Operation Optimization Of Urban Rail Transit Based On Passenger Flow Demand

Urban rail transit has developed rapidly into an important mode of urban transportation due to its punctuality,speed,comfort,safety,low pollution,and large volume,which plays a decisive role in the development of urban public transportation.With the rapid development of urban rail transit systems,passengers have put forward higher requirements for travel efficiency and service quality.In light of these,optimizing the efficiency of operation system is particularly important.Therefore,this paper first presents an exhaustive review of the previous studies on urban rail transit operation.Then,through theoretical modeling and case studies,this paper attempts to analyze and explore the key issues including passenger flow characteristics and operation plans,which are not only closely related to passengers,but also affect services quality significantly.Specifically,the detailed contributions of this work are summarized into four bullets as follows:(1)Temporal and spatial characteristics analysis of urban rail transit passenger flow.Firstly,this paper explored the factors influencing the passenger flow of the urban rail transit system from various aspects,such as social factors,ticket fares,road network planning and management.Then,based on the passenger flow data collected by the automatic fare collection system,the noise data were screened and eliminated,and the effective data were further analyzed as the basic data.Finally,the results of a case study based on Nanjing Metro Line 2 found that the passenger flow fluctuated significantly at peak periods in work days,and the proportion of commuting trips was large,but the passenger flow fluctuations were small on weekends;passenger flow varied due to the location of stations,the type of stations,and the land usage.(2)Passenger demand forecasting based on a hybrid method.Based on the Discrete Wavelet Transform and Seasonal Autoregressive Integrated Moving Average Model,a hybrid forecasting method DWT-SARIMA was introduced,which combined the advantages of Discrete Wavelet Transform and Seasonal Autoregressive Integrated Moving Average Model,and overcoming their shortcomings respectively.In addition,three key stages(decomposition stage,prediction stage and reconstruction stage)of the hybrid method were developed to forecast demand.Finally,a case study combined with the data of Yuantong Station of Nanjing Metro Line 2 was conducted.The results of three evaluation indicators(MAPE,VAPE,RMSE)showed that the proposed hybrid method was more accurate and reliable.(3)Multi-objective operation control optimization modeling based on demand.Firstly,based on wireless communication technology,this section explored the automatic train operation system of urban rail transit.A multi-objective operation optimization model matching passenger flow demand was developed to optimize operation,which incorporated the total waiting time of passengers and the total operation costs.Then,the elite preservation strategy and the "dynamic scoring" strategy in economics were introduced to improve the traditional genetic algorithm.Finally,the results of a case study based on Nanjing Metro Line 2 showed that the proposed multiobjective model and solution method could optimize the operation plans effectively.(4)Multi-train energy-saving optimization modeling considering regenerative braking.Firstly,based on the process and conversion principle of regenerative braking energy generation in urban rail transit systems,traction energy consumption,regenerative energy utilization and net energy consumption were calculated quantitatively.Then,a multi-train integrated optimization model incorporating timetable and speed profile optimization was constructed to minimize the net energy consumption.Besides,factors such as operation safety,comfort,and capacity were considered into the proposed model.Finally,catastrophic genetic algorithm was used to solve the proposed model,and a case study based on Nanjing Metro Line 2 was conducted to show the effectiveness of the model and method,it could provide managers with valuable energy-saving operation strategies.