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原文传递 Mobile Transit Trip Planning with Real-Time Data

题名：	Mobile Transit Trip Planning with Real-Time Data
正文语种：	中文
作者：	Jerald Jariyasunant aniel . Work ranko Kerkez Raja Sengupta Steven Glaser Alexandra ayen
作者单位：	epartment of ivil Engineering, University of alifornia, Berkeley 621 Sutardja ai Hall Berkeley, 94720 epartment of ivil Engineering, University of alifornia, Berkeley 640 Sutardja ai Hall Berkeley, 94720 epartment of ivil Engineering, University of alifornia, Berkeley 151 Hearst Memorial Mining Buiilding Berkeley, 94720 epartment of ivil Engineering, University of alifornia, Berkeley 642 Sutardja ai Hall Berkeley, 94720 4
关键词：	Data Real-Time Trip Transit the shortest paths transit optimality information real-time and through origin web browser travel time Suggestions performance development application JavaScript
摘要：	In this article, we describe the development of a transit trip planner (TTP) for mobile devices called Transitr, and evaluate its performance. The system predicts the shortest paths between any two points in the transit network using real-time information provided by a third party bus arrival prediction system, relying on GPS equipped transit vehicles. Users submit their origin and destination through a map-based iPhone application, or through a JavaScript enabled web browser. A server implementing a dynamic K-shortest paths algorithm with predicted link travel times returns personalized route directions for the user, displayed on a map. To assess the optimality and accuracy of the predicted shortest paths, an a posteriori comparison with a schedule-based transit trip planner and the GPS traces of the transit vehicles is performed on six-hundred origin destination pairs in San Francisco. The results show that routing using the predicted bus arrivals marginally increases the accuracy of the total travel time and the optimality of the route. Suggestions to improve the accuracy and optimality using real-time information are proposed.
会议日期：	20120528
会议举办地点：	深圳
会议名称：	第九届中国(国际)城市智能交通论坛
出版日期：	2012-05-28
母体文献：	第九届中国(国际)城市智能交通论坛论文集
分类号：	TP3 TU5