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A Service-Based Fare Policy for Flex-Route Transit Services

  • Jin-xing Shen
  • Yu-han Zhou
  • Ya-nan Liu
  • Feng Qiu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)

Abstract

In this chapter, a novel service-based fare strategy is proposed in designing appropriate fare structure when current fixed-route transit turns to flex-route policy, which is now the most popular type of flexible transit services in low-density areas. Service quality offered to each passenger group under two transit policies is assessed and on the premise of pursuing no additional profit in new services, a fare structure is developed based on the variation of service quality as well as cost of provision after implementing flex-route operating policy. In the acquired fare structure, regular riders enjoy discounted fares for imposed delay and curb-to-curb requests pay more money for personal deviation services.

Keywords

Flexible transit Flex-route transit Fare policy Service quality Simulation 

Notes

Acknowledgements

This work is supported by Key Laboratory of Integrated Regulation and Resources Development of Shallow Lakes of Ministry of Education (Grant No. 2015B06114), Jiangsu Planned Projects for Postdoctoral Research Funds (Grant No. 1701086B), Natural Science Foundation of Jiangsu Province (Grant No. BK20170879), and the Fundamental Research Funds for the Central Universities (Grant No. 2016B01014).

References

  1. 1.
    Borger BD, Mayeres I, Proost S, Wouters S (1996) Optimal pricing of urban passenger transport: a simulation exercise for Belgium. J Transport Econ Policy 30(1):31–54Google Scholar
  2. 2.
    Proost S, Dender KV (2008) Optimal urban transport pricing in the presence of congestion economies of density and costly public funds. Transp Res Part A 42:1220–1230Google Scholar
  3. 3.
    Parry IWH, Small KA (2009) Should urban transit subsidies be reduced? Am Econ Rev 99(3):700–724CrossRefGoogle Scholar
  4. 4.
    Mattson J, Ripplinger D (2012) Marginal cost pricing and subsidy of small urban transit. Transp Res Rec 2274:77–83CrossRefGoogle Scholar
  5. 5.
    Cervero R (1981) Flat versus differentiated transit pricing: what’s a fair fare? Transportation 10:211–232CrossRefGoogle Scholar
  6. 6.
    Cervero R (1990) Transit pricing research. Transportation 17:117–139CrossRefGoogle Scholar
  7. 7.
    America. TCRP Report 140 (2010) A guide for planning and operating flexible public transportation services. Transportation Research Board of the National Academies, Washington, DCGoogle Scholar
  8. 8.
    Emele CD, Oren N, Zeng C, Wright S, Velaga N, Nelson J, Norman TJ, Farrington J (2013) Agent-driven variable pricing in flexible rural transport services. Commun Comput Inform Sci 365:24–35CrossRefGoogle Scholar
  9. 9.
    Daganzo CF (1984) Checkpoint dial-a-ride systems. Transp Res Part B 18(4–5):315–327MathSciNetCrossRefGoogle Scholar
  10. 10.
    Fu L (2002) Planning and design of flex-route transit services. Transp Res Rec 1791:59–66CrossRefGoogle Scholar
  11. 11.
    Quadrifoglio L, Hall RW, Dessouky MM (2006) Performance and design of mobility Allowance shuttle transit services: bounds on the maximum longitudinal velocity. Transp Sci 40(3):351–363CrossRefGoogle Scholar
  12. 12.
    Nourbakhsh SM, Ouyang Y (2012) A structured flexible transit system for low demand areas. Transp Res Part B 46:204–216CrossRefGoogle Scholar
  13. 13.
    Qiu F, Li W, Haghani A (2015) An exploration of the demand limit for flex-route as feeder transit services: a case study in Salt Lake City. Public Transport 7(2):259–276CrossRefGoogle Scholar
  14. 14.
    Qiu F, Li W, Haghani A (2015) A methodology for choosing between fixed-route and flex-route policies for transit services. J Adv Transp 49(3):496–509CrossRefGoogle Scholar
  15. 15.
    Qiu F, Li W, Shen J (2014) Two-stage model for flex-route transit scheduling. J Southeast Univ (Nat Sci Ed) 44(5):1078–1084 (In Chinese)Google Scholar
  16. 16.
    Fu L (1999) On-line and off-line routing and scheduling of dial-a-ride paratransit vehicles. Comput aided Civ Infrastruct Eng 14:309–319CrossRefGoogle Scholar
  17. 17.
    Horn MET (2002) Fleet scheduling and dispatching for demand-responsive passenger services. Transp Res Part C 10:35–63CrossRefGoogle Scholar
  18. 18.
    Crainic TG, Malucelli F, Nonato M, Guertin F (2005) Meta-heuristics for a class of demand-responsive transit systems. Informs J Comput 17(1):10–24MathSciNetCrossRefGoogle Scholar
  19. 19.
    Cremers MLAG, Haneveld WKK, Vlerk MH (2009) A two-stage model for a day-ahead paratransit planning problem. Math Methods Oper Res 69:323–341MathSciNetCrossRefGoogle Scholar
  20. 20.
    Qiu F, Li W, An C (2014) A Google maps-based flex-route transit scheduling system. In: Proceedings of the 14th COTA international conference of transportation professionals, Changsha, China, pp 247–257Google Scholar
  21. 21.
    Qiu F, Li W, Zhang J (2014) A dynamic station strategy to improve the performance of flex-route transit services. Transp Res Part C 48:229–240CrossRefGoogle Scholar
  22. 22.
    Hess DB, Brown J, Shoup D (2004) Waiting for the bus. J Public Transp 7(4):67–84CrossRefGoogle Scholar
  23. 23.
    Wardman M (2004) Public transport values of time. Transp Policy 11(4):363–377CrossRefGoogle Scholar
  24. 24.
    Alshalalfah B, Shalaby A (2012) Feasibility of Flex-Route as a Feeder Transit Service to Rail Stations in the Suburbs: Case Study in Toronto. J Urban Plann Dev 138(1):90–100CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Jin-xing Shen
    • 1
  • Yu-han Zhou
    • 1
  • Ya-nan Liu
    • 1
  • Feng Qiu
    • 2
  1. 1.Ministry of Education Key Laboratory of Integrated Regulation and Resource Development on Shallow LakesHohai UniversityNanjingChina
  2. 2.Department of Computer ScienceUniversity of VictoriaVictoriaCanada

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