Advertisement

An investigation of route-choice in integrated public transport networks by risk-averse users

  • Anthony DownwardEmail author
  • Subeh Chowdhury
  • Chapa Jayalath
Original Paper

Abstract

Globally, transport agencies are implementing integrated public transport systems to increase ridership. Transfers are a key component of such integrated systems; they facilitate connectivity among modes, allowing the system to function in unison. It is well known that transfers can cause users to feel anxious, due to the possibility of missed connections. The public transport network in Auckland, New Zealand, is undergoing a transformation into an integrated system, which means more routes with transfers are replacing direct routes. This study examines the route choice of public transport users when faced with multiple routes involving transfers. Four sub-models are developed to create the Route Choice Model. One of the contributions of the present study is the development of the Risk-Aversion sub-model, which provides a straight-forward representation of users’ risk profiles for route-choice associated with transfers. These four sub-models are applied to a proposed integrated public transport network to determine the ridership of the new routes involving transfers. The socio-economic characteristics included in this study are gender and age. The findings show that females are more risk-averse than male users, and that users aged 40 or over are more risk-averse than those under 40. Overall, users who are more risk-averse prefer the transfer options if the service is reliable, and the transfer waiting time is relatively short in proportion to the overall journey time. Future research is encouraged to investigate the effects of other socio-economic characteristics on users’ decisions to make transfers, using the findings to make further improvements to the network.

Keywords

Public transport Transfers Risk-aversion Route-choice Service quality 

Notes

References

  1. Atzner P, Delbaen F, Eber JM, Heath D (1999) Coherent measures of risk. Math Finance 9(3):203–228CrossRefGoogle Scholar
  2. Auckland Transport (2013) Auckland regional public transport plan 2013. Retrived 13 Apr 2018, from https://at.govt.nz/media/308538/RPTP-2013-updateNov29-13.pdf
  3. Auckland Transport (2014) Statistics report October 2014. Retrieved 13 Apr 2018, from https://at.govt.nz/media/829487/Stats-report-Oct-2014.pdf
  4. Bachok S (2007) What do passengers need out of public transport information systems? In: 29th Conference of Australian institute of transport research, AdelaideGoogle Scholar
  5. Bak M, Borkowski P, Pawlowska B (2012) Types of solutions improving passenger transport interconnectivity. Transp Probl 7(1):27–36Google Scholar
  6. Blainey S, Hickford A, Preston J (2012) Barriers to passenger rail use: a review of the evidence. Transp Rev 32(6):675–696CrossRefGoogle Scholar
  7. Ceder A, Chowdhury S, Taghipouran N, Olsen J (2013) Modelling public-transport users’ behaviour at connection point. Transp Policy 27:112–122CrossRefGoogle Scholar
  8. Cheng YH (2010) Exploring passenger anxiety associated with train travel. Transportation 37(6):875–896CrossRefGoogle Scholar
  9. Choi S, Ruszcynski A (2008) A risk-averse newsvendor with law invariant coherent measures of risk. Oper Res Lett 36:77–82CrossRefGoogle Scholar
  10. Chowdhury S, Ceder A (2013) Definition of planned and unplanned transfer of public-transport service and users’ decision to use routes with transfers. J Public Transp 16(2):1–20CrossRefGoogle Scholar
  11. Chowdhury S, Ceder A (2016) Users’ willingness to ride an integrated public-transport service: a literature review. Transp Policy 48:183–195CrossRefGoogle Scholar
  12. Chowdhury S, Ceder A, Sachdeva R (2013) The effects of planned and unplanned transfers on public-transport users’ perception of transfer routes. Transp Plan Technol 37(2):154–168CrossRefGoogle Scholar
  13. Chowdhury S, Ceder A, Velty B (2014) Measuring public-transport network connectivity using Google Transit with comparison across cities. J Public Transp 17(4):76–92CrossRefGoogle Scholar
  14. Chowdhury S, Ceder A, Schwalger B (2015) The effects of travel time and cost savings on commuters’ decision to travel on public transport routes involving transfers. J Transp Geogr 43:151–159CrossRefGoogle Scholar
  15. Downward A, Young D, Zakeri G (2016) Electricity retail contracting under risk-aversion. Eur J Oper Res 251:846–859CrossRefGoogle Scholar
  16. Grotenhuis JW, Wiegmans BW, Rietveld P (2007) The desired quality of integrated multimodal travel information in public transport: customer needs for time and effort savings. Transp Policy 14(1):27–38CrossRefGoogle Scholar
  17. Guo Z, Wilson NHM (2004) Assessment of the transfer penalty for transit trip. Transp Res Rec 1872:10–18CrossRefGoogle Scholar
  18. Guo Z, Wilson NHM (2007) Modeling the effect of transit system transfer on travel behaviour. Transp Res Rec 2006:11–20CrossRefGoogle Scholar
  19. Guo Z, Wilson NHM (2011) Assessing the cost of transfer inconvenience in public transport systems: a case study of the London Underground. Transp Res Part A Policy Pract 45(2):91–104CrossRefGoogle Scholar
  20. Hadas Y, Ceder A (2010) Public transit network connectivity. Transp Res Rec 2143:1–8CrossRefGoogle Scholar
  21. Hidalgo D, King R (2014) Public transport integration in Bogota and Cali, Colombia—facing transition from semi-deregulated services to full regulation citywide. Res Transp Econ 48:166–175CrossRefGoogle Scholar
  22. Iseki H, Taylor BD (2009) Not all transfers are created equal: towards a framework relating transfer connectivity to travel behaviour. Transp Rev 29(6):777–800CrossRefGoogle Scholar
  23. Liu R, Pendyala RM, Polzin S (1997) Assessment of intermodal transfer penalties using stated preference data. Transp Res Rec 1607:74–80CrossRefGoogle Scholar
  24. Manju VS, Sajitha M, Isaac KP (2008) Analysis of route choice behaviour of bus transit users using fuzzy logic. In: 23rd ARRB conference, AdelaideGoogle Scholar
  25. Matas A (2004) Demand and revenue implications of an integrated public transport policy: the case of Madrid. Transp Rev 24(2):195–217CrossRefGoogle Scholar
  26. Molin E, Chorus C (2009) The need for advanced public transport information services when making transfers. Eur J Transp Infrastruct Res 4(9):397–410Google Scholar
  27. Nazem M, Trépanier M, Morency C (2011) Demographic analysis of route choice for public transit. Transp Res Rec J Transp Res Board 2217:71–78CrossRefGoogle Scholar
  28. Pflug GC (2000) Some remarks on the value-at-risk and the conditional value-at-risk. In: Uryasev SP (ed) Probabilistic constrained optimization. Nonconvex optimization and its applications, vol 49. Springer, BostonGoogle Scholar
  29. Raveau S, Guo Z, Munoz JC, Wilson NHM (2014) A behavioural comparision of route choice on metro networks: time, transfers, crowding, topology and socio-demographics. Transp Part A 66:185–195Google Scholar
  30. Rockafellar RT, Uryasev S (2002) Conditional value-at-risk for general loss distributions. J Bank Finance 26:1443–1471CrossRefGoogle Scholar
  31. Roman D, Darby-Dowman K, Mitra G (2007) Mean-risk models using risk measures: a multi-objective approach. Quant Finance 7(4):443–458CrossRefGoogle Scholar
  32. Van de Walle S, Steenberghen T (2006) Space and time related determinants of public transport use in trip chains. Transp Res Part A Policy Pract 40(2):151–162CrossRefGoogle Scholar
  33. Vassallo JM, Di Ciommo F, Garcia A (2012) Intermodal exchange stations in the city of Madrid. Transportation 39(5):975–995CrossRefGoogle Scholar
  34. Xu H, Zhou J, Xu W (2011) A decision-making rule for modeling travelers’ route choice behaviour based on cumulative prospect theory. Transp Res Part C 19(2):218–228CrossRefGoogle Scholar
  35. Yang J, Jiang G (2014) Development of an enhanced route choice model based on cumulative prospect theory. Transp Res Part C 47:168–178CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Engineering ScienceUniversity of AucklandAucklandNew Zealand
  2. 2.Department of Civil and Environmental EngineeringUniversity of AucklandAucklandNew Zealand
  3. 3.Department of Civil and Environmental Engineering, Transportation Research CentreUniversity of AucklandAucklandNew Zealand

Personalised recommendations