Optimization for the efficient management of rail transport operations

Abstract

As rail transport becomes more important, the e±cient operation of it will be more required. We considered two issues on this regard, one is on collecting and analyzing information related to rail transport, and the other is on ¯nding solutions to operations based on the analysis of relevant information. For the first issue, we consider the estimation of passengers' connection. The estimation of passengers' connection lies at a culmination of Smart Card data analysis. Such an estimation has an enormous impact on planning and operation of a public transit network. We propose a method for this purpose by exploiting the alighting and boarding behavior of passengers from Smart Card data analysis. For the second issue, we consider the e±cient operation for a train-conflict resolution. In practice, a train-conflict resolution is decentralized around dispatchers each of whom controls a few segments in a global railway network with her rule-of-thumb to operational data. Conceptually, the global sub- optimality or infeasibility of the decentralized system is resolved by a network controller who coordinates the dispatchers and train operators at the lower layers on a real-time basis. However, such notion of a multi-layer system cannot be effectual unless the top layer is able to provide a global solution soon enough for the dynamic lower layers to adapt in a seamless manner. Unfortunately, a train-conflict resolution problem is NP-hard and an effective solution method traded off between computation time and solution quality has been lacking in literature. Thus, we propose a column-generation-based algorithm that exploits the separability of the problem. A key ingredient of the algorithm is an efficient heuristic for the pricing subproblem for column generation. The performance of the proposed algorithm is compared to the ones of the previous MIP-based heuristic by [41] and the priority-based heuristic by [36].