TY - JOUR
T1 - Nonadditive public transit fare pricing under congestion with policy lessons from a case study in Toronto, Ontario, Canada
AU - Chin, Anchor
AU - Lai, Andy
AU - Chow, Joseph Y.J.
N1 - Funding Information:
This research was undertaken, in part, thanks to funding from the Canada Research Chairs program. The authors greatly appreciate help from Matthew J. Harvey in producing the transit geographic information system map. The authors are grateful for helpful comments from five anonymous referees.
Publisher Copyright:
© 2016, National Research Council. All rights reserved.
PY - 2016
Y1 - 2016
N2 - With increasing urbanization and the development of technologies that support automated fare collection, policy makers need decision-support tools to evaluate differentiated public transit fare pricing policies. However, the state-of-the-art tools that consider congestion effects account only for additive fares. A stochastic user equilibrium model with elastic demand was extended to handle nonadditive station-to-station-based fares and was solved by using a method of successive averages. In this paper, an illustrative example is used to show how simple price elasticities alone are not enough to predict the effects of a fare on demand within even a simple eight-node congested network. The first case study of a fare pricing policy was conducted in Toronto, Ontario, Canada; in this case, a distance-based policy was used for the Toronto Transit Commission subway system with respect to downtown and nondowntown subpopulations. The analysis found that compared with the base scenario of a Can$3 fixed fare, there are Pareto-improving fare policies (e.g., fixed rate of Can$2 and variable rate of Can$0.06/km), but the same policy might not be Pareto-improving for all subpopulations. These findings call for more sophisticated fare pricing policies for Toronto (e.g., zone-based) that can cater to specific needs of subpopulations.
AB - With increasing urbanization and the development of technologies that support automated fare collection, policy makers need decision-support tools to evaluate differentiated public transit fare pricing policies. However, the state-of-the-art tools that consider congestion effects account only for additive fares. A stochastic user equilibrium model with elastic demand was extended to handle nonadditive station-to-station-based fares and was solved by using a method of successive averages. In this paper, an illustrative example is used to show how simple price elasticities alone are not enough to predict the effects of a fare on demand within even a simple eight-node congested network. The first case study of a fare pricing policy was conducted in Toronto, Ontario, Canada; in this case, a distance-based policy was used for the Toronto Transit Commission subway system with respect to downtown and nondowntown subpopulations. The analysis found that compared with the base scenario of a Can$3 fixed fare, there are Pareto-improving fare policies (e.g., fixed rate of Can$2 and variable rate of Can$0.06/km), but the same policy might not be Pareto-improving for all subpopulations. These findings call for more sophisticated fare pricing policies for Toronto (e.g., zone-based) that can cater to specific needs of subpopulations.
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U2 - 10.3141/2544-04
DO - 10.3141/2544-04
M3 - Article
AN - SCOPUS:85015441468
VL - 2544
SP - 28
EP - 37
JO - Transportation Research Record
JF - Transportation Research Record
SN - 0361-1981
ER -