To avoid the problem of low AV (Autonomous Vehicle) lane utilization due to low demand for autonomous vehicles. A new type of dedicated AVT (Autonomous Vehicle Toll) lane is considered, where Human-driven vehicles are allowed to pay to enter the AVT lane and autonomous vehicles are allowed to enter the AVT lane free of charge. The optimal allocation of dedicated AV lanes and AVT lanes in a traffic network with mixed autonomous and human-driven vehicle flows is investigated, saving the total travel cost of the transport system. A bi-level programming model with a bi-level recursive structure is developed for the optimal deployment of lanes considering mixed traffic outflow conditions of autonomous and human-driven vehicles. The upper-level model is a mathematical planning model for decision makers to optimize the total travel cost of the system using decision variables, and the lower-level model is a mixed traffic equilibrium assignment model when autonomous vehicles follow the UE travel principle and human-driven vehicles follow the SUE travel principle to select their paths. The model is solved using KT complementary relaxation conditions and sensitivity analysis is performed on the key parameters in the model. The results show that the optimal deployment of AV/AVT lanes is related to the perception factor and the market penetration of AVs. The optimal network design solution for AV/AVT lane mixed deployment is not only effective in improving traffic efficiency, but also reduces the overall system cost of the traffic network and road traffic congestion.


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    Title :

    Optimal deployment of dedicated autonomous vehicle lanes under mixed traffic flow conditions in a road network


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2023) ; 2023 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13018


    Publication date :

    2024-02-14





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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