An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem

The single-track railway train timetabling problem (TTP) is an important and complex problem. This article proposes an integrated Monte Carlo Tree Search (MCTS) computing framework that combines heuristic methods, unsupervised learning methods, and supervised learning methods for solving TTP in discrete action spaces. This article first describes the mathematical model and simulation system dynamics of TTP, analyzes the characteristics of the solution from the perspective of MCTS, and proposes some heuristic methods to improve MCTS. This article considers these methods as planners in the proposed framework. Secondly, this article utilizes deep convolutional neural networks to approximate the value of nodes and further applies them to the MCTS search process, referred to as learners. The experiment shows that the proposed heuristic MCTS method is beneficial for solving TTP; The algorithm framework that integrates planners and learners can improve the data efficiency of solving TTP; The proposed method provides a new paradigm for solving TTP

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract 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

Delay Management and Dispatching in Railways

Passenger railway transportation plays a crucial role in the mobility in Europe. Since the privatization of the railway sector in the 90s, passenger satisfaction has become an important performance indicator in this sector. A key aspect for passengers is the reliability of transfers between trains. When a train arrives at the station with a delay, passengers might miss their connection if the next train departs on time. These passengers then prefer the connecting train to wait, but this introduces delays for many other passengers. Delay Management is a field in railway operations that deals with this situation. It determines whether a connecting train should wait for the passengers that arrive with a delayed train or should depart on time. In this thesis, we apply techniques from Operations Research to develop models and solution approaches for Delay Management. The objective in our models is the minimization of passenger delay. First, we extend the classical delay management model with passenger rerouting. This allows us to compute the exact delays for passengers. We develop an exact algorithm and several heuristics to solve this extension. Then, we incorporate the limited capacity of the stations in our models. Stations are the bottlenecks of the railway infrastructure, where delays of one train can easily propagate to other trains. When optimizing the wait-depart decisions, these secondary delays should be considered. We therefore develop an integrated model that includes headway constraints for trains on the same track in the station and an iterative approach that evaluates the timetable microscopically

Passenger Centric Train Timetabling Problem

The aim of this paper is to analyze and to improve the current planning process of the passenger railway service in light of the recent railway market changes. In order to do so, we introduce the Passenger Centric Train Timetabling Problem. The originality of our approach is that we account for the passenger satisfaction in the design of the timetable. We consider both types of timetable(s): cyclic and non-cyclic. The problem is modeled as a Mixed Integer Linear Programming (MILP) problem with an objective of maximizing the train operating company's profit while maintaining epsilon level of passenger satisfaction. By solving the model for various values of epsilon, the Pareto frontier is constructed. The analysis, based on an experiment using realistic data, shows that an improvement of passenger satisfaction while maintaining a low profit loss for the railway company can be achieved. A sensitivity analysis on passenger congestion illustrates a quantitative evidence that the non-cyclic timetables can account better for high density demand in comparison to cyclic timetables

Rolling Stock Rescheduling in Passenger Railways: Applications in short-term planning and in disruption management

Modern society is highly dependent on a reliable railway system for workforce mobility and easy access to the cities. However, the daily operations of a large passenger railway system are subject to unexpected disruptions such as rolling stock breakdowns or malfunctioning infrastructure. In a disrupted situation, the railway operator must adapt the timetable, rolling stock and crew to the modified conditions. This adaptation of resource allocations requires the solution of complex combinatorial problems in very short time and thus represents a major challenge for the involved dispatchers. In this thesis we develop models and solution methods for the rescheduling of the rolling stock during disruptions. The models incorporate service aspects (such as seat capacity), efficiency aspects (such as number of kilometers driven by the rolling stock), and process related aspects (such as the need for night-time relocation of rolling stock). The thesis contains applications of the developed models in three different contexts. First, we present a framework for applying the rescheduling models in the highly uncertain environment of railway disruption management, and we demonstrate the trade-off between computation time and solution quality. Second, we embed the rolling stock rescheduling models in a simulation framework to account for the dynamic passenger behavior during disruptions. This framework allows us to significantly decrease the delays experienced by passengers. Third, we apply the rescheduling models to real-life planning problems from the short-term planning department of the Netherlands Railways. The models lead to a considerable speed-up of the process and significant savings

Aspekte der Verkehrstelematik – ausgewählte Veröffentlichungen 2015

Mit dem sechsten Band der Schriftenreihe Verkehrstelematik wird ein Überblick über die intermodalen Forschungsthemen des Jahres 2015 der Professur für Verkehrsleitsysteme und ‑prozessautomatisierung der Fakultät Verkehrswissenschaften „Friedrich List“ der Technischen Universität Dresden anhand ausgewählter Veröffentlichungen gegeben. Sieben ausgewählte Artikel der Mitarbeiter, hauptsächlich veröffentlicht im Rahmen nationaler und internationaler Konferenzen, wurden dafür zusammengestellt. Die ersten Schwerpunkte bilden dabei die energieoptimale Steuerung und das Verkehrsmanagement im Schienenverkehr. Hier wird der Frage nachgegangen, wie Störungen des Bahnbetriebs im Echtzeit-Betriebsmanagement mit mathematischen Methoden begegnet werden kann. Als ein Ansatzpunkt wird das Erzeugen von robusten, stabilen und dabei auch energieeffizienten Fahrplänen diskutiert. Weiterhin wird versucht, im Rahmen des Betriebsmanagements mittels Konfliktlösungsalgorithmen operativ aktualisierte Fahrpläne so aufzubereiten, dass eine Umsetzung mit fahrzeugseitigen Fahrerassistenzsystemen ermöglicht und ein energieeffizienter Betrieb sichergestellt ist. Im zweiten Teil des Bandes wird gezeigt, wie die Methoden und Algorithmen der energieoptimalen Fahrweise und eines entsprechenden Fahrerassistenzsystems auf die Straßenbahn und auch den Bus übertragen werden können. Anschließend wird gänzlich auf den Individualverkehr fokussiert und der Frage der Reichweitenoptimierung elektrischer Fahrzeuge durch energieeffiziente Routing-Algorithmen unter Berücksichtigung von Echtzeit-Verkehrslagedaten nachgegangen. Wie im Schienenverkehr wird das Finden der optimalen Fahrstrategie auch hier durch Fahrerassistenzsysteme unterstützt

Multi-agent Near Real-Time Simulation of Light Train Network Energy Sustainability Analysis

As an attractive transportation mode, rail transit consumes a lot of energy while transporting a large number of passengers annually. Most energy-aimed research in rail transit focuses on optimizing the train timetable and speed trajectory offline. However, some disturbances during travel will cause the train to fail to follow the offline optimized control strategy, thus invalids the offline optimization. In the typical rail transit control framework, the moving authority of trains is calculated by the zone controller based on the moving/fixed block system in the zone. The zone controller is used to ensure safety when the travel plan of trains changes due to disturbance. Safety is guaranteed during the process, but the change of travel plan leads to extra energy costs. The energy-aimed optimization problem in rail transit requires ensuring safety, pursuing punctuality with considering track slope, travel comfort, energy transferring efficiency, and speed limit, etc. The complex constraints lead to high computational pressure. Therefore, it is difficult for the regional controller to re-optimize the travel plan for all affected trains in near real-time. Multi-agent systems are widely used in many other fields, which show decent performance in solving complex problems by coordinating multiple agents. This study proposes a multi-agent system with multiple optimization algorithms to realize energy-aimed re-optimization in rail transit under different disturbances. The system includes three types of agents, train agents, station agents and central agents. Each agent exchanges information by following the time trigger mechanism (periodically) and the event trigger mechanism (occasionally). Trigger mechanism ensures that affected agents receive necessary information when interference occurs, and their embedded algorithms can achieve necessary optimization. Four types of cases 5 / 128 are tested, and each case has plenty of scenarios. The tested results show that the proposed system provides encouraging performance on energy savings and computational speed

Hybrid model for proactive dispatching of railway operation under the consideration of random disturbances in dynamic circumstances

With the increasing traffic demand and limited infrastructure expansion, railway networks are often operated close to the full capacity, especially in heavily used areas. As a result, the basic timetable is quite susceptible to the operational disturbances, and thereby the propagation and accumulation of delays significantly degrade the service level for customers. To solve this problem, extensive researches have been conducted by focusing on the predefined robust timetables and the real time dispatching algorithm development. However, it has been widely recognized that excessive robust timetables may deteriorate the operating capacity of the railway network and the addition of recovery time and buffer time can be hardly implemented in the congested area. Moreover, most of the conventional dispatching algorithms ignore the further potential random disturbances during the dispatching process, which yield non-implementable dispatching solutions and, as consequences, inferior punctuality and repetitive dispatching actions. To this end, this project aims to develop a new algorithm for real-world dispatching process with the consideration of risk-oriented random disturbances in dynamic circumstances. In the procedure of this project, an operational risk map will be firstly produced: by simulating considerable amount of disturbed timetables with random disturbances generated in a Monte-Carlo scheme and calculating the corresponding expected negative impacts (average total weighted waiting time among all the disturbances scenarios), different levels of operational risk will be assigned to each block section in the studied railway network. Within a rolling time horizon framework, conflicts are detected with the inclusion of risk-oriented random disturbances in each block section, and the near-optimal dispatching solutions are calculated by using Tabu search algorithm. Finally, three indicators including total weighted waiting time, the number of relative reordering and average absolute retiming, are chosen to testify the effectiveness and advantages of the proposed dispatching algorithm. The sensitivity analysis of dispatching-related parameters is conducted afterwards to investigate the most appropriate relevant parameters for the specific studied area. The proposed algorithms are expected to be capable of automatically producing near-optimal and robust dispatching solutions with sufficient punctuality achieved

Modelling, solution and evaluation techniques for Train Timetable Rescheduling via optimisation

It is common on railways for a single train delay to cause other trains to become delayed, multiplying the negative consequences of the original problem. However, making appropriate changes to the timetable in response to the initial delay can help to reduce the amount of further delay caused. In this thesis, we tackle the Train Timetable Rescheduling Problem (TTRP), the task of finding the best combination of timetable changes to make in any given traffic scenario. The TTRP can be formulated as an optimisation problem and solved computationally to aid the process of railway traffic control. Although this approach has received considerable research attention, the practical deployment of optimisation methods for the TTRP has hitherto been limited. In this thesis, we identify and address three outstanding research challenges that remain barriers to deployment. First, we find that existing TTRP models for large station areas are either not sufficiently realistic or cannot be solved quickly enough to be used in a real-time environment. In response, a new TTRP model is introduced that models the signalling system in station areas in fine detail. Using a new set of real instances from Doncaster station, we show that our tailored solution algorithm can obtain provably optimal or near-optimal solutions in sufficiently short times. Second, we argue that existing ways of modelling train speed in TTRP models are either unrealistic, overly complex, or lead to models that cannot be solved in real-time. To address this, innovative extensions are made to our TTRP model that allow speed to be modelled parsimoniously. Real instances for Derby station are used to demonstrate that these modelling enhancements do not incur any extra computational cost. Finally, a lack of evidence is identified concerning the fairness of TTRP models with respect to competing train operators. New evaluation techniques are developed to fill this gap, and these techniques are applied to a case study of Doncaster station. We find that unfairness is present when efficiency is maximised, and find that it mostly results from competition between a small number of operators. Moreover, we find that fairness can be improved up to a point by increasing the priority given to local trains. This work represents an important step forward in optimisation techniques for the TTRP. Our results, obtained using real instances from both Doncaster and Derby stations, add significantly to the body of evidence showing that optimisation is a viable approach for the TTRP. In the long run this will make deployment of such technology more likely