Francesco Corman

Reordering and Local Rerouting Strategies to Manage Train Traffic in Real Time

Traffic controllers regulate railway traffic by sequencing train movements and setting routes with the aim of ensuring smooth train behaviour and limiting, as much as possible, train delays. In this paper, we describe the implementation of a real-time traffic management system, called ROMA (Railway traffic Optimization by Means of Alternative graphs), to support controllers in the everyday task of managing disturbances. We make use of a branch-and-bound algorithm for sequencing train movements, while a local search algorithm is developed for rerouting optimization purposes. The compound problem of routing and sequencing trains is approached iteratively, computing an optimal train sequencing for given train routes and then improving this solution by locally rerouting some trains. An extensive computational study is carried out, based on a dispatching area of the Dutch railway network. We study practical size instances, and include in the model important operational constraints, including rolling stock and passenger connections. Different types of disturbances are analysed, including train delays and blocked tracks. Comparison with common dispatching practice shows the high potential of the system as an effective support tool to improve punctuality.

Optimal multi-class rescheduling of railway traffic

During real-time traffic management, the railway system suffers perturbations. The task of dispatchers is to monitor traffic flow and to compute feasible rescheduling solutions in case of perturbed operations. The main objective of the infrastructure manager is delay minimization, but the dispatchers also need to comply with the objectives of the train operating companies. This paper presents an innovative optimization framework in order to reschedule trains with different classes of priority, that can be computed statically or dynamically in order to include the needs of different stakeholders. An iterative train scheduling procedure is proposed in order to compute feasible train schedules for an ordered set of priority classes, from the highest one to the lowest one. At each step, the procedure focuses on the current priority class, preserving solution quality from the higher priority classes and neglecting lower priority classes in the optimization of train orders and times. The multi-class rescheduling problem is formulated via alternative graphs that are able to model precisely train movements at the microscopic level of block sections and block signals. Each step of the iterative train scheduling procedure is solved to optimality by a state-of-the-art branch and bound algorithm. The results show an interesting gap between single-class and multi-class rescheduling problems in terms of delay minimization. Each priority class is also evaluated in order to assess the performance of the different rescheduling solutions.

Evaluating Disturbance Robustness of Railway Schedules

Railway traffic is operated according to a detailed schedule, specifying for each train its path through the network plus arrival and departure times at its scheduled stops. During daily operations, disturbances perturb the plan and dispatchers take action in order to keep operations feasible and to limit delay propagation. This article presents a thorough assessment of the possible application of an optimization-based framework for the evaluation of different timetables and proactive railway traffic management over a large network, considering stochastic disturbances. Two types of timetables are evaluated in detail: “ regular” and “ shuttle” timetables. The former is the regular plan of operations for normal traffic conditions, while the latter plan is designed to be robust against widespread disturbances, such as adverse weather, track blockage, and other operational failures. A test case is presented on a large Dutch railway network with heavy traffic, for which we compute by microsimulation detailed train movements at the level of block signals and at a precision of seconds. When comparing the timetables, a trade-off is found between the minimization of train delays, due to potential conflicts and due to delayed rolling stock and crew duties, and the minimization of passenger travel time between given origins and destinations.

Rescheduling models for railway traffic management in large-scale networks

In the last decades of railway operations research, microscopic models have been intensively studied to support traffic operators in managing their dispatching areas. However, those models result in long computation times for large and highly utilized networks. The problem of controlling country-wide traffic is still open since the coordination of local areas is hard to tackle in short time and there are multiple interdependencies between trains across the whole network. This work is dedicated to the development of new macroscopic models that are able to incorporate traffic management decisions. Objective of this paper is to investigate how different levels of detail and number of operational constraints may affect the applicability of models for network-wide rescheduling in terms of quality of solutions and computation time. We present four different macroscopic models and test them on the Dutch national timetable. The macroscopic models are compared with a state-of-the-art microscopic model. Trade-off between computation time and solution quality is discussed on various disturbed traffic conditions.

Railway line capacity consumption of different railway signalling systems under scheduled and disturbed conditions

This paper evaluates the capacity consumption on a Dutch railway line both under scheduled and disturbed traffic conditions. For the scheduled condition the standard UIC compression method is used, while the computation of capacity consumption under disturbed conditions requires multiple simulation runs via a Monte Carlo simulation set up. For the analysis we use the optimization-based train dispatching system ROMA that combines the alternative graph formulation of train rescheduling with blocking time modelling of the signalling constraints. For this study ROMA is extended to various signalling/ATP systems so that the braking behaviour of hindered trains is accurately simulated in the different configurations. In addition, ROMA computes the compressed timetable with conflict-free train paths without rescheduling. For the disturbed scenarios, we consider a case study of the Utrecht-Den Bosch line equipped with different signalling/ATP systems. Two traffic control scenarios are considered for the disturbed scenarios: a first come first served rule and an advanced branch-and-bound algorithm. The results show that the scheduled capacity consumption improves from the Dutch NS’54/ATB to ETCS Level 2 with short blocks. In presence of delayed operations, the capacity consumption declines for NS’54/ATB, since trains have to brake and run at lower speeds. With ETCS, there is a considerable gain in terms of capacity consumption and punctuality compared to NS’54/ATB, since the braking distances decrease when delayed trains run at lower speeds, having a stabilizing effect on headway times, delay propagation and throughput.

Effectiveness of dynamic reordering and rerouting of trains in a complicated and densely occupied station area

Abstract Railway traffic experiences disturbances during operations that cause conflicts between train paths or even deadlock situations. Dispatchers need actions to restore feasibility and limit spreading of delays through the network. To help them in such a task, the dispatching support tool ROMA (Railway traffic Optimization by Means of Alternative graphs) has been implemented in a laboratory environment. This paper reports on enhancements to the underlying train dispatching model as well as to the solution algorithms studied in order to tackle the increased complexity of busy stations with multiple conflicting paths and high service frequencies. Advanced train reordering and rerouting techniques are compared with straightforward rules and the current approach in the Netherlands. Extensive computational studies based on accepted statistical distributions of train delays for Utrecht Central Station assess the effectiveness of the ROMA tool in terms of solution quality and computation time.

A Review of Online Dynamic Models and Algorithms for Railway Traffic Management

Railway timetables are developed to make operations robust and resilient to small delays. However, disturbances perturb the daily plan, and dispatchers adjust the plan to keep operations feasible and to limit delay propagation. Rescheduling approaches aim at updating the offline timetable at best, in the presence of delays. We present a survey of the recent approaches on online railway traffic rescheduling problems, which exhibit dynamic and stochastic (or, at least, not completely deterministic) aspects. In fact, while online static rescheduling has reached a wide degree of dissemination, much is still to be done with regard to the probabilistic nature of the railway traffic rescheduling problems, and also how to best take uncertainty into account for future states. Open challenges for the future research are finally outlined.

A variable neighbourhood search for fast train scheduling and routing during disturbed railway traffic situations

This paper focuses on the development of metaheuristic algorithms for the real-time traffic management problem of scheduling and routing trains in complex and busy railway networks. This key optimization problem can be formulated as a mixed integer linear program. However, since the problem is strongly NP-hard, heuristic algorithms are typically adopted in practice to compute good quality solutions in a short computation time. This paper presents a number of algorithmic improvements implemented in the AGLIBRARY optimization solver in order to improve the possibility of finding good quality solutions quickly. The optimization solver manages trains at the microscopic level of block sections and at a precision of seconds. The solver outcome is a detailed conflict-free train schedule, being able to avoid deadlock situations and to minimize train delays. The proposed algorithmic framework starts from a good initial solution for the train scheduling problem with fixed routes, obtained via a truncated branch-and-bound algorithm. Variable neighbourhood search or tabu search algorithms are then applied to improve the solution by re-routing some trains. The neighbourhood of a solution is characterized by the set of candidate trains to be re-routed and the available routes. Computational experiments are performed on railway networks from different countries and various sources of disturbance. The new algorithms often outperform a state-of-the-art tabu search algorithm and a commercial solver in terms of reduced computation times and/or train delays. HighlightsVariable neighbourhood search algorithms are proposed for efficient railway traffic control.New neighbourhood search strategies for real-time train re-routing are developed.Practical railway test cases from various European countries are investigated.The new algorithms compute good quality solutions in a short computation time.The new algorithms often outperform a state-of-the-art tabu search algorithm.

Centralized versus distributed systems to reschedule trains in two dispatching areas

Railway dispatchers are in charge of rescheduling trains during operations in order to limit propagation of disturbances occurring in real-time. To help the dispatchers in such task, an advanced decision support system, ROMA (Railway traffic Optimization by Means of Alternative graphs), has been recently implemented to optimize railway traffic within a single dispatching area. This paper presents a novel distributed optimization system to control trains running in a Dutch railway network that is divided into two complex dispatching areas with dense traffic, each one controlled by a single dispatcher with the support of a local ROMA. A coordination level is introduced in order to manage the interaction among the two local ROMAs. An extensive computational assessment of the centralized and distributed systems is performed by using simple and advanced train scheduling algorithms, including dispatching rules adopted during operations. The effectiveness of the distributed system is shown in terms of computation time and delay minimization for practical statistical entrance delay distributions and in presence of an increasing number of blocked platforms in the main station area.

Rescheduling Dense Train Traffic over Complex Station Interlocking Areas

Railway rescheduling is the task of restoring feasibility in case of disturbances and limiting the propagation of delays through a railway network. This task becomes more difficult when dealing with complex interlocking areas, since operational rules constrain the passage of trains through short track sections. This paper presents a detailed microscopic representation of the railway network that is able to tackle the complexity of a station area with multiple conflicting routes and high service frequency. Two alternative graph formulations are presented to model the incompatibility between routes: one based on track sections and another based on the aggregation of track sections into station routes. An extensive computational study gives useful information on the performance of the two formulations for different disturbance scenarios.