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Finding routes in a public transport network. A case study

机译:在公共交通网络中查找路线。案例研究

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There is a need for reliable and trustworthy public transport planner services. Mass transits are trying to solve problems as traffic jams or increasing pollution in densely populated city agglomerations. Passengers are looking for travel information on-line and companies providing public transport solutions often struggle with providing modern trip planner platforms. We address this problem by providing simple and easy to implement solution for shortest paths computation in public transport network of any size. In first part of the paper we present our approach to building time-expanded graph model of public transportation network. We use a structure with multiple vertices within one stop identified by triple (stop, time, line) and edges with constant travel times. Second part introduces k-shortest paths algorithm for created graph. We provide step by step explanation along with code samples. Average algorithm response time is around 1.5s for a public transport network of the size over 2800 stops. This is accepted query time for a web application. None additional speed-up techniques has been used to keep implementation and explanations simple and leaving room for further improvements. Algorithm has been successfully tested in Upper Silesian Industrial District (GOP) in cooperation with main public transport provider in the area (KZK GOP). This real instance is the biggest one in Poland and contains 370 lines and over 2800 stops.
机译:需要可靠和可信赖的公共交通计划者服务。大众运输部门正试图解决人口稠密的城市群中交通拥堵或污染加剧的问题。乘客正在在线寻找旅行信息,而提供公共交通解决方案的公司通常会在提供现代旅行计划平台的过程中苦苦挣扎。我们通过为各种规模的公共交通网络中的最短路径计算提供简单,易于实现的解决方案来解决此问题。在本文的第一部分中,我们介绍了建立公共交通网络的时间扩展图模型的方法。我们使用一种结构,该结构在一个停靠点内具有多个顶点,该停靠点由三倍(停靠点,时间,线)和具有恒定行进时间的边标识。第二部分介绍了创建图的k最短路径算法。我们提供逐步说明以及代码示例。对于大小超过2800站的公共交通网络,平均算法响应时间约为1.5秒。这是Web应用程序可接受的查询时间。没有使用任何其他的加速技术来使实现和说明保持简单,并为进一步的改进留有空间。已与该地区主要的公共交通提供商(KZK GOP)合作,在上西里西亚工业区(GOP)中成功测试了该算法。该真实实例是波兰最大的实例,包含370条线路和2800多个站点。

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