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Objective With the rapid development of technologies in urban rail transit equipment industry and the continuous maturation of their application, the automation level of urban rail transit operations is steadily increasing. FAO (fully automatic operation) systems, featuring high safety, high reliability and high efficiency, are being adopted by many cities. While improving operational safety and efficiency, FAO systems propose new requirements on train dispatchers in areas such as integrated analysis and intelligent decision-making, handling ability of complex operational scenarios, and extended management responsibilities within depots. To assist dispatchers in acquiring information more quickly and making decisions more efficiently, it is necessary to enhance the ATS (automatic train supervision) system with intelligent features. Method Based on the operational needs for secure and intelligent management, smart emergency decision-making, and intelligent emergency response on FAO lines, a design for FAO line IOC (intelligent operation and control) system is proposed, its framework, architecture, and external interfaces are introduced. A detailed analysis is conducted on the performance optimization design of functional modules, including integrated information expansion for train operation, power arm traffic control, fully automated timetable editing, passenger flow analysis and operational evaluation, fault and emergency command, and smart depot collaboration. Result & Conclusion The practical application of the IOC system on Shenzhen Metro Line 16 demonstrates that the intelligent expansion of FAO line ATS system not only improves line operational efficiency, but also significantly enhances the safety and reliability of FAO system. It markedly boosts the level of intelligent operation management, the system adaptability to complex operational environments, and the capacity for rapid response to emergencies.

Railway transport in the Czech Republic largely uses transit corridors, which are significantly more used than the rest of the railway network. This assumption leads to an increase in the safety of these lines and gradual double-tracking. All these aspects lead to the fact that the given lines are controlled from dispatching centres, while other lines are controlled from regional dispatch offices. The above parameters also apply to other non-corridor but still busy lines. It can therefore be concluded that this trend of remote control brings significant operational and technological advantages. The most visible benefit is the personnel saving of operational employees, but another significant benefit, which is not so obvious at first glance, is the benefit of a more efficient organization and management of railway transport. This impact brings the effect of smoother traffic, which manifests itself in a lower burden on the environment, thanks to fewer train starts and stops, or there will be no complete stop, but only a reduction in train speed. Furthermore, this influence is evident in the more frequent overtaking of trains not in railway stations, but in inter-station sections. All these advantages lead to an increase in an important operational indicator, which is the throughput of the track. [ABSTRACT FROM AUTHOR]

Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it is necessary to execute a train rescheduling procedure to rearrange the train paths of the out-of-gauge trains and the affected trains based on the fundamental timetable. For rescheduling the timetable, considering the blockades and the speed restrictions caused by the out-of-gauge trains, this paper proposed a time-space-state network representation for describing the out-of-gauge train rescheduling problem. A novel concept, speed allowance, is introduced to describe the train speed restriction due to the out-of-gauge trains. An integer programming model based on the time-space network is proposed to minimize the total train delay when running the out-of-gauge trains. The model can be solved by the rolling-time horizon approach for reducing computational time. A numerical example is conducted based on the conventional railway in China, demonstrating the solution performance of the model and the practical use of the methodology. Gurobi solver cannot obtain an optimal solution within 1 h when the planning-time horizon is greater than 120 min. With the rolling-time horizon approach, the rescheduled timetable can be obtained within 124 s for the 300 min planning-time horizon using 180 min rolling-time window. [ABSTRACT FROM AUTHOR]

Out-of-gauge trains are trains with loading freight that exceeds the loading limitation border. Considering collision avoidance, the out-of-gauge trains have speed restriction of their own, and the trains running on the parallel track. Therefore, it is necessary to execute a train rescheduling procedure to rearrange the train paths of the out-of-gauge trains and the affected trains based on the fundamental timetable. For rescheduling the timetable, considering the blockades and the speed restrictions caused by the out-of-gauge trains, this paper proposed a time-space-state network representation for describing the out-of-gauge train rescheduling problem. A novel concept, speed allowance, is introduced to describe the train speed restriction due to the out-of-gauge trains. An integer programming model based on the time-space network is proposed to minimize the total train delay when running the out-of-gauge trains. The model can be solved by the rolling-time horizon approach for reducing computational time. A numerical example is conducted based on the conventional railway in China, demonstrating the solution performance of the model and the practical use of the methodology. Gurobi solver cannot obtain an optimal solution within 1 h when the planning-time horizon is greater than 120 min. With the rolling-time horizon approach, the rescheduled timetable can be obtained within 124 s for the 300 min planning-time horizon using 180 min rolling-time window.

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.

The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown. [ABSTRACT FROM AUTHOR]

The boarding/alighting process at railway platforms is an important determinant of the railway system performance and depends on the characteristics of passengers, the layout of the platform, and the rolling stock. This research aims to increase the understanding of the process, providing a methodological approach to model the passengers’ behaviour when boarding at railway platforms. Adequate criteria were selected to define the so called “boarding cluster” and an easy mechanism was developed to select the boarding clusters. Passenger flow data collected at Bern railway station in Switzerland was used to test the proposed approach. The results show that (a) the clusters near the doors grow in the longitudinal direction with a rate of 6:1 between the length and width of clusters, and that (b) the growth curves rise quickly when clusters are still small, i.e., at the beginning of the boarding/alighting activity. Further research is needed to extend the validation of the model, considering other variables, such as critical pedestrian densities which occur at specific hot spots near obstacles at platforms. [ABSTRACT FROM AUTHOR]

Simulation methods are widely used in the field of railway planning and operations. Currently, several commercial software tools are available that not only provide functionality for railway simulation but also enable further evaluation and optimisation of the network for scheduling, dispatching, and capacity research. However, the various tools are all lacking with respect to the standards they utilise as well as their published interfaces. For an end-user, the basic mechanism and the assumptions built into a simulation tool are unknown, which means that the true potential of these software tools is limited. One of the most critical issues is the lack of the ability of users to define a sophisticated workflow, integrated in several rounds of simulation with adjustable parameters and settings. This paper develops and describes a user-based, customisable platform. As the preconditions of the platform, the design aspects for modelling the components of a railway system and building the workflow of railway simulation are elaborated in detail. Based on the model and the workflow, an integrated simulation platform with open interfaces is developed. Users and researchers gain the ability to rapidly develop their own algorithms, supported by the tailored simulation process in a flexible manner. The productivity of using simulation tools for further evaluation and optimisation will be significantly improved through the user-adaptable open interfaces. [ABSTRACT FROM AUTHOR]

General Electric Company (GE) partnered with Norfolk Southern Railroad (NS) to create and implement an optimization algorithm-based software system that dispatches thousands of trains in real time, increases their average speed, and allows NS to realize annual savings in the hundreds of millions of dollars. NS handles a range of rail traffic that includes intermodal, automobile transport, manifest freight, and passenger, all with unique priorities and scheduling requirements. Previously, dispatching for each geographic area was managed manually from regional dispatch centers and did not encompass a view of the entire rail network. The algorithm that we developed incorporates data about the properties of the rail networks (e.g., track layout, speed restrictions, height and weight restrictions), data about the trains (e.g., schedules, operating costs, train characteristics), and additional activities associated with train dispatching, such as crew changes and inspections. In doing so, we created a novel system to manage all train dispatching, increased the average speed of trains by two miles per hour, and decreased operating costs, while significantly improving schedule adherence and crew expirations. Every mile-perhour increase in average speed translates to $200 million savings in capital and operational expenses annually for NS. GE is currently implementing this system at two other railroads and is gaining additional important benefits from the project. [ABSTRACT FROM AUTHOR]

Advanced decision support for rail traffic control is significant for enhancing the safety and quality of railway transport service. Data-driven methods have shown powerful learning ability and wide extensibility for prediction, classification, and decision-making problems. In this paper, we propose a hybrid prediction model based on deep forest (DF) ensemble learning to analyze the two common rail traffic control actions, i.e., changing the dwelling times and running times. This basic concept is to mimic the decision-making of rail traffic controllers, providing them with advanced decisions/control actions using data-driven learning algorithms. According to the decision-making approach of rail traffic controllers, the learning process of the model is split into two stages, i.e., learning the type of action (ToA) and the number of changes (NoC) in the dwelling times and running times (i.e., how many dwelling times or running times have been changed compared with the planned ones). The first stage is a classification problem; thus, DF classifier with the synthetic minority oversampling technique (SMOTE) is employed to deal with imbalanced data. In the second stage, the DF regressor treats the NoC in the dwelling and running times as numerical variables and utilizes the information from stage one, i.e., the prediction results of the classification model, to make predictions. The proposed model is calibrated on train operation data from two high-speed railway lines in China. The experimental results and comparative analyses show that the proposed method provides advanced and timely decision support for controllers. These characteristics of the proposed model are imperative for supporting the dynamic decision-making of controllers to manage railway traffic. [ABSTRACT FROM AUTHOR]

Train movements on railway lines are generally controlled by human dispatchers. Because disruptions often occur, dispatchers make real-time scheduling and routing decisions in an attempt to minimize deviations from the official timetable. This optimization problem is called train dispatching. We represent it as a mixed integer linear programming model, and solve it with a Benders'-like decomposition within a suitable master/slave scheme. Interestingly, the master and the slave problems correspond to a macroscopic and microscopic representation of the railway, recently exploited in heuristic approaches to the problem. The decomposition, along with some new modeling ideas, allowed us to solve real-life instances of practical interest to optimality. Automatic dispatching systems based on our macro/micro decomposition-in which both master and slave are solved heuristically-have been in operation in several Italian lines since 2011. The exact approach described in this paper outperforms such systems on our test bed of real-life instances. Furthermore, a system based on another version of the exact decomposition approach has been in operation since February 2014 on a line in Norway. [ABSTRACT FROM AUTHOR]

The article focuses on evaluation of human reliability in the human - machine system in the railway industry. Based on a survey of a train dispatcher and of selected activities, we have identified risk factors affecting the dispatcher's work and the evaluated risk level of their influence on the reliability and safety of preformed activities. The research took place at the authors' work place between 2012-2013. A survey method was used. With its help, authors were able to identify selected work activities of train dispatcher's risk factors that affect his/her work and the evaluated seriousness of its influence on the reliability and safety of performed activities. Amongst the most important finding fall expressions of unclear and complicated internal regulations and work processes, a feeling of being overworked, fear for one's safety at small, insufficiently protected stations. [ABSTRACT FROM AUTHOR]

The paper presents a new urban public traffic supernetwork model by using the existing bus network modeling method, consisting of the conventional bus traffic network and the urban rail traffic network. We investigate the synchronization problem of urban public traffic supernetwork model by using the coupled complex network’s outer synchronization theory. Analytical and numerical simulations are given to illustrate the impact of traffic dispatching frequency and traffic lines optimization to the urban public traffic supernetwork balance. [ABSTRACT FROM AUTHOR]

Optimal alternative selection to address the emergency situation is critical for dispatcher group in Unattended Train Operation (UTO) to guide emergency process. It is difficult to provide the precise decision value under one criterion and to evaluate the emergency alternatives among multiple dispatchers. This paper presents a hybrid emergency decision-making method integrating fuzzy analytic hierarchy process (FAHP) described by linguistic terms with enhanced weighted ordered weighted averaging (WOWA) operator. The enhanced WOWA operator aggregates the preference matrices of multidispatcher through the constructed emergency response task model of dispatcher group in OCC. This calculation approach takes into consideration the relations of emergency tasks to derive the importance weights of dispatchers and integrates them into the ordered weighted averaging (OWA) operator weights based on a fuzzy membership relation. A case study of applying the method in an emergency of a train fire is given to demonstrate the feasibility and usefulness of the methods associated with the group multicriteria decision-making (GMCDM) theory in emergency management of UTO metro system. [ABSTRACT FROM AUTHOR]

The technology for controlling the distance between two trains is shifting from traditional fixed red, yellow and green signals on the infrastructure track circuits towards more and more dynamic systems, which are based on moving blocks, where the distance is computed according to real-time positioning, and the control of the distance is computed on-line. This is the case, for example, in the European Rail Traffic Management System (ERTMS), which proposes three different levels, from 1 to 3. This paper addresses the time-honoured problem of scheduling trains on a single track, in the light of recent results in robust team decision theory. The control model can be used in two modes: as a decision support tool for train dispatchers to evaluate the distance between trains in the current schedule, and as a planning tool to evaluate the effects of timetable changes. The main contribution of the paper is the application of a recent result in robust team decision theory to control noncritical train distances in moving blocks, such as in ERTMS Level 3. The case study is related to real data from an ERTMS simulation and controller software tool. [ABSTRACT FROM AUTHOR]

Trains' movements on a railway network are regulated by official timetables. Deviations and delays occur quite often in practice, demanding fast rescheduling and rerouting decisions in order to avoid conflicts and minimize overall delay. This is the real-time train dispatching problem. In contrast with the classic "holistic" approach, we show how to decompose the problem into smaller subproblems associated with the line and the stations. This decomposition is the basis for a master-slave solution algorithm, in which the master problem is associated with the line and the slave problem is associated with the stations. The two subproblems are modeled as mixed integer linear programs, with specific sets of variables and constraints. Similarly to the classical Benders' decomposition approach, slave and master communicate through suitable feasibility cuts in the variables of the master. Extensive tests on real-life instances from single and double-track lines in Italy showed significant improvements over current dispatching performances. A decision support system based on this exact approach has been in operation in Norway since February 2014 and represents one of the first operative applications of mathematical optimization to train dispatching. [ABSTRACT FROM AUTHOR]

Purpose: The train dispatching is a crucial issue in the train operation adjustment when passenger flow outbursts. During holidays, the train dispatching is to meet passenger demand to the greatest extent, and ensure safety, speediness and punctuality of the train operation. In this paper, a fuzzy passenger demand forecasting model is put up, then a train dispatching optimization model is established based on passenger demand so as to evacuate stranded passengers effectively during holidays. Design/methodology/approach: First, the complex features and regularity of passenger flow during holidays are analyzed, and then a fuzzy passenger demand forecasting model is put forward based on the fuzzy set theory and time series theory. Next, the bi-objective of the train dispatching optimization model is to minimize the total operation cost of the train dispatching and unserved passenger volume during holidays. Finally, the validity of this model is illustrated with a case concerned with the Beijing-Shanghai high-speed railway in China. Findings: The case study shows that the fuzzy passenger demand forecasting model can predict outcomes more precisely than ARIMA model. Thus train dispatching optimization plan proves that a small number of trains are able to serve unserved passengers reasonably and effectively. Originality/value: On the basis of the passenger demand predictive values, the train dispatching optimization model is established, which enables train dispatching to meet passenger demand in condition that passenger flow outbursts, so as to maximize passenger demand by offering the optimal operation plan. [ABSTRACT FROM AUTHOR]

Abstract: Railway traffic control by dispatchers in case of abnormality is critical to assure the service quality of a railway system’s operation. However, this unique professional knowledge often lies in the dispatcher’s mind. Therefore, this study aims to transform a train dispatcher’s expertise into a useful knowledge rule. The fuzzy Petri Net approach is adopted to formulate the decision rules of train dispatchers in case of abnormality as the basis for future development of a dispatching decision support system. The dispatching decision rules, factors, and possible options when perturbation happens are collected via expert interviews and literature reviews. This study discusses the abnormal scenarios, including centralized traffic control system failure, automatic train protection failure, and locomotive failure. A case study of a line section of Taiwan’s railway network is implemented and the empirical result could be used as a reference in railway dispatching in case of abnormality. [Copyright &y& Elsevier]

In December 2006, Netherlands Railways introduced a completely new timetable. Its objective was to facilitate the growth of passenger and freight transport on a highly utilized railway network and improve the robustness of the timetable, thus resulting in fewer operational train delays. Modifications to the existing timetable, which was constructed in 1970, were not an option; additional growth would require significant investments in the rail infrastructure. Constructing a railway timetable from scratch for about 5,500 daily trains was a complex problem. To support this process, we generated several timetables using sophisticated operations research techniques. Furthermore, because rolling-stock and crew costs are principal components of the costs of a passenger railway operator, we used innovative operations research tools to devise efficient schedules for these two resources. The new resource schedules and the increased number of passengers resulted in an additional annual profit of E40 million ($60 million); the additional revenues generated approximately E10 million of this profit. We expect this profit to increase to E70 million ($105 million) annually in the coming years. However, the benefits of the new timetable for the Dutch society as a whole are much greater: more trains are transporting more passengers on the same railway infrastructure, and these trains are arriving and departing on schedule more than they ever have in the past. In addition, the rail transport system will be able to handle future transportation demand growth and thus allow cities to remain accessible to more people. Therefore, we expect that many will switch from car transport to rail transport, thus reducing the emission of greenhouse gases. [ABSTRACT FROM AUTHOR]

Abstract: The incorporation of overtime considerations in the production and dispatching of ready mixed concrete (RMC) is a complex real world problem. In this research, the authors develop a network flow model for an RMC carrier according to Taiwan''s operation situation, that integrates RMC production scheduling and truck dispatching in the same framework, to decide on an optimal RMC supply schedule which also integrates overtime considerations. To build the model, the authors employ a time–space network technique to formulate the production of RMC and the truck fleet flows in the dimensions of time and space. The model is formulated as a mixed integer network flow problem with side constraints. To efficiently solve the problem a solution algorithm incorporating a mathematical programming solver is developed. The network flow model and the solution method are then evaluated using real operating data. The test results show that the model and the solution algorithm could be useful for actual operations. [Copyright &y& Elsevier]

This paper presents queueing models for predicting dispatching delays on single track low speed rail lines with widely spaced passing locations. Because of scheduling unpredictabilities, we assume Poisson arrivals of trains. Because of the slow transit speeds, we assume that trains traveling in the same direction can do so on close headways. We also assume siding capacity at passing locations is not limiting. Under these assumptions, we calculate expected delays on individual segments of single track and for segments of single track with an alternate route. [ABSTRACT FROM AUTHOR]