Train Traffic Optimization System to Minimize Stochastic Delays on Single Line Railway Track

Abstract

Abstract Optimization is a far-ranging research area applicable to many areas of science and technology. Increasing demand and complexities in the domain of transportation, day by day trigger many countries in the world to pay their attention to maximizing a rapid utilization of railway transportation due to its higher degree of cost-effectiveness and environment friendliness. However, the main consideration is to utilize successfully the existing infrastructure in order to provide better and enhanced service. There is no difference in Sri Lanka, compared with other countries, facing the same problems in transportation in order to provide efficient railway services. Providing better railway service in Sri Lanka is a big challenge than other countries due to managing stochastic delays. This is the main critical problems has to be addressed in the Sri Lankan context. Annual train delays due to stochastic events are considerably high in rate than in other countries. This directly effects on total productivity of the country itself intern. The main focus of this research is to reduce delay created by stochastic events, applying optimization technologies and low-cost devices. This research focused on minimizing stochastic delay on single line rail network. Firstly in this context, the critical issue faced was gathering accurate real-time train dynamics that need to trigger rescheduling process, just after each stochastic delay, created by stochastic events. Secondly, finding flexible and adaptable algorithm in order to search optimal running time and optimal departure time is not easy, and after literature reviews, Ant Colony Optimization was applied as a methodology to the problem domain to search optimal running time and departure time that need train to move from one node to another node within the undirected graph. The main concern of research was to minimize stochastic delay by providing a realtime assisting tool to train controllers that help them in the rescheduling process. Further, this system also provides a better platform to train drivers, station masters, and train controllers to minimize stochastic delay automatically by sending auto generated instructions and establishing communication between them. As a research outcome, Computer Based Real Time Train Re-scheduler (CBRTTRS) was designed to achieve more functionalities than above, such as off-time rescheduling, data mining, railway safety monitoring, and public acknowledgment, etc.