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首页> 外文期刊>Transportation Research Part B: Methodological >Synchronization of bus timetabling
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Synchronization of bus timetabling

机译：总线时间表同步

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摘要

Timetable generation is a subproblem of bus network strategic planning, in which the departure time of each trip is determined. We study the bus network of Monterrey, Mexico, which is similar to those of other cities in Latin America. It is a large bus network where passenger transfers must be favored, almost evenly spaced departures are sought, and bus bunching of different lines must be avoided. We formulate the timetabling problem of this network with the objective of maximizing the number of synchronizations to facilitate passenger transfers and avoid bus bunching along the network. We define these synchronizations as the arrivals of two trips with a separation time within a time window to make a flexible formulation. This flexibility is a critical aspect for the bus network, since travel times vary because of reasons such as driver speed, traffic congestion, and accidents. By proving that our problem is NP-hard we answer a 10-year-old open question about the NP-hardness of similar problems present in literature. Next, we analyze the structural properties of the feasible solution space of our model. This analysis leads to a preprocessing stage that eliminates numerous decision variables and constraints. Moreover, this preprocessing defines feasible synchronization and arrival time windows that are used in a new metaheuristic algorithm. Empirical experimentation shows that our proposed algorithm obtains high-quality solutions for real-size instances in less than one minute.
机译：时间表的生成是公交网络战略规划的一个子问题，其中确定每次行程的出发时间。我们研究了墨西哥蒙特雷的公交网络，该网络与拉丁美洲的其他城市类似。这是一个大型公交网络，必须支持旅客转乘，寻求几乎均匀间隔的出发点，并且必须避免不同线路的公交车集中。我们制定此网络的时间表问题，目的是最大化同步次数，以方便乘客转移并避免沿网络的公交车拥挤。我们将这些同步定义为两次旅行的间隔时间内的间隔时间，以形成灵活的表述方式。对于公交网络来说，灵活性是至关重要的方面，因为行驶时间会因驾驶员速度，交通拥堵和事故等原因而有所不同。通过证明我们的问题对NP困难，我们回答了一个有关文献中类似问题的NP困难的10年未解决的问题。接下来，我们分析模型可行解空间的结构特性。该分析导致一个预处理阶段，该阶段消除了许多决策变量和约束。此外，该预处理定义了在新的元启发式算法中使用的可行的同步和到达时间窗口。经验实验表明，我们提出的算法可在不到一分钟的时间内为真实大小的实例获得高质量的解决方案。

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