Joaquin Rodriguez

A detailed analysis of the actual impact of real-time railway traffic management optimization

Abstract Railway traffic is often perturbed by unexpected events causing delays, which may greatly propagate. Nowadays, dispatchers deal with delays trying to limit this propagation with scarce decision support tools. RECIFE-MILP is an optimization algorithm which may be used to support dispatchers decisions. In this paper, we illustrate the analysis performed in collaboration with the French infrastructure manager (SNCF Reseau) to assess the actual impact of the application of optimization in real-time railway traffic management. We perform a twofold experimental analysis on two French complex junctions characterized by intense mixed (passenger and freight) traffic. On the one hand, we assess this impact on scenarios specifically identified as relevant by SNCF Reseau. On the other hand, we tackle actually occurred scenarios and we compare the decisions made by RECIFE-MILP with those made by the dispatchers who actually faced the perturbation. These experiments show through simulation that the optimization may remarkably improve the way traffic is managed.

Timetable rearrangement to cope with railway maintenance activities

Abstract Maintenance activities on the railway infrastructure are necessary to maintain its functionality and availability. Commonly, the maintenance activities are planned first. Then, the timetable is elaborated respecting the unavailability periods caused by the former. However, sometimes unplanned maintenance activities have to be introduced at short notice, and the timetable must be rearranged to respect the new unavailabilities. In addition, specific trains may be necessary to perform maintenance activities, and they are typically not scheduled in the timetable. In this case, the timetable may need to be further rearranged to integrate the maintenance trains. In this paper, we propose a mixed-integer linear programming formulation that rearranges a timetable to cope with the capacity consumption produced by maintenance activities. It includes the consideration of maintenance trains and other specific constraints, such as temporary speed limitations. In this formulation, the rearrangement of the timetable is optimized based on a microscopic representation of both the infrastructure and the rolling stock. We assess three algorithms founded on this formulation on a real case study in the French railway network and we show their practical applicability.

RECIFE-SAT: A MILP-based algorithm for the railway saturation problem

Abstract Measuring capacity of railway infrastructures is a problem even in its definition. In this paper, we propose RECIFE-SAT, a MILP-based algorithm to quantify capacity by solving the saturation problem. This problem consists of saturating an infrastructure by adding as many trains as possible to an existing (possibly empty) timetable. Specifically, RECIFE-SAT considers a large set of potentially interesting saturation trains and integrates them in the timetable whenever possible. This integration is feasible only when it does not imply the emergence of any conflict with other trains. Thanks to a novel approach to microscopically represent the infrastructure, RECIFE-SAT guarantees the absence of conflicts based on the actual interlocking system deployed in reality. Hence, it can really quantify the actual capacity of the infrastructure considered. The presented version of RECIFE-SAT has two objective functions, namely it maximizes the number of saturation trains scheduled and the number of freight ones. In an experimental analysis performed in collaboration with the French infrastructure manager, we show the promising performance of RECIFE-SAT. To the best of our knowledge, RECIFE-SAT is the first algorithm which is shown to be capable of saturating rather large railway networks considering a microscopic infrastructure representation.

Ant colony optimization for train routing selection: Operational vs tactical application

Railway traffic is often perturbed by unexpected events. To effectively cope with these events, the real-time railway traffic management problem (rtRTMP) seeks for train routing and scheduling methods which minimize delay propagation. The size of rtRTMP instances is strongly affected by the number of routing alternatives available to each train. Performing an initial selection on which routings to use during the solution process is a common practice to simplify the problem. The train routing selection problem (TRSP) reduces the number of routings available for each train to be used in the rtRTMP. This paper describes an Ant Colony Optimization (ACO) algorithm for the TRSP, and analyses its application in two different contexts: at tactical level, based on historical data and with abundant computation time, or at operational level, based on the specific traffic state and with a limited computation time. Promising results are obtained on the instances of the Lille terminal station area, in France, based on realistic traffic disturbance scenarios.

Train timetabling during infrastructure maintenance activities

Maintenance activities are necessary to maintain the functionality of the railway infrastructure. Commonly, the maintenance activities are planned first. Then, the timetable is elaborated respecting the unavailability periods caused by the former. However, unplanned maintenance activities may have to be introduced at short notice, and the timetable must be rearranged. In addition, specific trains may be necessary to perform maintenance activities, and they are typically not scheduled in the timetable. In this case, the timetable may need to be further modified to integrate the maintenance trains. In this paper, we propose a mixed integer linear programming formulation that modifies a timetable to cope with maintenance activities. It includes the consideration of maintenance trains and other specific constraints, such as temporary speed limitations. In this formulation, the modified timetable is optimized based on a microscopic representation of both the infrastructure and the rolling stock. We test the proposed formulation on a real case study in the French railway network and we show its practical applicability.

Real-time railway traffic management optimization and imperfect information: preliminary studies

Railway traffic is often perturbed by unexpected events and appropriate train routing and scheduling shall be applied to minimize delay propagation. A number algorithms for this routing and scheduling problem have been proposed in the literature and they have been tested in different traffic situations. Nonetheless, their performance are almost always studied considering perfect knowledge of future traffic conditions, which is almost impossible to achieve in reality. In this paper, we propose an experimental analysis assessing the usefulness of these algorithms in case of imperfect information. We consider RECIFE-MILP as a traffic management algorithm and advanced or delayed train entrance times in the control area as the source of imperfect information. The results show that the application of traffic management optimization allows outperforming the first-come-first-served management strategy even if the actual traffic conditions are not perfectly known by the optimization algorithm.

A Constraint-Based Scheduling Model for Optimal Train Dispatching

Railway networks are faced to an increase demand of new services. This situation leads to train schedules close to the maximum capacity of the infrastructure. As the extension of the infrastructure is too expensive, an alternative solution is to improve traffic management in congested areas. This task can be formulated as an optimization problem which is a joint scheduling and allocation problems. The problem is considered to be a NP-hard problem which makes it difficult to solve using exact methods for a reasonable problem size. In this paper, we compare two heuristic methods for solving the problem. The first one uses a two-phase approach to perform independently resource allocation and scheduling. The second one performs incrementally the two kind of decisions at track section level, i.e. at each step, the algorithm performs decisions of allocation of a track section or of scheduling a pair of train runs on this section.Copyright