Dedicated lanes for autonomous vehicles (AV) are introduced as an effective strategy to improve mobility in mixed traffic of AVs and heterogeneous drivers. However, adding a new lane is costly, and dedicating an existing lane may increase traffic congestion in other lanes. Previous studies investigated the impacts of AV dedicated lanes on throughput at the segment level and/or assumed a fixed route choice. However, AV dedicated lanes change route choice behavior, which affects traffic distribution over the network. Therefore, this study explores the impacts of AV dedicated lanes on the traffic performance of large-scale networks by conducting a network-level cost–benefit analysis on the implementation of AV dedicated lanes under different demand and AV market penetration rate (MPR) scenarios. This study examines the impacts of implementing AV dedicated lanes to freeway links on traffic congestion at corridor and network levels. Various factors are explored: changes in aggregate flow–density relationships, throughput, and average travel times. To this end, DYNASMART-P software is updated to consider AV dedicated lanes and is used to simulate the mixed traffic. Traffic simulation analyses on the large-scale network of Chicago indicate that these impacts depend on the AV MPR, demand level, and AV dedicated lane implementation approach. Dedicated lanes for AVs are beneficial for a high demand level scenario at all AV MPRs. However, for the base demand scenario, (760,000 vehicles during a.m. peak), deploying such lanes is only justified for low AV MPRs. Furthermore, the impacts of AV dedicated lanes on traffic at the network level are different from those on single segments.


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    Title :

    Assessing the Network-Wide Impacts of Dedicated Lanes for Connected Autonomous Vehicles


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2022-08-08




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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