Service optimization for bus corridors with short-turn strategies and variable vehicle size

Site, Paolo Delle; Filippi, F.

doi:10.1016/s0965-8564(97)00016-5

Cited by 76 publications

(42 citation statements)

References 5 publications

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“…Since the bus shares the same road infrastructure as the car, the standard deviation of running times This result stems from assuming that the arrival of buses at bus stops follows a Poisson distribution, as done in several models that consider random bus arrival times at bus stops (e.g., Delle Site and Filippi, 1998;Cominetti and Correa, 2001;Cepeda et al, 2006;Cortés et al, 2011). Implicit in (6) is that we ignore any correlation between headway and travel time in the specification of the bus utility function.…”

Section: Mode Choice Modelmentioning

confidence: 99%

Accounting for travel time variability in the optimal pricing of cars and buses

2014

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A number of studies have shown that in addition to the common influences on mode, route and time of day of travel choices such as travel time and cost, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean-variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars and buses as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes. AcknowledgementsPart of this research was developed while the first author was with the Institute of Transport and Logistics Studies in Sydney. This study is supported in part by the Australian Research Council Discovery Program Grant DP120100201 titled: 'Valuation of Service Reliability and Crowding under Risk and Uncertainty: Neglected Drivers of Demand for Public Transport', and by the Complex Engineering Systems Institute, Chile (Grants ICM P-05-004-F, CONICYT FBO16). We thank NSW Roads and Maritime Services (RMS) for providing the data to estimate the travel time variability functions in Sydney, and the referees for their extensive and invaluable comments. 1 IntroductionTravellers do not like wasting time in traffic or waiting at a bus stop. A major element of transport research has focussed on estimating and monetising the (average) time savings of infrastructure investment and the demand management measures targeted at reducing travel times. We are, however, increasingly aware that users are not only willing to pay for a shorter travel time, but also for a more reliable trip. Uncertain travel times cause users to arrive earlier or later than expected at their destination, and influences mode choice, route choice and departure time decisions; suggest...

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Section: Mode Choice Modelmentioning

confidence: 99%

Accounting for travel time variability in the optimal pricing of cars and buses

2014

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“…Tirachini et al [18] developed a short turning model to optimize the frequencies, vehicle sizes, and turnaround stations for the short turning and full-length train services on a bus corridor. Only a single operation period is taken into consideration in this work, while Site and Filippi [19] focused on service patterns over different operation periods for a bus corridor to minimize the cost for users and operators by taking short turning strategy and variable vehicle sizes into consideration. Leffler et al [20] dealt with the problem to determine where the turnaround station for a short turning service is and when a bus is a short turning service on a single bidirectional bus line in real-time.…”

Section: Introductionmentioning

confidence: 99%

A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

Zhang

Wang

et al. 2018

Journal of Advanced Transportation

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In urban rail transit systems, train scheduling plays an important role in improving the transport capacity to alleviate the urban traffic pressure of huge passenger demand and reducing the operation costs for operators. This paper considers the train scheduling with short turning strategy for an urban rail transit line with multiple depots. In addition, the utilization of trains is also taken into consideration. First, we develop a mixed integer nonlinear programming (MINLP) model for the train scheduling, where short turning train services and full-length train services are optimized based on the predefined headway obtained by the passenger demand analysis. The MINLP model is then transformed into a mixed integer linear programming (MILP) model according to several transformation properties. The resulting MILP problem can be solved efficiently by existing solvers, e.g., CPLEX. Two case studies with different scales are constructed to assess the performance of train schedules with the short turning strategy based on the data of Beijing Subway line 4. The simulation results show that the reduction of the utilization of trains is about 20.69%.

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“…These studies considered a given timetable and in Ceder [5] also the minimization of the number of vehicles required. Other studies of Delle Site and Filippi [6], Tirachini et al [7], and Cortés et al [8] dealt with the minimization of the total social cost for users and operators.…”

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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Service optimization for bus corridors with short-turn strategies and variable vehicle size

Cited by 76 publications

References 5 publications

Accounting for travel time variability in the optimal pricing of cars and buses

Accounting for travel time variability in the optimal pricing of cars and buses

A Short Turning Strategy for Train Scheduling Optimization in an Urban Rail Transit Line: The Case of Beijing Subway Line 4

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

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