Heng (author) / Kashyap, Gaurav (author) / Nian, Dong (author) / Lin, Wei (author)

    Conference:

    International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington



    Publication date :

    2022-08-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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