Optimal operation for rail transit systems under advanced information

Rail transit is normally operated on a fixed train schedule (timetable), de signed based on data from typical days. In practice, however, unexpected fl uctuations in passenger flow and/or in facilities may occur, making the ori ginal schedule unrealizable or non-optimal. This calls for a real-time Deci sion Support System (DSS) which can assist transit operators to effectively adjust the train schedule on the real-time basis when the operation enviro nment changes markedly. Such a system can be made possible by the latest de velopments in intelligent transportation technologies. As the theoretical p art of an operational DSS, this paper presents an optimization model, based on information available from the advanced surveillance technologies (e.g. the current situation of facilities, the short-term prediction of passenge r flow, etc.), to optimize the real-time train schedule for a specific time horizon. An approximation algorithm for this model is proposed and some co mputational results are reported.