Rail passenger service delays: An overview

Alwadood, Zuraida; Shuib, Adibah; Hamid, N. A.

doi:10.1109/beiac.2012.6226102

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Major service disruptions result from various conditions or factors such as accidents, problems in train operation, malfunctioning or damaged equipment, routine maintenance, construction, passenger boarding or alighting, and even extreme weather conditions [4], [6], [7]. Rail service disruptions directly affect scheduled timetable and inevitably cause train delay [8]. Significant train delay can eventually lead to service loss or even cancellation [7].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Lapamonpinyo

Derrible

Corman³

2022

IEEE Open J. Intell. Transp. Syst.

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Passenger train delay significantly influences riders' decision to choose rail transport as their mode choice. This article proposes real-time passenger train delay prediction (PTDP) models using the following machine learning techniques: random forest (RF), gradient boosting machine (GBM), and multi-layer perceptron (MLP). In this article, the impact on the PTPD models using Real-time based Data-frame Structure (RT-DFS) and Real-time with Historical based Data-frame Structure (RWH-DFS) is investigated. The results show that PTDP models using MLP with RWH-DFS outperformed all other models. The influence of the external variables such as historical delay profiles at the destination (HDPD), ridership, population, day of the week, geography, and weather information on the real-time PTPD models are also further analyzed and discussed.

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Section: Introductionmentioning

confidence: 99%

Real-Time Passenger Train Delay Prediction Using Machine Learning: A Case Study With Amtrak Passenger Train Routes

Lapamonpinyo

Derrible

Corman³

2022

IEEE Open J. Intell. Transp. Syst.

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“…Train punctuality is a very important aspect of the reliability of the service. The perceived utility of the system will definitely decrease if trains are late or cancelled very often [2]. According to Puong [3], dwell time is a key parameter of system performance, service reliability and quality in any mode of public transportation.…”

Section: Introductionmentioning

confidence: 99%

Train Dwell Time Models for Rail Passenger Service

San

Masirin

2016

MATEC Web of Conferences

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Abstract. In recent years, more studies had been conducted about train dwell time as it is a key parameter of rail system performance and reliability. This paper draws an overview of train dwell time models for rail passenger service from various continents, namely Asia, North America, Europe and Australia. The factors affecting train dwell time are identified and analysed across some rail network operators. The dwell time models developed by various researches are also discussed and reviewed. Finally, the contributions from the outcomes of these models are briefly addressed. In conclusion, this paper suggests that there is a need to further study the factors with strong influence upon dwell time to improve the quality of the train services.

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