y型网络中个人和家庭乘客的早晨通勤问题

Transp. Sci. Pub Date : 2022-02-04 DOI:10.2139/ssrn.3881217
Dongdong He, Yang Liu, Qiuyan Zhong, D. Wang
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引用次数: 4

摘要

本文研究了一个具有两条上游链路和一条下游链路的y型网络中家庭通勤者和个人通勤者的早晨通勤问题。家庭家长每天上学前都要经过一个容量有限的上游瓶颈,将孩子送到学校。然后,他们穿越家庭和个人通勤者共同的下游瓶颈,到达工作场所。我们探讨了错开政策(即错开上班和上学时间)对交通拥堵分布和社会福利的影响。我们解析解决了所有的均衡情况,揭示了所有的交通拥堵模式。结果表明,错开政策在某些情况下可能是有害的。当个人需求相对较低时,错开政策可能不会改善社会福利。当个人需求较高时,优化工作开始时间与学校开始时间之间的时间间隔,可以显著提高社会福利。研究了错开策略对系统性能的影响。在考虑总系统成本和总拥塞成本这两个系统性能指标时,我们推导出了一个Pareto边界,为决策者提供了一个很好的候选集。我们的研究结果表明,系统性能的最优交错策略取决于两组的需求分布。当个体需求较高时,存在唯一的最优交错策略,使系统性能最优。然而,当个体需求较低时,应从帕累托边界选择最优错开策略。在此基础上,我们重新审视了错开政策下的产能扩张悖论。研究表明,在下游瓶颈处扩容总能降低系统总成本。然而,当上游瓶颈的容量扩大时,可能会出现这种矛盾现象,但如果合理设计调度间隙,则可以消除这种矛盾现象。
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On the Morning Commute Problem in a Y-shaped Network with Individual and Household Travelers
This paper examines the morning commute problem when both household commuters and individual commuters are considered in a Y-shaped network with two upstream links and a single downstream link. The household parents daily pass through an upstream bottleneck with a limited capacity before a school and drop off their children. Then, they traverse the downstream bottleneck common to both household and individual commuters and arrive at the workplace. We explore the effects of staggering policy, that is, staggering work and school start times, on the distribution of traffic congestion and social welfare. We analytically solve all the equilibrium cases and reveal all the traffic congestion patterns. The results reveal that the staggering policy may be harmful in certain cases. When the demand of individuals is relatively low, the staggering policy may not improve social welfare. When the demand of individuals is high, social welfare can be significantly improved if the schedule gap between the work start time and school start time is optimized. The effects of the staggering policy on system performance are examined. We derive a Pareto frontier, which provides a good candidate set for policymakers when the two system performance measures, that is, the total system cost and the total congestion cost, are considered. Our results show that the optimal staggering policy on system performance depends on the demand distribution of the two groups. When the demand of individuals is high, there exists a unique optimal staggering policy that optimizes system performance. However, when the demand of individuals is low, the optimal staggering policy should be selected from the Pareto frontier. Furthermore, we re-examine the capacity expansion paradox under the staggering policy. Our study shows the capacity expansion at the downstream bottleneck can always reduce the total system cost. However, the paradoxical phenomenon may arise when the capacity of the upstream bottleneck is expanded, but it can be eliminated if the schedule gap is properly designed.
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