Trip-Based Path Algorithms Using the Transit Network Hierarchy
- 517 Downloads
In this paper, we propose a new network representation for modeling schedule-based transit systems. The proposed network representation, called trip-based, uses transit vehicle trips as network edges and takes into account the transfer stop hierarchy in transit networks. Based on the trip-based network, we propose a set of path algorithms for schedule-based transit networks, including algorithms for the shortest path, a logit-based hyperpath, and a transit A*. The algorithms are applied to a large-scale transit network and shown to have better computational performance compared to the existing labeling algorithms.
KeywordsPublic transit modeling Network hierarchy Schedule-based transit network Shortest path Hyperpath Transit A*
This study has been funded by the Exploratory Advanced Research Program (EARP) of the Federal Highway Administration, and by the Strategic Highway Research Program 2 (SHRP2) Project C10-B. Appreciation is given to the University of Arizona Transit Research Unit (UATRU) members and to two anonymous reviewers for their ideas and comments.
- Bander J.L., and White C.C., (1991). A new route optimization algorithm for rapid decision support. Proceeding of IEEE Conference on Vehicle Navigation and Information Systems.Google Scholar
- GTFS Data Exchange (2010). https://developers.google.com/transit/gtfs, Accessed Jan, 2010.
- Khani A. (2013). Models and solution algorithms for transit and intermodal passenger assignment (Development of FAST-TrIPs Model). PhD Dissertation, University of Arizona, Tucson AZ.Google Scholar
- Khani A, Lee S, Hickman M, Noh H, Nassir N (2012) Intermodal path algorithm for time-dependent auto network and scheduled transit service. Transp Res Rec J Transp Res Board 2284:40–46Google Scholar
- Khani A., Sall E., Zorn L. and Hickman M., (2013). Integration of the FAST-TrIPs person-based dynamic transit assignment model, the SF-CHAMP regional, activity- based travel demand model, and san francisco’s citywide dynamic traffic assignment model. Proceedings of the 92nd Annual Meeting of Transportation Research Board, Washington DC.Google Scholar
- Khani A., Bustillos B., Noh H., Chiu Y.C., and Hickman M., (2014). Modeling transit and intermodal tours in a dynamic multimodal network. Proceeding of the 93rd Annual Meeting of the Transportation Research Board, Washington DC.Google Scholar
- Liu C.L., Pai T.E., Chang C.T., and Hsieh C.M., (2001). Path-planning algorithms for public transportation systems. Proceeding of the 4th International IEEE Conference on Intelligent Transportation Systems, Oakland, California, USA.Google Scholar
- Moore EF (1957) The shortest path through a maze. Proceeding of the international symposium on the theory of switching, vol 2, The Annuals of the Computation Laboratory of Harvard University 30. Harvard University Press, CambridgeGoogle Scholar
- Noh H, Hickman M, Khani A (2012b) Logit-based congested transit assignment using hyperpaths on a scheduled transit network, presented at the 4th international symposium on dynamic traffic assignment. Martha’s Vineyard, MAGoogle Scholar
- Raveau S, Gau Z., Munoz J.C., Wilson, N.H.M., (2012). Route choice modeling on metro networks. Proceeding of Conference on Advanced Systems for Public Transit, Santiago, Chile.Google Scholar