A review of train delay prediction approaches

Spanninger, Thomas; Trivella, Alessio; Büchel, Beda; Corman, Francesco

doi:10.1016/j.jrtpm.2022.100312

Cited by 28 publications

(14 citation statements)

References 74 publications

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“…According to Carvalho et al (2021), RF, SVM, ANN and k-nearest neighbors (KNN) are the most frequently used algorithms to address airfreight delay and were also adopted in this study. Spanninger et al (2022) identified RF, SVM and ANN as the main data-driven classification techniques to predict transportation delay, producing single-value deterministic outputs. The nature of the delays defined by the deterministic approach according to Spanninger et al (2022) is that it "does not convey any information about their (un)certainty and ignore (or drastically simplify) the random occurrence of disturbances in the prediction horizon" as stochastic methods would perform.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Spanninger et al (2022) identified RF, SVM and ANN as the main data-driven classification techniques to predict transportation delay, producing single-value deterministic outputs. The nature of the delays defined by the deterministic approach according to Spanninger et al (2022) is that it "does not convey any information about their (un)certainty and ignore (or drastically simplify) the random occurrence of disturbances in the prediction horizon" as stochastic methods would perform. Due to the high predictive power, machine learning data-driven methods are one of the main techniques adopted in transportation delay.…”

Section: Literature Reviewmentioning

confidence: 99%

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Intelligent algorithms applied to the prediction of air freight transportation delays

Mendonça,

Oliveira,

Lima Jr

et al. 2023

IJPDLM

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PurposeThe objective of this paper is to evaluate whether the data from consignors, logistics service providers (LSPs) and consignees contribute to the prediction of air transport shipment delays in a machine learning application.Design/methodology/approachThe research database contained 2,244 air freight intercontinental shipments to 4 automotive production plants in Latin America. Different algorithm classes were tested in the knowledge discovery in databases (KDD) process: support vector machine (SVM), random forest (RF), artificial neural networks (ANN) and k-nearest neighbors (KNN).FindingsShipper, consignee and LSP data attribute selection achieved 86% accuracy through the RF algorithm in a cross-validation scenario after a combined class balancing procedure.Originality/valueThese findings expand the current literature on machine learning applied to air freight delay management, which has mostly focused on weather, airport structure, flight schedule, ground delay and congestion as explanatory attributes.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Intelligent algorithms applied to the prediction of air freight transportation delays

Mendonça,

Oliveira,

Lima Jr

et al. 2023

IJPDLM

View full text Add to dashboard Cite

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“…According to Spanninger et al (2022), data-driven train delay predictions are based on a direct prediction of delays at the desired station or point in time rather than explicit modeling of train-event dependency structure to capture traffic flow dynamics. Kecman and Goverde (2015) broadly categorised datadriven train delay models into global and local models.…”

Section: Modeling Approachesmentioning

confidence: 99%

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

Tiong

Palmqvist

2023

Preprint

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Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper proposes a set of equations conditional on the train location for analyzing train arrival delay factors at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between regression residuals caused by shared unobserved variables among equations. The railway data from 2017 to 2020 in Sweden are used to validate the proposed model and explore the effects of various factors on train arrival delays. The results confirm the necessity of developing a set of station-specific train arrival delay models to understand the heterogeneous impact of explanatory variables. The results show that the significant factors impacting train arrival delays are primarily train operations, including dwell times, running times, and operation delays from previous trains and upstream stations. The factors of the calendar, weather, and maintenance are also significant in impacting delays. Importantly, different train operating management strategies should be targeted at different stations since the impacts of these factors could vary depending on where the station is.

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“…According to the prediction duration, prediction can be divided into two categories: short-term prediction (0-30 min) and long-term prediction (>30 min) [2]. The problem is an event-driven predictions based on graph [34], and the network has typical characteristics such as spatiotemporal dependence, complexity, and community, which has become a bottleneck restricting the current high-speed train delay prediction. This bottleneck is mainly reflected in the following aspects.…”

Section: Introductionmentioning

confidence: 99%

An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

Zhang

Peng

et al. 2023

IEEE Trans. Fuzzy Syst.

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High-speed train delay prediction has always been one of the important research issues in the railway dispatching. Accurate and interpretable delay prediction can enable staff to implement preventive measures and scheduling decisions in advance, and guide relevant departments to cooperate in completing complex transportation tasks, so as to improve rail transit operations, service quality, and the efficiency of train operation. This article proposes a new interpretable model based on graph community neural network and time-series fuzzy decision tree. This model can well capture the influence of spatiotemporal characteristics, train community structure, and multifactor in high-speed train station delay prediction. Besides, the time series fuzzy decision tree based on multiobjective optimization and reduced error pruning can mine potential decision rules to improve the model's interpretability, transparency, and high reliability. Finally, we prove that the prediction effect of the proposed model is superior than the other seven state-of-the-art models and our model is interpretable.

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A review of train delay prediction approaches

Cited by 28 publications

References 74 publications

Intelligent algorithms applied to the prediction of air freight transportation delays

Intelligent algorithms applied to the prediction of air freight transportation delays

Analyzing Factors Contributing to Real-time Train Arrival Delays using Seemingly Unrelated Regression Models

An Interpretable Station Delay Prediction Model Based on Graph Community Neural Network and Time-Series Fuzzy Decision Tree

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