基于精简搜索树的公交出行路径选择
Trip-Based Public Transit Routing Using Condensed Search Trees
摘要：
研究了公交网络中的帕累托最优出行规划问题。大多数现有的算法和加速技术的工作原理是计算子行程的中间站，直到到达目的地。相比之下，基于行程的模型侧重于行程和它们之间的转移，将行程构建为一系列行程。在本文中，我们开发了一种加速技术，该模型的灵感来自现有的最先进的加速技术，传输模式和集线器标签背后的原则。由此产生的算法使我们能够在不到半毫秒的时间内在非常大的真实网络上计算帕累托最优（相对于到达时间和传输次数）24小时配置文件。与公共交通网络上的双标准查询的当前技术水平相比，这快了两个数量级，同时增加了预处理开销最多一个数量级。
英文摘要：
We study the problem of planning Pareto-optimal journeys in public transit networks. Most existing algorithms and speed-up techniques work by computing subjourneys to intermediary stops until the destination is reached. In contrast, the trip-based model focuses on trips and transfers between them, constructing journeys as a sequence of trips. In this paper, we develop a speed-up technique for this model inspired by principles behind existing state-of-the-art speed-up techniques, Transfer Pattern and Hub Labelling. The resulting algorithm allows us to compute Pareto-optimal (with respect to arrival time and number of transfers) 24-hour profiles on very large real-world networks in less than half a millisecond. Compared to the current state of the art for bicriteria queries on public transit networks, this is up to two orders of magnitude faster, while increasing preprocessing overhead by at most one order of magnitude.
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