Mobile Transit Trip Planning with Real-Time Data

Jerald Jariyasunant, Daniel B. Work, Branko Kerkez, Raja Sengupta, Steven Glaser and Alexandre Bayen

University of California Transportation Center, Working Papers from University of California Transportation Center

Abstract: In this article, we describe the development of atransit trip planner(TTP) for mobile devicescalled Transitr, and evaluate its performance. The system predicts the shortest paths betweenany two points in the transit network using real-time information provided by a third party busarrival prediction system, relying on GPS equipped transit vehicles. Users submit their originand destination through a map-based iPhone application, or through a JavaScript enabled webbrowser. A server implementing a dynamic K-shortest paths algorithm with predicted linktravel times returns personalized route directions for the user, displayed on a map. To assessthe optimality and accuracy of the predicted shortest paths, an a posteriori comparison witha schedule-based transit trip planner and the GPS traces of the transit vehicles is performedon six-hundred origin destination pairs in San Francisco. The results show that routing usingthe predicted bus arrivals marginally increases the accuracy of the total travel time and theoptimality of the route. Suggestions to improve the accuracy and optimality using real-timeinformation are proposed.

Keywords: Social; and; Behavioral; Sciences (search for similar items in EconPapers)
Date: 2011-09-01
New Economics Papers: this item is included in nep-tre
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