Optimizing a desirable fare structure for a bus-subway corridor

This paper aims to optimize a desirable fare structure for the public transit service along a bus-subway corridor with the consideration of those factors related to equity in trip, including travel distance and comfort level. The travel distance factor is represented by the distance-based fare strategy, which is an existing differential strategy. The comfort level one is considered in the area-based fare strategy which is a new differential strategy defined in this paper. Both factors are referred to by the combined fare strategy which is composed of distance-based and area-based fare strategies. The flat fare strategy is applied to determine a reference level of social welfare and obtain the general passenger flow along transit lines, which is used to divide areas or zones along the corridor. This problem is formulated as a bi-level program, of which the upper level maximizes the social welfare and the lower level capturing traveler choice behavior is a variable-demand stochastic user equilibrium assignment model. A genetic algorithm is applied to solve the bi-level program while the method of successive averages is adopted to solve the lower-level model. A series of numerical experiments are carried out to illustrate the performance of the models and solution methods. Numerical results indicate that all three differential fare strategies play a better role in enhancing the social welfare than the flat fare strategy and that the fare structure under the combined fare strategy generates the highest social welfare and the largest resulting passenger demand, which implies that the more equity factors a differential fare strategy involves the more desirable fare structure the strategy has.

[1]  Jui-Hsien Ling TRANSIT FARE DIFFERENTIALS: A THEORETICAL ANALYSIS , 1998 .

[2]  Liwei Zhang,et al.  A smoothing approach for solving transportation problem with road toll pricing and capacity expansions , 2015 .

[3]  Jiantong Zhang,et al.  Fare Design in Urban Transit Network considering Elastic Demand and Adverse Weather's Impact , 2014, J. Appl. Math..

[4]  Hai Yang,et al.  MODELING BUS SERVICE UNDER COMPETITION AND REGULATION , 2000 .

[5]  Wafaa Saleh,et al.  SOLVING TRAFFIC CONGESTION FROM THE DEMAND SIDE , 2015 .

[6]  Marc E. Pfetsch,et al.  Models for fare planning in public transport , 2012, Discret. Appl. Math..

[7]  William H. K. Lam,et al.  THE GENERALIZED NASH EQUILIBRIUM MODEL FOR OLIGOPOLISTIC TRANSIT MARKET WITH ELASTIC DEMAND , 2005 .

[8]  William H. K. Lam,et al.  Optimal Fare Structure for Transit Networks with Elastic Demand , 2000 .

[9]  Alejandro Tirachini,et al.  Agent-based optimisation of public transport supply and pricing: impacts of activity scheduling decisions and simulation randomness , 2015 .

[10]  H. Pietrantonio URBAN TRAVEL DEMAND MODELING: FROM INDIVIDUAL CHOICES TO GENERAL EQUILIBRIUM , 1997 .

[11]  John M. Rose,et al.  Multimodal pricing and optimal design of urban public transport: The interplay between traffic congestion and bus crowding , 2014 .

[12]  Shing Chung Josh Wong,et al.  Optimization of a bus and rail transit system with feeder bus services under different market regimes , 2009 .

[13]  Ying-En Ge,et al.  OPTIMIZING FARES AND TRANSFER DISCOUNTS FOR A BUS-SUBWAY CORRIDOR , 2019 .

[14]  Shing Chung Josh Wong,et al.  The Optimal Transit Fare Structure under Different Market Regimes with Uncertainty in the Network , 2009 .

[15]  A Evans A THEORETICAL COMPARISON OF COMPETITION WITH OTHER ECONOMIC REGIMES FOR BUS SERVICES , 1987 .

[16]  André de Palma,et al.  Discomfort in Mass Transit and its Implication for Scheduling and Pricing , 2013 .

[17]  Steven I-Jy Chien,et al.  Optimization of Fare Structure and Service Frequency for Maximum Profitability of Transit Systems , 2007 .

[18]  Chris Hendrickson,et al.  Design of Local Bus Service with Demand Equilibration , 1982 .

[19]  Steven I-Jy Chien,et al.  Joint Optimization of Temporal Headway and Differential Fare for Transit Systems Considering Heterogeneous Demand Elasticity , 2013 .

[20]  C. Nash,et al.  MANAGEMENT OBJECTIVES, FARES AND SERVICE LEVELS IN BUS TRANSPORT , 1978 .

[21]  B. Borger,et al.  Transport externalities and optimal pricing and supply decisions in urban transportation: a simulation analysis for Belgium , 1998 .

[22]  Hai-Jun Huang,et al.  Pricing and mode choice based on nested logit model with trip-chain costs , 2015 .

[23]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[24]  Ying-En Ge Dynamic traffic modelling for travel demand management , 2016 .

[25]  Y. She Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods , 1985 .

[26]  Michael Florian,et al.  Optimal strategies: A new assignment model for transit networks , 1989 .

[27]  Daniel Fleishman,et al.  Fare Policies, Structures, and Technologies , 1996 .

[28]  J J Collings,et al.  MAXIMISATION OF PASSENGER MILES IN THEORY AND PRACTICE , 1994 .

[29]  Shing Chung Josh Wong,et al.  A stochastic transit assignment model using a dynamic schedule-based network , 1999 .

[30]  W. Lam,et al.  BUS PASSENGER WALKING DISTANCES AND WAITING TIMES: A SUMMER-WINTER COMPARISON , 1981 .

[31]  Kathryn Stewart,et al.  Optimising network flows by low-revenue tolling under the principles of dynamic user equilibrium. , 2010 .

[32]  H. Williams On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit , 1977 .

[33]  Avishai Ceder,et al.  Integrated Optimization of Bus Line Fare and Operational Strategies Using Elastic Demand , 2017 .