Advertisement

Networks and Spatial Economics

, Volume 15, Issue 3, pp 635–653 | Cite as

Trip-Based Path Algorithms Using the Transit Network Hierarchy

Article

Abstract

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.

Keywords

Public transit modeling Network hierarchy Schedule-based transit network Shortest path Hyperpath Transit A* 

Notes

Acknowledgments

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.

References

  1. Ahuja RK, Magnanti TL, Orlin JB (1993) Network flows: theory, algorithms, and applications. Prentice Hall, NJMATHGoogle Scholar
  2. 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
  3. Chabini I, Shan L (2002) Adaptations of the A* algorithm for the computation of fastest paths in deterministic discrete-time dynamic networks. Intell Transp Syst IEEE Trans 3(1):60–74CrossRefGoogle Scholar
  4. GTFS Data Exchange (2010). https://developers.google.com/transit/gtfs, Accessed Jan, 2010.
  5. De Cea J, Fernandez E (1993) Transit assignment for congested public transport systems: an equilibrium model. Transplant Sci 27(2):133–147CrossRefMATHGoogle Scholar
  6. Dijkstra E (1959) A note on two problems in connection with graphs. Numer Math 1:269–271MathSciNetCrossRefMATHGoogle Scholar
  7. Fisk C (1980) Some developments in equilibrium traffic assignment. Transp Res B 14(3):243–255MathSciNetCrossRefADSGoogle Scholar
  8. Hamdouch Y, Lawphongpanich S (2008) Schedule-based transit assignment model with travel strategies and capacity constraints. Transp Res B 42(7–8):663–684CrossRefGoogle Scholar
  9. Hamdouch Y, Marcotte P, Nguyen S (2004) A strategic model for dynamic traffic assignment. Netw Spat Econ 4(3):291–315CrossRefMATHGoogle Scholar
  10. Hart EP, Nilsson NJ, Raphael B (1968) A formal basis for the heuristic determination of minimum cost paths. Syst Sci Cybern IEEE Trans ON 4(2):100–107CrossRefGoogle Scholar
  11. 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
  12. 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
  13. 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
  14. 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
  15. Koncz N, Greenfeld J, Mouskos K (1996) A strategy for solving static multiple optimal path transit network problem. J Transp Eng 122(3):218–225CrossRefGoogle Scholar
  16. Larrain H, Muñoz JC (2008) Public transit corridor assignment assuming congestion due to passenger boarding and alighting. Netw Spat Econ 8(2–3):241–256MathSciNetCrossRefMATHGoogle Scholar
  17. 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
  18. Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper Res 52(2):191–212CrossRefMATHGoogle Scholar
  19. 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
  20. Nassir N, Khani A, Hickman M, Noh H (2012) Algorithm for intermodal optimal multidestination tour with dynamic travel times. Transp Res Rec J Transp Res Board 2283:57–66CrossRefGoogle Scholar
  21. Nguyen S, Pallottino S (1988) Equilibrium traffic assignment for large-scale transit networks. Eur J Oper Res 37(2):176–186MathSciNetCrossRefMATHGoogle Scholar
  22. Nguyen S, Pallottino S, Gendreau M (1998) Implicit enumeration of hyperpaths in a logit model for transit networks. Transp Sci 32(1):54–64CrossRefMATHGoogle Scholar
  23. Nguyen S, Pallottino S, Malucelli F (2001) A modeling framework for passenger assignment on a transport network with timetables. Transp Sci 35(3):238–249CrossRefMATHGoogle Scholar
  24. Noh H, Hickman M, Khani A (2012a) Hyperpaths in network based on transit schedules. Transp Res Rec J Transp Res Board 2284:29–39CrossRefGoogle Scholar
  25. 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
  26. Nuzzolo A, Russo F, Crisalli U (2001) A doubly dynamic schedule-based assignment model for transit networks. Transp Sci 35(3):268–285CrossRefMATHGoogle Scholar
  27. 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
  28. Schmöcker J, Bell M, Kurauchi F (2008) A quasi-dynamic capacity constrained frequency-based transit assignment model. Transp Res B 42:925–945CrossRefGoogle Scholar
  29. Spiess H, Florian M (1989) Optimal strategies: a new assignment model for transit networks. Transp Res B 23(2):83–102CrossRefGoogle Scholar
  30. Teklu F (2008) A stochastic process approach for frequency-based transit assignment with strict capacity constraints. Netw Spat Econ 8(2–3):225–240CrossRefMATHGoogle Scholar
  31. Tong CO, Richardson AJ (1984) A computer model for finding the time-dependent minimum path in a transit system with a fixed schedule. J Adv Transp 18(2):145–161CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Center for Transportation ResearchUniversity of Texas at AustinAustinUSA
  2. 2.School of Civil EngineeringUniversity of QueenslandBrisbaneAustralia
  3. 3.Pima Association of GovernmentsTucsonUSA

Personalised recommendations