Predicting on-time performance in scheduled railroad operations: methodology and application to train scheduling

Hallowell, Susan F.; Harker, Patrick T.

doi:10.1016/s0965-8564(97)00009-8

Cited by 24 publications

(13 citation statements)

References 9 publications

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“…The paper explains which of the measures represent the interests of the passengers and which are more suitable to represent the operator's point of view. Hallowell and Harker (1998) present a method of predicting the schedule reliability of a partially double rail line on which delays are caused by the need for one train to wait for the passing train. Using a simulation model for the design of a reliable timetable, the researchers describe measures of departure-time inaccuracy: at origin, arrival at destination, delay time from origin to destination, and slack time from origin to destination (slack time is defined as planned ride time minus free running time).…”

Section: Background Of Reliability Problemsmentioning

confidence: 99%

Applied analysis for improving rail-network operations

Ceder

Hassold

2015

Journal of Rail Transport Planning & Management

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Section: Background Of Reliability Problemsmentioning

confidence: 99%

Applied analysis for improving rail-network operations

Ceder

Hassold

2015

Journal of Rail Transport Planning & Management

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“…The main examples of these models are the following: (i) simulation models (see Bergmark (1996), König (2001), Middelkoop and Bouwman (2000), and Wahlborg (1996)), (ii) Max-Plus models (see Goverde (1998), De Kort (2000), and Soto Y Koelemeijer et al 2000), and (iii) analytical models (see Carey (1999), Higgins and Kozan (1998), and Huisman and Boucherie (2001)). Other relevant literature on stochastic methods for the improvement of railway timetables is Hallowell and Harker (1998), Schwanhäußer (1994), Mühlhans (1990), and Petersen and Taylor (1982). However, a drawback of the existing models is that they are mainly evaluation models and that, based on these models, optimization of a timetable can only be achieved by trial-and-error.…”

Section: Introductionmentioning

confidence: 99%

Cyclic Railway Timetabling: A Stochastic Optimization Approach

Kroon

Dekker

Vromans³

Lecture Notes in Computer Science

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Real-time railway operations are subject to stochastic disturbances. However, a railway timetable is a deterministic plan. Thus a timetable should be designed in such a way that it can absorb the stochastic disturbances as well as possible. To that end, a timetable contains buffer times between trains and supplements in running times and dwell times. This paper first describes a stochastic optimization model that can be used to find an optimal allocation of the running time supplements of a single train on a number of consecutive trips along the same line. The aim of this model is to minimize the average delay of the train. The model is then extended such that it can be used to improve a given cyclic timetable for a number of trains on a common infrastructure. Computational results show that the average delay of the trains can be reduced substantially by applying relatively small modifications to the timetable. In particular, allocating the running time supplements in a different way than what is usual in practice can be useful.

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“…Most of the research in railway scheduling has been done specific to passenger trains which, unlike freight trains, follow fixed timetables. Examples of train scheduling or timetabling include Cai and Goh (1994), Huntley et al (1995), Higgins et al (1996), Carey and Lockwood (1995), Hallowell and Harker (1998), Caprara et al (2002), Dessouky et al (2006), Zhou and Zhong (2007), D'Ariano et al (2007, 2008, Lusby et al (2011aLusby et al ( , 2011b and Cacchiani and Toth (2012).…”

Section: Introductionmentioning

confidence: 99%