Generation and Calibration of Transit Hyperpaths

Schmöcker, Jan-Dirk; Shimamoto, Hiroshi; Kurauchi, Fumitaka

doi:10.1016/j.sbspro.2013.05.013

Cited by 22 publications

(21 citation statements)

References 22 publications

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“…We take the parameters and in our model from Bowman and Turnquist (1981) and showed that these values affect the service quality which might justify further calibration efforts. Furthermore, currently we assume that passengers consider all the possible departures from the stop, but some passenger groups might be interested only in a subset of the services as indicated in Schmöcker et al (2013). Related to this, the value of the cost of missing the last desired service has been simply set to a large value in our model.…”

Section: Discussionmentioning

confidence: 99%

A model of bus bunching under reliability-based passenger arrival patterns

Fonzone

Schmöcker

Liu

2015

Transportation Research Part C: Emerging Technologies

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If bus service departure times are not completely unknown to the passengers, non-uniform passenger arrival patterns can be expected. We propose that passengers decide their arrival time at stops based on a continuous logit model that considers the risk of missing services. Expected passenger waiting times are derived in a bus system that allows also for overtaking between bus services. We then propose an algorithm to derive the dwell time of subsequent buses serving a stop in order to illustrate when bus bunching might occur. We show that non-uniform arrival patterns can significantly influence the bus bunching process. With case studies we find that, even without exogenous delay, bunching can arise when the boarding rate is insufficient given the level of overall demand. Further, in case of exogenous delay, non-uniform arrivals can either worsen or improve the bunching conditions, depending on the level of delay. We conclude that therefore such effects should be considered when service control measures are discussed.

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Section: Discussionmentioning

confidence: 99%

A model of bus bunching under reliability-based passenger arrival patterns

Fonzone

Schmöcker

Liu

2015

Transportation Research Part C: Emerging Technologies

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“…With the accommodation of discrete choice models, the optimal strategy models for transit networks become more behaviorally realistic. Schmöcker et al (2013) built a two-level model in which the upper level is constrained by logit choice, while the lower level is constrained by the frequency property; the model parameters are estimated using smart card data. the error term of random utility generally stands for randomness only and can be generally represented by some parametric probability distribution.…”

Section: The Short Story Of Route Choice Studiesmentioning

confidence: 99%

A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

Fukuda

2015

Transportation Research Procedia

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Previous route choice studies treated uncertainties as randomness; however, it is argued that other uncertainties exist beyond random effects. As a general modeling framework for route choice under uncertainties, this paper presents a model of route choice that incorporates hyperpath and network generalized extreme-value-based link choice models. Accounting for the travel time uncertainty, numerical studies of specified models within the proposed framework are conducted. The modeling framework may be helpful in various research contexts dealing with both randomness and other non-probabilistic uncertainties that cannot be exactly perceived.

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“…An overview by [6] describes a range of smart card data applications, varying from strategic and tactical planning optimization to operational improvements. Most applications aim at assessing OD-patterns [18,21], route choice behavior [22] and transfer analysis [23].…”

Section: Automated Passenger Counts and Fare Validationmentioning

confidence: 99%

“…The most detailed information is available in the latter case, where each trip leg in a journey -a journey may consist of multiple trip legs separated by interchanges -is tracked, whereas when the smart card devices are located on the platforms, information is only available of the first and the last station, making route search through the public transport network necessary for the analyst (e.g. [22]). The complete passenger journey can be therefore traceable.…”

Section: A Passenger Behaviour Estimation and Ridership Predictionmentioning

confidence: 99%

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

Oort

Cats

2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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ABSTRACT-New big data (sources) in the public transport industry enable to deal with major challenges such as elevating efficiency, increasing passenger ridership and satisfaction and facilitate the information flow between service providers and service users. This paper presents two actual cases from the Netherlands and Sweden in which automated data sources were utilized to support the planning and operational processes. The cases illustrate the benefits of using smartcard and vehicle positioning data. Due to the data (processing), valuable insights were gained helping to make the right choices and improve the public transport system.

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Generation and Calibration of Transit Hyperpaths

Cited by 22 publications

References 22 publications

A model of bus bunching under reliability-based passenger arrival patterns

A model of bus bunching under reliability-based passenger arrival patterns

A Hyperpath-based Network Generalized Extreme-value Model for Route Choice under Uncertainties

Improving Public Transport Decision Making, Planning and Operations by Using Big Data: Cases from Sweden and the Netherlands

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