Bi-objective conflict detection and resolution in railway traffic management
Bi-objective conflict detection and resolution in railway traffic management
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DOI：
10.1016/j.trc.2010.09.009
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发表时间：
2012-02-01
影响因子：
8.3
通讯作者：
Pranzo, Marco
中科院分区：
文献类型：
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作者：
Corman, Francesco;D'Ariano, Andrea;Pranzo, Marco
铁路冲突检测和解决是调度员面临的日常任务，包括在干扰使时刻表不可行时调整列车时刻表。调度员在这项任务中追求的主要目标是最大限度地减少列车延误，而列车运营公司也对乘客不满的其他指标感兴趣。当减少列车延误需要取消一些连接服务时，这两个目标是相互冲突的，从而导致换乘乘客的额外等待时间。实际上，基础设施公司和列车运营公司讨论保留或放弃哪些连接，以达到一个折衷的解决方案。本文考虑的双目标问题，最小化列车延误和错过的连接，以提供一组可行的非支配时间表，以支持这一决策过程。我们使用一个详细的替代图模型，以确保调度的可行性和开发两个启发式算法来计算非支配调度的帕累托前沿。我们的计算研究，一个复杂的和密集的荷兰铁路网络的基础上，表明，良好的协调连接的列车服务是非常重要的，以实现实时效率的铁路服务，因为连接的管理可能会严重影响列车正点。这两种算法在有限的计算时间内精确地逼近了Pareto前沿。(C)2010爱思唯尔有限公司版权所有。
Railway conflict detection and resolution is the daily task faced by dispatchers and consists of adjusting train schedules whenever disturbances make the timetable infeasible. The main objective pursued by dispatchers in this task is the minimization of train delays, while train operating companies are also interested in other indicators of passenger dissatisfaction. The two objectives are conflicting whenever train delay reduction requires cancellation of some connected services, causing extra waiting times to transferring passengers. In fact, the infrastructure company and the train operating companies discuss on which connection to keep or drop in order to reach a compromise solution.This paper considers the bi-objective problem of minimizing train delays and missed connections in order to provide a set of feasible non-dominated schedules to support this decisional process. We use a detailed alternative graph model to ensure schedule feasibility and develop two heuristic algorithms to compute the Pareto front of non-dominated schedules. Our computational study, based on a complex and densely occupied Dutch railway network, shows that good coordination of connected train services is important to achieve real-time efficiency of railway services since the management of connections may heavily affect train punctuality. The two algorithms approximate accurately the Pareto front in a limited computation time. (C) 2010 Elsevier Ltd. All rights reserved.