Train Operation Adjustment Of The Urban Rail Transit Based On Dwell Time Model

With the advantages of safety, high efficiency, convenience, reliability, low energy consumption and light environmental pollution, Urban rail transit has become an important part of urban public transportation. The actual operation of train is highly affected by the changes of passenger flow in urban rail transit, and the train’s delay often occurs because of the traffic disturbance. As urban rail transit has simple lines, large passenger flows and short headway, one delay may turn into a large scale of train delays, especially in the peak period. If the delay mishandled, passengers would be detained, the quality of traffic service, the efficiency of passenger transportation and even the whole line would be serious affected. Therefore, based on the influence of passengers on the train operation, the study of train operation adjustment under different scenarios has a great significance for urban rail transit. Considering the dwell time model, the optimization of train rescheduling is discussed. The main contents are as follows:Firstly, the train operation adjustment based on dwell time model under a small scale delay is studied. Considering the influence of passengers on train delay, a novel multi-objective train adjustment model is built by incorporating the dwelling time model of trains in each station. For the special features of urban rail transit and the influence of passengers, three adjustment objects are considered in the model. The target of stranded passengers’number is got by analyzing the relationship of the dwell time and passengers that getting on and off the train. The dwell time is regarded as dynamic values associated with traffic in the constraint condition of the problem. The improved Particle Swarm Optimization (PSO) algorithm is used to solve train operation adjustment problem with high computing efficiency.Secondly, the train operation adjustment based on dwell time model under a big scale delay is studied. The above proposed adjustment model and strategy are not suitable for the special features of a big scale delay. The train reschedule optimization is regarded as the timetable schedule optimization by using the method of the timetable rescheduling. Considering the influence of passengers, the initial train operation model is improved by combining the dwell time model. The multi-objective adjustment model is built based on three aspects including optimizing traffic train operation, the passenger travel and timetable rescheduling. It’s resolved by the improved PSO algorithm, the optimization results can make the train recover the normal operation fast.Finally, Train schedule adjustment software is developed according to the above proposed train adjustment model and strategy. The proper adjustment method is adopted according to the different delay scale. The target data are analyzed after the train delay is adjusted. The software is designed based on the.NET Framework which contains several function modules, namely the train diagram generation, the delay adjustment and the analysis of the target data. The train delay adjustment experiment is constructed to validate the functions of the software.