Determining Appropriate Fare Levels for Distance-Based Fare Structure

Yook, Donghyung; Heaslip, Kevin

doi:10.3141/2415-14

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…In terms of the graduated fare, as a very popular system in Germany and Switzerland [5], the zone fare system is simpler than the distance-based fare system [1][2][3]. Over the past several decades, the distancebased fare system has been extensively studied [6][7][8][9][10][11][12][13][14][15], but the research on the design of zone fare system has been neglected. To the best of our knowledge, the literature on zone fare system is scarce.…”

Section: Introductionmentioning

confidence: 99%

Zone Fare System Design in a Rail Transit Line

Yang

Deng

Wang

et al. 2020

Journal of Advanced Transportation

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As a widely existing form of public transit fare structures, zone fare system is traditionally designed in a separate way, which may lead to suboptimal results. This paper aims to concurrently address the zone division and fare calculation issues of the zone fare system design in a rail transit line. It is necessary to consider passengers as well as operators to find an impartial zone fare system. A fair zone fare system is one where the zone fares are as close as possible to the distance-based fares, for the fares of the distance-based fare system are highly correlated with the actual distance of trips. Thus, the fare deviations for the trips between the zone fare system and the distance-based fare system are utilized as the evaluation metric of fairness. To achieve the goal of minimizing fare deviations for all trips, we introduce three indexes: average absolute deviation, average squared deviation, and maximum mileage fare deviation. With the three indexes as the objective functions, we develop a joint optimization algorithm where a novel zone boundary adjustment scheme is proposed as the key technique. Numerical results show that the proposed algorithm can effectively provide a joint optimal scheme and the optimal number of planned zones is 5 for Changsha Metro Line 2. The proposed algorithm can provide guidance for the practical design and adjustment of the zone fare system.

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Section: Introductionmentioning

confidence: 99%

Zone Fare System Design in a Rail Transit Line

Yang

Deng

Wang

et al. 2020

Journal of Advanced Transportation

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“…When differential fare strategy is applied, passengers pay fare dependent on one or more factors, including length of trip, time of day, quality of service. Most studies on differential fare strategy, such as, Yang and Kin [21], Tsai et al [28,40], and Yook and Heaslip [41], concentrated on distance based factors.…”

Section: Introductionmentioning

confidence: 99%

Integrated Optimization of Bus Line Fare and Operational Strategies Using Elastic Demand

Tang

Ceder

2017

Journal of Advanced Transportation

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An optimization approach for designing a transit service system is proposed. Its objective would be the maximization of total social welfare, by providing a profitable fare structure and tailoring operational strategies to passenger demand. These operational strategies include full route operation (FRO), limited stop, short turn, and a mix of the latter two strategies. The demand function is formulated to reflect the attributes of these strategies, in-vehicle crowding, and fare effects on demand variation. The fare is either a flat fare or a differential fare structure; the latter is based on trip distance and achieved service levels. This proposed methodology is applied to a case study of Dalian, China. The optimal results indicate that an optimal combination of operational strategies integrated with a differential fare structure results in the highest potential for increasing total social welfare, if the value of parameter related to additional service fee is low. When this value increases up to more than a threshold, strategies with a flat fare show greater benefits. If this value increases beyond yet another threshold, the use of skipped stop strategies is not recommended.

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“…As shown in the next section, the literature on systematic evaluation of pricing policies across a whole network is relatively scarce. The studies can generally be divided between (a) elasticity-dependent price optimization without congestion effects [e.g., Daskin et al (10), Chien and Tsai (11), Borndörfer et al (12), and Yook and Heaslip (13)] and (b) zone-based tariff optimization without congestion effects (14,15).…”

mentioning

confidence: 99%

Nonadditive Public Transit Fare Pricing Under Congestion with Policy Lessons from a Case Study in Toronto, Ontario, Canada

Chin

Lai

Chow³

2016

Transportation Research Record: Journal of the Transportation R

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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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Determining Appropriate Fare Levels for Distance-Based Fare Structure

Cited by 7 publications

References 10 publications

Zone Fare System Design in a Rail Transit Line

Zone Fare System Design in a Rail Transit Line

Integrated Optimization of Bus Line Fare and Operational Strategies Using Elastic Demand

Nonadditive Public Transit Fare Pricing Under Congestion with Policy Lessons from a Case Study in Toronto, Ontario, Canada

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