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Accurate prediction of recoverable train delay can support the train dispatchers' decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections' influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively. Key words: high-speed railway, primary delay recovery, buffer time, data-driven approaches, random forest model. La prediction precise de retard de train recuperable peut appuyer la prise de decisions des regulateurs de train en matiere de revision d'horaire et d'amelioration de la fiabilite du service. Dans le cadre de cette etude, nous presentons les resultats d'un effort ayant pour but de developper un modele predictif du retablissement de retard primaire (RRP) utilisant des donnees d'exploitation des trains de la liaison ferroviaire a grande vitesse du Wuhan-Guangzhou (W-G). A cette fin, nous avons d'abord identifie les principales variables qui contribuent aux retards, y compris la periode tampon inactive, la periode tampon de parcours, l'ampleur du retard primaire et l'incidence des sections individuelles. Differents modeles sont appliques et calibres en vue de prevoir le retablissement du retard primaire. Les resultats de validation faite sur des ensembles de donnees d'essai indiquent que le modele de regression par foret d'arbres decisionnels (RFAD) surpasse les trois autres modeles de rechange, a savoir la regression lineaire multiple (RLM), la machine a vecteurs de support (MVS) et les reseaux de neurones artificiels (RNA) quant a la mesure de l'exactitude de prediction. Specifiquement, les resultats de l'evaluation montrent que lorsque la tolerance de prediction est moins de 1 minute, le modele de regression par foret d'arbres decisionnels (RFAD) peut realiser une exactitude de prediction jusqu'a 80,4 %, tandis que le niveau d'exactitude est de 44,4, 78,5 et 78,5 % pour les modeles RLM, MVS et RNA, respectivement. [Traduit par la Redaction] Mots-cles : liaison ferroviaire a grande vitesse, retablissement du retard primaire, periode tampon, approches fondees sur les donnees, modele de foret d'arbres decisionnels., Introduction Despite the availability of advanced communication and control technologies, high-speed railway (HSR) lines could still be subject to significant delay in their daily operations due to various unexpected disruptions [...]

Urban rail transit (URT) scheduling requires designing efficient timetables that can meet passengers' expectations about the lower travel cost while attaining revenue management objectives of the train operators. This paper presents a biobjective timetable optimization model that seeks maximizing the operating revenue of the railway company while lowering passengers' average travel cost. We apply a fuzzy multiobjective optimization and a nondominated sorting genetic algorithm II to solve the optimization problem and characterize the trade-off between the conflicting objective functions under different types of distances. To illustrate the model and solution methodology, the proposed model and solution algorithms are validated against train operation record from a URT line of Chengdu metro in China. The results show that significant improvements can be achieved in terms of the travel cost and revenue return criteria when implementing the solutions obtained by the proposed model., 1. Introduction Being fast, reliable, safe, and convenient, URT has been able to provide satisfactory trip services and thus mitigate urban traffic congestion in Metropolitan cities, e.g., Tokyo [1], Beijing [...]

This paper presents the results of acase study on the causes and effects of typical service disruptions in aHigh-speed rail (HSR) system in China–Wuhan–Guangzhou High-speed railway (WH-GZ HSR). With acause-specific approach, seven delay causalities leading to primary delays (PDs) are identified, and the properties and consequences of each primary delay (PD) factor is derived. The comparison of candidate distributional forms shows that the Log-normal distribution model can approximate better the length of all identified PDs. For each PD cause, the distribution of delay duration is estimated and tuned. Next, cause-specific distributional models for PDs severity are discussed. The models for the number of affected trains are presented in the form of inverse regression models with specific domains. Then, comparing five different kinds of candidate models, the results show that the Cubic is the best to approximate the distributions of total-affected time. [ABSTRACT FROM AUTHOR]