Abstract
By using a hypothetical transport network that reflects common origin and destination relations in a regional transport network, we illustrate the effects of changing fares from a zonal to a distance-based structure. We take the zonal fare as a base case and model the effect of different fare/km, including non-additive fares, where the marginal price per km is decreasing with a typical regional network. We restrict our analysis to a fixed total demand and consider the effects of fares on route choice including station access choice and walking to nearby destinations. The results indicate some general trends that can be expected, such as the fare range in order to achieve similar fare revenue incomes. At this fare parity point the total travel time tends to be reduced in the distance-based case but the flows become less dispersed. Furthermore, in case of a non-additive distance-based fare, we show that total utility could be improved at the fare parity point compared to additive fares.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
To avoid confusion we emphasise that “distance-based” in our case means “truly” distance-based, i.e. route-based. In some systems (mostly metro systems) around the world distance-based structures are employed in which customers pay according to the shortest distance between origin and the destinations, but customers are free to take any or at least a range of alternative routes as well but would not be charged for making a detour (in terms of distance).
References
Chin A, Lai A, Chow JYJ (2016) Nonadditive public transit fare pricing under congestion with policy lessons from a case study in Toronto, Ontario, Canada. Transp Res Rec J Transp Res Board 2544:28–37. https://doi.org/10.3141/2544-04
Constantin I, Florian D (2015) Integrated fare modelling with strategy-based transit assignment. In: 13th conference on advanced systems in public transport, CASPT2015, Rotterdam, The Netherlands, 19–23 July 2015
Daganzo CF (2010) Structure of competitive transit networks. Transp Res Part B Methodol 44(4):434–446. https://doi.org/10.1016/j.trb.2009.11.001
Daskin MS, Schofer JL, Haghani AE (1988) A quadratic programming model for designing and evaluating distance-based and zone fares for urban transit. Transp Res Part B Methodol 22(1):25–44. https://doi.org/10.1016/0191-2615(88)90032-X
Farber S, Bartholomew K, Li X, Páez A, Habib KMN (2014) Assessing social equity in distance based transit fares using a model of travel behavior. Transp Res Part A Policy Pract 67:291–303. https://doi.org/10.1016/j.tra.2014.07.013
Jin Z, Schmöcker J-D, Maadi S (2019) On the interaction between public transport demand, service quality and fare for social welfareoptimisation. Res Transp Econ 79:100732. https://doi.org/10.1016/j.retrec.2019.05.005
Jørgensen F, Preston J (2007) The relationship between fare and travel distance. J Transp Econ Policy 41(3):451–468
Lam WHK, Zhou J (2000) Optimal fare structure for transit networks with elastic demand. Transp Res Rec J Transp Res Board 1733(1):8–14. https://doi.org/10.3141/1733-02
Lo HK, Yip CW, Wan KH (2003) Modeling transfer and non-linear fare structure in multi-modal network. Transp Res Part B Methodol 37(2):149–170. https://doi.org/10.1016/S0191-2615(02)00005-X
Maadi S (2018) Hyperpath and social welfare optimization considering non-additive public transport fare structures. Ph.D. Thesis, Kyoto University
Maadi S, Schmöcker J-D (2017) Optimal hyperpaths with non-additive link costs. Transp Res Part B Methodol 105:235–248. https://doi.org/10.1016/j.trb.2017.08.030
Mackie PJ, Jara-Diaz S, Fowkes AS (2001) The value of travel time savings in evaluation. Transp Res Part E Logist Transport Rev 37(2–3):91–106. https://doi.org/10.1016/S1366-5545(00)00013-2
Nguyen S, Pallottino S (1988) Equilibrium traffic assignment for large scale transit networks. Eur J Oper Res 37:176–186. https://doi.org/10.1016/0377-2217(88)90327-X
Schmöcker J-D, Bell M, Kurauchi F, Shimamoto H (2009) A game theoretic approach to the determination of hyperpaths in transportation networks. In: Transportation and traffic theory 2009: golden jubilee, pp 1–18. https://doi.org/10.1007/978-1-4419-0820-9_1
Schmöcker J-D, Fonzone A, Maadi S, Van der Ploog R (2017) Determining fare structures: evidence and recommendations from a qualitative survey. EMTA Brief. http://www.emta.com/spip.php?article693. Accessed April 2020
Spiess H, Florian M (1989) Optimal strategies: a new assignment model for transit networks. Transp Res Part B Methodol 23:83–102. https://doi.org/10.1016/0191-2615(89)90034-9
Tsai F-M, Chien S, Spasovic L (2008) Optimizing distance-based fares and headway of an intercity transportation system with elastic demand and trip length differentiation. Transp Res Rec J Transp Res Board 2089:101–109. https://doi.org/10.3141/2089-13
Acknowledgements
This research has been supported by JSPS research fund 18K04390.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Maadi, S., Schmöcker, JD. Route choice effects of changes from a zonal to a distance-based fare structure in a regional public transport network. Public Transp 12, 535–555 (2020). https://doi.org/10.1007/s12469-020-00239-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12469-020-00239-9