Optimizing Fares and Transfer Discounts for a Bus-Subway Corridor

Liu, Bingzheng; Ge, Ying-En; Cao, Kai; Xi, Jiang; Meng, Linyun

doi:10.3846/transport.2019.11566

Cited by 3 publications

(3 citation statements)

References 30 publications

(33 reference statements)

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“…More recently, Liu et al [14] analyzed the effects of differential fare strategies on social welfare. The authors found that all differential fare strategies produced higher social welfare than the flat fare.…”

Section: Literature Reviewmentioning

confidence: 99%

Fare inequities in public transit system in Costa Rica

Calderón

Agüero-Valverde

2021

Ingeniería

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Problems in transit fare equity affect the daily commute of specific groups that depend mostly on public transportation. Previous studies showed that some routes present operational characteristics that increased the price charged to the users. To address this issue, a methodology to identify the routes that have fares much higher than expected, after considering operational parameters, is developed. This paper presents a methodology implemented to evaluate fare inequities in public transport networks. The case study is the bus public transport network in Costa Rica. The evaluation is performed using fare per kilometer as independent variable and operational variables, such as route length, monthly ridership and vehicle occupancy by using cluster analysis and Bayesian multilevel modelling. The results indicate that random coefficients models perform better than independent models for clustered data. Furthermore, the routes with higher differences between observed and estimated (i.e. expected) fares are the ones to be addressed first in individual audits, because these are the routes who charge higher operational costs into the fare, increasing inequity among the population.

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Section: Literature Reviewmentioning

confidence: 99%

Fare inequities in public transit system in Costa Rica

Calderón

Agüero-Valverde

2021

Ingeniería

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“…The transit corridor defined here is the same as one in [ 32 ], in which more sensitive analysis can be found, including the influence of transit vehicle capacity and passenger demand level.…”

Section: Numerical Experiments and Analysismentioning

confidence: 99%

Optimizing a desirable fare structure for a bus-subway corridor

Liu

Cao

et al. 2017

PLoS ONE

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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.

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“…Liu et al [1] optimized fares and transfer discount with the aim of maximizing social welfare, and concluded that travel time uncertainty and station spacing differences play an important role. From the perspective of operator and travelers, Ding [2] simulated the implementation of transfer discount in Dalian, and demonstrated discount has a promoting effect on guiding transfer travel.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Zhu

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Reasonable transit transfer discount policies incentivize people to take public transportation and alleviate the increase in vehicle usage. This research establishes and analyzes a dynamic game model with perfect information for public transportation operator and travelers as players. By treating the optimization process of discount as a learning process for operator to continuously explore and obtain the optimal price in a complex traveler environment, an adaptive discount adjustment method based on deep reinforcement learning is proposed. The result shows that transfer discount can increase the travel share of public transportation; Operator can use the first mover advantage to and achieve the most favorable Nash equilibrium for himself; The applicability of the proposed method in transfer discount scenarios is tested via simulation; According to the reinforcement learning DDPG algorithm, the Nash equilibrium is reached when the fare change interval is between (-0.68, -0.52) yuan, resulting in an increase in the usage of public transportation.

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Optimizing Fares and Transfer Discounts for a Bus-Subway Corridor

Cited by 3 publications

References 30 publications

Fare inequities in public transit system in Costa Rica

Fare inequities in public transit system in Costa Rica

Optimizing a desirable fare structure for a bus-subway corridor

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

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