Optimizing a desirable fare structure for a bus-subway corridor

Abstract

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.

Research on transfer discount for public transportation based on deep reinforcement learning and game theory

“…Liao [4] established a multi-objective model for discount policy, and concluded the impedance of the road network decreases most significantly when the discount range is 15%-35%. Liu [5] jointly optimized the fare system of rail transit and bus, obtained the best combination fare system, and predicted the distribution of travelers in different public transport lines. Jin et al [6] analyzed the influence of different discount policies on the choice of travel modes of different groups in Chengdu.…”

Understanding the effect of sociodemographic and psychological latent characteristics on flex-route transit acceptance

“…The advantages of such a mode might be subjected to the low-demand areas. For instance, the fixed schedule and routes could increase the departure interval of buses in low-demand areas, and in turn affect the comfort of passengers [ 4 , 5 ]. In this context, flex-route transit (FRT) is regarded as a promising public transit mode [ 5 , 6 ].…”

“…For strategy planning, which aims to apply an appropriate transit system in the different service areas and actual demand levels. Therefore, a decision method between traditional fixed-route and FRT in various demand densities was presented [ 4 , 16 ]. Furthermore, a mixed transit system incorporating the advantages of fixed-route transit and FRT was proposed.…”

Optimizing Fares and Transfer Discounts for a Bus-Subway Corridor

“…The other aim is to analyse impacts of consideration of new passenger O-D pairs and uncertainty in travel time of mode on the optimal fare structure. The two intellectual merits differentiate this work from Liu et al (2017). Another intellectual merit of this work is the integration into the optimization model of difference in stop spacing between bus and subway services and the influence of uncertainty in travel time of each mode on passenger behavior.…”