Connected and automated vehicle (CAV) exclusive lanes are an important application of intelligent transportation technologies on highways. However, current research on CAV exclusive lanes mainly focuses on basic road segments rather than on on-ramp junctions. This paper analyzed the impact of CAV exclusive lanes on the traffic capacity of on-ramp junctions through theoretical analysis and numerical simulation. This paper established a capacity model for heterogeneous traffic consisting of CAVs and manual vehicles (MVs) considering the CAV degradation mechanism. Three key parameters, namely the CAV penetration rate, the proportion of CAVs on regular lanes, and a CAV platoon coefficient, were introduced into the heterogeneous traffic capacity model. Based on this input, the relationship between traffic capacity and the three parameters was analyzed on regular lanes. The critical CAV penetration rate at which a CAV exclusive lane reaches saturation was expressed mathematically. An on-ramp junction scenario with a CAV exclusive lane was simulated numerically with microscopic heterogeneous traffic flow models. Experimental results showed that congestion at the junction area no longer spread upstream after the CAV penetration rate exceeded 70% without CAV exclusive lanes. However, for the case in which a CAV exclusive lane was used, CAVs on the exclusive lane traveled quickly through the junction and effectively relieved traffic congestion when the CAV penetration rate reached 40%. The results also showed that a long acceleration lane slightly reduced the CAV platoon coefficient. In addition, a CAV exclusive lane improved traffic capacity when the CAV penetration rate was higher than 30%, regardless of the length of the acceleration lane.


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    Title :

    How Connected and Automated Vehicle–Exclusive Lanes Affect On-Ramp Junctions


    Contributors:
    Ma, Ke (author) / Wang, Hao (author)


    Publication date :

    2020-11-27




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown



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