Efficient Train Management System - An AI Approach

The main mode of commuting in Mumbai is local train system. Around 8 million passengers travel daily by local trains. These trains are heavily crowded and create chaos with even a slight change in schedule. The number of trains running daily is so high that its difficult to accommodate more trains in the schedule and retain their performance. At the same time, the overcrowding issue needs to be resolved by improving performance and efficiency of the trains. Artificial Intelligence is growing rapidly and can be applied in many areas to make the existing system intelligent and better. This paper gives an overview of how Artificial Intelligence can be used to improve efficiency of the system by providing a dynamic timetable to make trains less crowded. This can be done through analysis of Historical Data to identify reasons for issues such as delay in trains, overcrowding, etc. On the basis of this analysis, a model for efficient train allocation and time table management is proposed. Furthermore, the viability and application of the model is also proposed in this paper.