Normally, rail transit is operated on a fixed schedule, designed based on data from typical days. In practice, however, unexpected fluctuations in passenger flow and/or in facilities may occur, making the original timetable unrealizable or non-optimal. This calls for a decision support system (DSS) capable of assisting transit operators to effectively adjust the train schedule in real-time when the operation environment changes markedly. Such a system can be made possible by the latest developments in intelligent transportation systems. As the theoretical part of an operational DSS, this paper presents an optimization model, based on information available from advanced surveillance technology, to optimize the real-time train schedule for a specific time horizon. An approximation algorithm for this model is proposed and some computational results are reported and discussed.
Samenvatting