Simulation-Based Genetic Algorithm towards an Energy-Efficient Railway Traffic Control

The real-time traffic control has an important impact on the efficiency of the energy utilization in the modern railway network. This study is aimed to develop an energy-efficient railway traffic control solution for any specified railway. In other words, it is expected to define suitable driving profiles for all the trains running within a specified period through the targeted network with an objective to minimize their total energy consumption. How to optimize the train synchronization so as to benefit from the energy regenerated by electronic braking is also considered in this study. A method based on genetic algorithm and empirical single train driving strategies is developed for this objective. Six monomode strategies and one multimode strategy are tested and compared with the four scenarios extracted from the Belgian railway system. The results obtained by simulation show that the multi-mode control strategy overcomes the mono-mode control strategies with regard to global energy consumption, while there is no firm relation between the utilization rate of energy regenerated by dynamic braking operations and the reduction of total energy consumption

Railway traffic scheduling with use of reinforcement learning

The reliability of railway traffic is commonly evaluated with train punctuality, where the\ud deviations of actual train arrivals/departures and train arrivals/departures published in the\ud timetable are compared. Minor train delays can be mitigated or even eliminated with running\ud time supplements, while major delays can lead to so-called secondary delays of other trains\ud on the network. Railway lines with high capacity utilization are more likely subject to delays,\ud since a greater number of trains means a larger number of potential conflicts and more\ud interactions between trains. Consequently, the secondary delays are harder to limit. Railway\ud manager and carrier personnel are responsible for safe, undisturbed and punctual railway\ud traffic. But unforeseen events can lead to delays, which calls for train rescheduling, where\ud new train arrivals and departures are calculated. Train rescheduling is a complex\ud optimization problem, currently solved based on dispatcher’s expert knowledge. With the\ud increasing number of trains the complexity of the problem grows, the need for a decision\ud support system increases. Train rescheduling is considered an NP-complete problem, where\ud conventional mathematical and computer optimization methods fail to find the optimal\ud solution, but artificial intelligence approaches have some measure of success. In this\ud dissertation an algorithm for train rescheduling based on reinforcement learning, more\ud precisely Q-learning, was developed. The Q-learning agent learns from rewards and\ud punishments received from the environment, and looks for the optimal train dispatching\ud strategy depending on the objective function

Decision making strategies for real time trains movement planning

Orientador: Fernando Antonio Campos GomideDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: O transporte ferroviário tem grande participação no transporte de cargas e passageiros em todo o mundo. No Brasil a malha ferroviária sofreu um processo de abandono e deterioração no período de 1960 a 1990. A partir de 1990 a privatização da rede ferroviária nacional iniciou uma retomada de investimentos e nos últimos anos à demanda por transporte ferroviário vem crescendo significativamente. É necessário, então, que os recursos da ferrovia sejam utilizados de maneira eficiente para atender a crescente demanda, o que exige planejamento estratégico, táctico e operacional. No nível operacional uma das principais etapas e também umas das mais carentes de ferramentas computacionais é o Planejamento de Circulação de trens. O processo operacional de uma ferrovia é dinâmico, sujeito a inúmeras interferências imprevisíveis e uma ferramenta computacional para o apoio ao planejamento de circulação de trens deve fornecer soluções com tempo de processamento compatível com essa realidade. Este trabalho propõe algoritmos para o planejamento de circulação de trens em tempo real, utilizando metodologias de inteligência computacional e conjuntos nebulosos. Um algoritmo objetiva decidir localmente a preferência entre trens concorrendo pelo uso de um segmento de linha singela de modo a seguir uma referência de percurso fornecida por algum algoritmo de otimização ou por um especialista. Outro algoritmo decide, além da preferência entre trens, a velocidade de percurso dos trens para mantê-los o mais próximo possível de suas referências. O terceiro algoritmo usa elementos de busca em árvore para obter uma solução para o planejamento de circulação de trens. É feito um estudo comparativo dos algoritmos aqui propostos e de algoritmos existentes na literatura. O estudo comparativo é feito a pm1ir de instâncias pequenas de problema de planejamento de circulação e uma instância que considera dados reais de uma ferrovia brasileira. Os resultados mostram que os algoritmos propostos obtêm soluções próximas às ótimas para as instâncias pequenas e soluções satisfatórias para o caso realAbstract: Railways plays a major role in freight and passenger transportation in the whole world. The Brazilian railway system has suffered a process of abandon and deterioration from 1960 to 1990. Since 1990 the privatization of the national railways brought new investments and in the last years the demand for railway transportation has increased significantly. Railway resources must be efficiently managed to match the increasingly transportation demand. This requires efficient strategic, tactical and operational planning. One of the main tasks at the operational planning level concerns train circulation and associated tools. Railway operation is a very dynamic process because trains are subject to many unexpected interferences. Computational tools to help trains circulation planning must provide solutions in a time range consistent with real-time needs. This work suggests algorithms for real-time train movement planning, using computational intelligence and fuzzy set theory methodology. One of the algorithms decides the preference between trains competing for a single line track at the same moment. The aim is to drive train circulation as dose as possible to reference trajectories supplied by human experts, global optimization algorithms or both. Other algorithm decides preference between trains and chose the velocity with which trains must travel to remain as dose as possible to its references. The third algorithm uses depth search algorithm to obtain a solution for train circulation problems. A comparative study considering the algorithms proposed herein and algorithms suggested in the literature. The comparative study is done using small railway system instances. Data of a major Brazilian railway is adopted to illustrate how the algorithms behave to solve larger instances. Results show that the algorithms here proposed obtain near optimal solutions for small instances and satisfactory solutions for the real caseMestradoAutomaçãoMestre em Engenharia Elétric