Delay Adjustment Of Train Operation In Urban Rail

Rapid, convenient, large volume and low contamination are the advantages of urban rail transit. With the development of city, urban rail becomes an important means of relieving the heavy transportation. In normal circumstance, trains operate as the schedule timetable. However, the operation of trains may deviate from schedule timetable when an accident occurs. If the means to solve this problem is not suitable, a serious initial delay will appear. As urban rail transit has a simple line, short headways and large passenger flows, one delay may turn into a large scale of train delays, especially in a peak period. Therefore, study of delay adjustment and the means to handle the accident of urban rail transit system is essential. The main research in this paper is delay adjustment in urban rail transit. The detailed research contents are as follows:Firstly, adjustment of small scale delay is studied and the model of train regulation in small delay scale is established. When the scale of train delay is not too large, the influence is finite. The adjustment of the running time between stations and the dwelling time at stations will be adopted to eliminate the delays. As the features of urban rail transit, passenger flow has been added into our model, and three optimization objects are considered in this paper. Because of Particle Swarm Optimization has the feature of fast computing, we use it to figure out the optimization results.Secondly, adjustment of large scale delay is studied. Timetable rescheduling and regulation strategies are used to recover train operation to the normal condition. Train operation adjustment has the features of real-time computing, multiple optimization objects and complex characteristics of internal relations. System has multiple inputs and outputs, and many variables are nonlinear, time-varying and uncertainty. If a large scale of delay occurs, the impact on train running is large and it is difficult to get its adjustment strategy through an accurate model. Predictive control theory takes multi-step test, rolling optimization and feedback correction to figure out the result. Therefore, we use predictive control to get the train running adjustment strategy in case of massive delay.Finally, a simulation system of urban rail is designed, and the coping strategies are studied in the circumstance of rail failure or train failure happens. According to the features of urban rail transit, a model with considering the passenger flow is established to describe the process of train operation. Different strategies are figured out in different delay scale. Simulation results prove the validity of the adjustment strategy and the model. Finally, a simulation of urban rail transit system has been designed, and a study in response measures under some unexpected situation has also been introduced. The effects of measures were analyzed and evaluated.