Highlights A stochastic bottleneck model is formulated for morning commute problem at a merge. Two merging behavior is considered: priority merge and fixed-rate merge. User equilibrium departure patterns are derived analytically. Stochastic bottleneck capacity increased mean trip cost and lengthened peak periods. Capacity paradox in one upstream bottleneck suggests metering is beneficial.

    Abstract This paper investigates the impact of stochastic capacity at the downstream bottleneck after a merge and the impact of merging behavior on the morning commuters’ departure-time patterns. The classic bottleneck theory is extended to include a uniformly distributed capacity and the commuters’ equilibrium departure patterns are derived for two different merging rules. The results show that uncertainty in the bottleneck capacity increases the commuters’ mean trip cost and lengthens the peak period, and that the system total cost is lower under give-way merging than under a fixed-rate merging. Capacity paradoxes with dynamic user responses are found under both merging rules.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Stochastic bottleneck capacity, merging traffic and morning commute


    Contributors:


    Publication date :

    2014-02-15


    Size :

    23 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Spillovers, merging traffic and the morning commute

    Lago, Alejandro / Daganzo, Carlos F. | Elsevier | 2006


    Spillovers, merging traffic and the morning commute

    Lago, Alejandro | Online Contents | 2007


    Morning commute problem with queue-length-dependent bottleneck capacity

    Chen, Jin-Yong / Jiang, Rui / Li, Xin-Gang et al. | Elsevier | 2019


    Temporal-spatial allocation of bottleneck capacity for managing morning commute with carpool

    Xiao, Ling-Ling / Liu, Tian-Liang / Huang, Hai-Jun et al. | Elsevier | 2020