Application of Data Clustering to Railway Delay Pattern Recognition

Cerreto, Fabrizio; Nielsen, Bo Friis; Nielsen, Otto Anker; Harrod, Steven

doi:10.1155/2018/6164534

Cited by 42 publications

(23 citation statements)

References 28 publications

(74 reference statements)

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“…To mitigate the error of assigning passengers to the early arriving train, an early slack is usually used [6], which allows passengers to be assigned to trains a few minutes before the scheduled arrival. In this example, a 2 minutes early slack would be sensible and aligns with early arriving trains observed in Denmark [21].…”

Section: A Assigning Trips To Trainssupporting

confidence: 60%

When Did the Train Arrive? A Bayesian Approach to Enrich Timetable Information Using Smart Card Data

Lemaitre

Andersen

Frellsen

2021

IEEE Open J. Intell. Transp. Syst.

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Smart card data from the Automatic Fare Collecting systems (AFC) and timetable information, such as Automatic Vehicle Location (AVL), are used in combination by practitioners and researchers to gain a deeper understanding of the public transit network. In some cases, AVL data are not available due to records being missing in the system. In such cases, people resort to the used schedule timetable such as General Transit Feed Specification (GTFS) to match smart card data to the transit network. Since delays or changes to the timetable are not contained in the scheduled timetable, it can result in wrong matches between the smart card data and the transit network. This paper shows how the uncertainty of arrival and departure times affects passengers to train assignments and proposes a method for estimating the missing arrival time of trains when the recorded timetable information is not available. The method uses the knowledge of how the tap-outs are distributed in a hierarchical, latent Bayesian model to predict the arrival times of trains. Evaluated on 15,136 train arrivals, the model can infer 70% of the arrivals times with an average error of 28 to 32 seconds depending on the station.

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Section: A Assigning Trips To Trainssupporting

confidence: 60%

When Did the Train Arrive? A Bayesian Approach to Enrich Timetable Information Using Smart Card Data

Lemaitre

Andersen

Frellsen

2021

IEEE Open J. Intell. Transp. Syst.

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“…The proposed open timetable used in Schweizerische Bundesbahnen (SBB) helps railway timetable planners to evaluate actual schedule adherence data and assist dispatchers in identifying delays [19]. Delay distributions show the number of trains in various groups and different delay patterns using real data with clustering methods [69].…”

Section: C: MLmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

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“…Markovic et al [3] propose a prediction model of arrival train delays with support vector regression in order to have a better understanding of the relationship between infrastructure and delays. Cerreto et al [8] apply k-means clustering to identify different delay profiles, providing new insights for managerial decisions.…”

Section: Related Workmentioning

confidence: 99%

Estimating Long-Term Delay Risk with Generalized Linear Models

Faverges

Russolillo

Picouleau

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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This paper presents an original methodology to estimate delay risk a few days before operations with generalized linear models. These models represent a given variable with any distribution from the exponential family, allowing to compute for any subject its own probability distribution according to its features. This methodology is applied on small delays (less than 20 minutes) of high-speed trains arriving at a major french station. Several distributions are tested to fit delay data and three scenarios are evaluated: a single GLM with a negative binomial distribution and two two-part models using both a logistic regression as first part to compute the probability of arriving on time, and a second part using a negative binomial or a lognormal distribution to obtain the probabilities associated with positive delay values. This paper also proposes a validation methodology to assess the quality of these probabilistic predictions based on two aspects: calibration and discrimination.

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Application of Data Clustering to Railway Delay Pattern Recognition

Cited by 42 publications

References 28 publications

When Did the Train Arrive? A Bayesian Approach to Enrich Timetable Information Using Smart Card Data

When Did the Train Arrive? A Bayesian Approach to Enrich Timetable Information Using Smart Card Data

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Estimating Long-Term Delay Risk with Generalized Linear Models

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