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Based on the Bayesian network (BN) paradigm, we propose a hybrid model to predict the three main consequences of disruptions and disturbances during train operations, namely, the primary delay (L), the number of affected trains (N), and the total delay times (T). To obtain an effective BN structure, we first analyze the dependencies of the involved factors on each station and among adjacent stations, given domain knowledge and expertise about operational characteristics. We then put forward four candidate BN structures, integrating expert knowledge, the interdependencies learned from real-world data, and real-time prediction and operational requirements. Next, we train the candidate structures based on a 5-fold cross-validation method, using the operational data from Wuhan-Guangzhou (W-G) and Xiamen-Shenzhen (X-S) high-speed railway (HSR) lines in China. The best performing structure is nominated to predict the consequences of disruptions and disturbances in the two HSR lines. Comparisons results show that the proposed model outperforms three other commonly used predictive models, reaching an average prediction accuracy of 96.6%, 74.8%, and 91.0% on the W-G HSR line, and 94.8%, 91.1%, and 87.9% on the X-S HSR line for variables L, N, and T, respectively.

The idea of roadway transportation as a shared economy and next-generation of mobility services is increasingly becoming a reality with the premise that the roadway system is a collection of scarce...

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). Wit...

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.

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.

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.

This paper presents a time supplements allocation (TSA) method that incorporates historical train operation data to optimize buffer-time distribution in the sections and stations of a published tim...

To solve bottlenecks and capacity shortages in railway corridors, it is necessary to consider passengers’ temporal-spatial preferences in conjunction with train dispatching decisions at the line planning stage. A design of the departure and stop plans which is based on the passengers’ preferences would guarantee a high quality of service. This paper presents a line planning optimization model for high-speed railway (HSR) operations that addresses both the operating costs of lines and passengers’ preferences through a bi-level integer programming (IP) problem. Using actual travel data from the Shenzhen–Changsha HSR in China, we investigate passengers’ travel behavior during different time periods and between several origin and destination (OD) pairs to characterize their spatial-temporal preferences. An IP model is then developed that maximizes the degree of passenger departure time satisfaction (DTS) at the higher level and the operating costs of lines at the lower level. Due to the complexity of the problem, a methodology is proposed to find a conflict-free solution for the proposed model to find train frequencies in each block and their stop plans at each station between the upper- and lower-level models. It is shown that a given passenger trip demand between an origin and a destination could become more flexible by analyzing its time characteristics and the DTS. Finally, a case study is presented to show the effectiveness of the model and the solution approach.

Train operations are subject to stochastic variations, reducing service punctuality and thus the quality of service (QoS). Models of such variations are needed to evaluate and predict the potential impact of disturbances and to avoid service punctuality reduction in train service management and timetabling. In this paper, through a case study of the Wuhan–Guangzhou (WH–GZ) high-speed rail (HSR), we show how a wealth of train operation records can be used to model the stochastic nature of train operations at each level, section and station. Specifically, we examine different distribution models for running times of individual sections and show that the Log-logistic probability density function is the best distributional form to approximate the empirical distribution of running times on the specified line. Next, we show that the distribution of running times in each section can be used to accurately infer arrival delays. Consequently, we construct the underlying analytical model and derive the respective arrival delay distribution at the downstream stations. The results support the correctness of the model presented and show that the proposed model is suitable for constructing the distribution of arrival delays at every station of the specified line. We show that the integrated distribution models of running times and arrival delays, driven by empirical data, can also be used to evaluate the QoS at individual track sections.

We study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint., NA

As a vital component of train operational control, train delay propagation pattern discovery is critically important for both railway controllers and passengers. In this study, we present a carefully designed deep learning model, called FCF-Net, that comprises fully connected neural networks (FCNN) and convolutional neural networks (CNN) for train delay propagation pattern recognition in railway systems. FCF-Net first uses a CNN component that handles train timetables as images to capture interactions of train events and an FCNN component to capture the influence of non-operational features separately; then it uses another FCNN component to combinedly learn the dependencies between operational and non-operational features. In addition, considering the imbalance of train delay data, a cost-sensitive technique that assigns different misclassification costs for different class was used to better deal with the imbalanced data. The main goal of the FCF-Net is to realize efficient and accurate train delay propagation pattern recognition by mining potential knowledge from train operation data. The predictive and computational performance of the model was tested and evaluated on data from two high-speed railway lines with different operational features in China. The results show that FCF-Net, once trained with sufficient data, outperforms conventional deep learning with common loss and other state-of-the-art deep learning models for train delay propagation pattern recognition, indicating its capability in knowledge discovery from train operation data. In addition, the computational results show that FCF-Net exhibits more efficient training process than existing state-of-the-art deep learning models.

The focus of this study was to conduct statistical analysis on primary delays in Wuhan–Guangzhou high-speed railway (HSR). The main statistics of primary delays were investigated, including delay causes, delay frequencies, delays’ temporal and spatial occurrences, affected number of trains, and delay recovery patterns. Models that can illustrate the primary delays duration and the number of affected trains were developed. Namely, the log-normal and the Weibull distributions are tested, and the results affirm that the former one can better approximate the duration of primary delays. Subsequently, a non-linear regression model to fit the distribution of the affected number of trains was presented. The temporal and spatial analysis of primary delays and capacity utilization show that there is a high degree of dependency between the periods with high delay frequency and capacity bottlenecks. Specifically, wherever there is a high capacity utilization rate, there is a high probability of delay occurren...

Improving the quality of service of a rail transportation system, and enhancing the operation safety require a quantitative understanding of the dynamic and stochastic characteristics of its train operations, especially those caused by unexpected disruptions. In this paper, based on historical train operation records, the characteristics of the primary delays (PDs) occurred on the Wuhan-Guangzhou (WH-GZ) high-speed railway (HSR) are investigated, with a specific focus on the underlying behavioral and physical factors. Alternative distribution models, including Log- normal, Weibull, and Gamma distributions, are calibrated and subsequently tested using hold-out data, to investigate the temporal and spatial distributions of PDs. The Kolmogorov- Smirnov (K-S) test results show that all the candidate models can fit the PD distribution curves, however, the Log-normal distributional form outperforms the other models. Subsequently, the model validation, carried out on the test dataset and the entire data, supported by the results obtained from the K-S two-sample test, indicate that the Log-normal model could satisfy the requirements with sufficient accuracy. Keywords–High-speed railway; Primary delay; Spatial and temporal distribution; Kolmogorov-Smirnov test; Train operation records

This paper presents a carefully designed train delay prediction model, called FCLL-Net, which combines a fully-connected neural network (FCNN) and two long short-term memory (LSTM) components, to capture operational interactions. The performance of FCLL-Net is tested using data from two high speed railway lines in China. The results show that FCLL-Net has significantly improved prediction performance, over 9.4% on both lines, in terms of the selected absolute and relative metrics compared to the commonly used state-of-the-art models. Additionally, the sensitivity analysis demonstrates that interactions of train operations and weather-related features are of great significance to consider in delay prediction models.

We study the problem of managing traffic on single-bottleneck roadways using a mobility permit (MP)-based method—a scheme that becomes increasingly relevant to shape the new mobility culture. To identify the main concerns of the involved stakeholders, we present an integrated framework that embeds our MP-based traffic management scheme, in which the mobility permit users are allowed to choose the options best matching their travel needs. Furthermore, to find the most effective market prices, the proposed permit allocation approach is integrated into an iterative auction process to achieve the best equilibrium state in terms of permit prices that is suitable for users, mitigating potential efficiency loss. Our computational results indicate the effectiveness of the proposed scheme as an alternative solution for permit-based mobility management on single-bottleneck roadways.

Reducing delays in the metro transit system improves passenger satisfaction and the operational efficiency of the system. However, current delay-recovery strategies tend to reduce delays rather than the operational costs of the metro company. We propose optimized delay-recovery strategies to reduce the delay and increase energy efficiency simultaneously in this study. We classified delays in historical data according to the relationship between the delay time and the following headway, and we present optimized delay-recovery strategies for different initial delay conditions. A multi-objective optimization model was used to minimize the cumulative delay time (CDT) and energy consumption. We added weight factors to the objective functions to reflect the preferences of decision-makers regarding delay recovery and energy savings. We applied the non-dominated sorting genetic algorithm-II (NSGA-II) to solve the proposed model and verified its performance using a case study of the Chengdu Metro. The results showed that the proposed model exhibited excellent performance regarding delay recovery and energy savings.

One of the main challenges arising in a high-speed railway (HSR) is predicting how fast a train, once delayed, can recover its operation. Accurate prediction of delay recovery in the downstream stations of a HSR line can help train dispatchers make adjustments to the timetables and inform the passengers of the expected delay to improve service reliability and increase passenger satisfaction. In this paper, we present the results of an effort to develop data-driven delay recovery prediction models using train operation records from the Centralized Traffic Control system (CTC) of Wuhan-Guangzhou (W-G) HSR in Guangzhou Railway Bureau. We first identified the main variables that contribute to delay, including total dwell (TD) time, running buffer (RB) time, magnitude of primary delay (PD), and individual sections' influence. Two alternative models, namely, multiple linear regression (MLR) and random forest regression (RFR), are calibrated and evaluated. The validation results on test datasets indicate that both models have good performance, with the RFR model outperforming the MLR in terms of prediction accuracy. Specifically, the evaluation results show that when the prediction tolerance is less than 3 minutes, the RFR model can achieve up to 90.9% of prediction accuracy, while this value is 84.4% for MLR model.

This paper proposes a full fuzzy TOPSIS as an optimization model for railway signaling system selection issue. The study is a new approach to such problems in railway domain. Analysis of the obtained results is explained and they indicate that the proposed approach as full fuzzy decision support model is a feasible and practical approach. The approach needs no defuzzification and it considers both the qualitative and quantitative. The fuzzy approach is employed to cope with imprecision in the assessment of the relative importance of criteria and alternatives. In proposed method given a linguistic verbal judgment matrix, fuzzy normalized judgment matrix is constructed by implementing a new developed normalization method. Then the fuzzy scores of the alternatives are determined based on solving of a non-linear programming model. Finally the ranks of the alternatives are determined through a fuzzy ranking method. The aforementioned algorithm was applied in one of the railway lines of Iran.