Timetabling Optimization Design Considering Train Circulation and Disturbances for High-Speed Rail System

• Main Authors: Nailah Firdausiyah, 費瑞拉
• Other Authors: Chien-Cheng Chou
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/93663145830819436605

碩士 === 國立中央大學 === 土木工程研究所 === 98 === Managing circulation of trains, including regular inspection, car cleaning times and turning back operations, has become important due to the scarcity of railway company resources. The Taiwan High-Speed Railway (THSR) already has cyclic patterns of daily train circulation, but these patterns have not been modeled yet. Moreover, based on a review of the literature, researchers in the railway field have never considered train circulation, especially in HSR systems, even though it is important. This research proposes a scheduling optimization model that has the capability to accommodate not only basic requirements such as railway topology, traffic rules, and user requirements, but also train circulation as well. Mixed integer and dynamic programming have been chosen to solve the model under CPLEX. In addition, railway systems are often characterized by high traffic density and heterogeneous traffic that is sensitive to disturbances; thus, rescheduling activity for updating an existing schedule in response to disruptions is needed. This research has applied sensitivity analysis in order to identify how disturbances propagate in the original timetable and which actions to decide in order to mitigate the impact instead of cancelling many trains. Assumptions as well as input and output values are configured by using real data from THSR,which used two lines, 128 services, 29 trains, and eight stations. The model has obtained a timetable result as good as the real timetable in a short computation time (that is, 0.10 second). Sensitivity analysis results could determine critical infrastructure and parameters that are sensitive to disturbances. Therefore, it could be a good simulation analysis for predicting the effect of disruptions on the timetable without doing real experiments such as trains being disordered and overtaken.