| Rail transit is an indispensable mode of public transport in the process of modern urbanization,which can provide important support for urban development and residents’ travel.Train tracking interval is one of the important indicators to measure the efficiency of urban transport.Appropriate tracking interval can effectively improve the efficiency of urban transport,and the use of vehicle-vehicle communication can reduce train tracking time.This paper mainly studies the optimization problem of train tracking algorithm based on the principle of vehicle-vehicle communication.It uses CBTC system and analyzes the train tracking interval on the basis of moving block and obtains its calculation formula.It proposes to replace the general train tracking model with the virtual reconnection model based on vehicle-vehicle communication and add various influencing factors to establish a new train tracking model.After that,the switching time of train crossing over was analyzed,and the train hysteresis parameters at different speeds were optimized by genetic algorithm to reduce the switching time of train crossing over.Finally,the optimized train tracking model was obtained,and compared with the traditional train tracking model,it was proved that the new train tracking model could greatly reduce the train tracking time.The main conclusions are as follows:(1)The simulation analysis and comparison between RDBM and virtual reconnection model in CBTC system show that under the parameters set in this paper,the virtual reconnection model is improved by 88.8% and 71.4% in terms of tracking interval time and line passing capability,and the performance is better.(2)On the basis of this model,it is concluded that the speed of the front and rear trains will have an impact on train tracking.The simulation analysis of the tracking time of the front and rear trains at different ratios is carried out respectively,and it is concluded that the train tracking interval is the shortest when the speed of the front and rear trains is the same.The addition of slope factor and communication delay to the model makes it more abundant and more reliable and safe compared with the general model.(3)The process of over-zone switching and signal strength changes in the process of over-zone switching were analyzed,COST-231 HATA was selected as the wireless transmission model of trains,and the relationship between train hysteresis parameters and over-zone switching time was analyzed,and the over-zone switching algorithm was established for simulation.It was concluded that the higher the train speed,the smaller the hysteresis parameters.Then,a method is proposed to reduce the time of train crossing zone switching by changing the hysteresis parameters and genetic algorithm is used to optimize the hysteresis parameters.(4)Genetic algorithm was used to optimize train hysteresis parameters,and speed-communication satisfaction probability was selected as fitness function to obtain the dynamic hysteresis parameters close to the ideal condition.Compared with the fixed hysteresis parameters,it was concluded that the train crossover switching time under the optimized hysteresis parameters was shorter.The final optimization model was compared with the traditional model.Under the parameters set in this paper,it is concluded that the train tracking time is reduced to 63.0% of the general model,and the line passing capacity is increased by 46.9%. |
