Nowadays, railways are confronted with numerous pressing problems, including capacity optimization, energy conservation, cost reduction and improving customer satisfaction. While the traditional railway is a very safe means of transportation, it still cannot meet all these requirements. Hence there are high interests in two available systems to overcome these challenges: Traffic Management System (TMS) and the optimized Automatic Train Operation (ATO). TMS is an efficient solution, which centralizes data to the railway infrastructure manager, while optimized ATO is an on-board approach available to minimize the loss of efficiency caused by manual operation. Until now, there are a lot of research studies about TMS and ATO as separate subjects. However, there is only poor research, which aims at closely coordinating the optimization strategies of TMS and ATO. This is because TMS is limited in the network scheduling optimization, while optimized ATO is centred on train behaviour optimization. Therefore this article presents the key idea of this research is to bridge the gap between TMS and ATO, to make progress towards combining the knowledge from both the infrastructure side and the train side. The primary objective is to bring an added value for achieving higher capacity, less energy consumption and higher operation quality by integration of Automatic Train Operation with centralized Train Management. In addition, an optimized ATO approach is introduced by integration of predictive fuzzy control with analytical hierarchy process. If this research has a positive response, it could bring about a new Computers in Railways XIII 39
vehicle scheduling and rescheduling are central challenges for the planning and operation of railways. even though these problems have been the subject of many research and development over several decades, railways still -with good reason -at the end of the day rely on well-trained and experienced personnel to provide practical solutions to these problems. over the last couple of years, novel techniques based on machine learning have been used to propose solutions to problems such as image and speech recognition that could not have been imagined previously. the aim of machine learning is to design algorithms that can improve automatically through experience. the experience possessed by traffic dispatchers is often their greatest tool. it is, therefore, not implausible that machine learning techniques could also be used to provide better automation or support to the railway scheduling and rescheduling problems. this article describes the results of a study conducted to evaluate the extent to which solutions to the scheduling and rescheduling problems could be improved using a machine learning technique called reinforcement learning. the solutions obtained using this technique are compared with solutions obtained using classical algorithmic and constraint-based search techniques. the initial results have been obtained under a simulated environment developed by Swiss Federal railways for the public Flatland challenge competition. this research has been ranked number 4 in this international competition. Although these initial results have been obtained under simulated conditions and using limited computational resources, they look promising compared to classical scheduling and rescheduling solutions and suggest that further work in this direction could be worthwhile
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.