Train Dwell Time Models for Rail Passenger Service

San, Hor Peay; Masirin, Mohd Idrus Mohd

doi:10.1051/matecconf/20164703005

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…However, it can be seen that, in most of the models developed, passengers are one major aspect which contributed to the train dwell time. The number of boarding and alighting passengers and throughpassengers were included in the majority of these models [25].…”

Section: Review Of Research On Dwell Timementioning

confidence: 99%

The influence of the density inside a train carriage on passenger boarding rate

Luangboriboon

Seriani

Fujiyama

2020

International Journal of Rail Transportation

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The Influence of the Density Inside a Train Carriage on Passenger Boarding RatePassenger alighting and boarding time is an important factor for crowded metros with high frequency operations. Some operators leave the train doors open until the last passenger boards or passengers voluntarily stop boarding, while others regulate the boarding process. To evaluate whether it is effective to regulate the passenger while boarding or not, this research explores how the density inside the train would influence the passenger boarding rate by conducting a laboratory experiment. The results showed that the passenger boarding rate increased as the density increased, up to approximately 2.5-3.0 passengers/m 2 . Beyond that point, our results suggested that the boarding rate may have decreased, but the evidence was not conclusive. It is deduced that, under the experimented density (up to around 5 passengers/m 2 ), the density does not have any apparent negative effects on the boarding rate, which implies that there is no strong evidence to recommend passenger boarding regulation.

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Section: Review Of Research On Dwell Timementioning

confidence: 99%

The influence of the density inside a train carriage on passenger boarding rate

Luangboriboon

Seriani

Fujiyama

2020

International Journal of Rail Transportation

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“…In [18], dwell time variability is considered by gathering real-world data to calculate the conflict probability in a timetable. A revision of train dwell time models is performed in [25]. They all gather real-world data from several installations, the work concludes that the amount of passengers getting on and off and waiting at platforms are the most influencing parameters.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy maximum capacity and occupancy time rate measurements in urban railway lines

Navarro

Fernández-Cardador

Cucala

2018

Eur. Transp. Res. Rev.

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Nowadays, railway transport capacity is an important bottleneck for many railway operators that face its ever-increasing demand. This poses a challenge to existing lines as operation under conditions close to saturation tends be unstable. Capacity in urban railway systems depends largely on dwell times at platforms, but capacity measures proposed in literature rarely include the uncertainty associated to these times. In this paper this uncertainty is modeled as fuzzy numbers and two new capacity measures are proposed: the Fuzzy Maximum Capacity and the Fuzzy Occupancy Time Rate. The proposed model makes use of a railway simulator that enables route compression to obtain the conflict-free compressed time of the section under study. Three practical capacity problems from the perspective of the railway traffic operator have been presented and solved.The new measures provide more information to the railway operator than the standard UIC method that does not include uncertainty regarding dwell times. Finally, the model has been applied to the section Gràcia-Sarrià, belonging to the Spanish railway operator FGC.

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“…The dwelling time consists of three parts [36]: the time before the doors open, the period of time during passenger exchange and the time prior to departure after the doors have closed. For the doors, the open and close times are fixed; studies estimated the dwelling time by modelling passengers' boarding and alighting process [37][38][39][40][41][42]. However, the number of passengers boarding and alighting trains in the off-peak hours is far less than that of the peak.…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

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Train Dwell Time Models for Rail Passenger Service

Cited by 12 publications

References 5 publications

The influence of the density inside a train carriage on passenger boarding rate

The influence of the density inside a train carriage on passenger boarding rate

Fuzzy maximum capacity and occupancy time rate measurements in urban railway lines

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

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