Research On Location And Capacity Of Electric Taxi Charging Station Based On Trajectory Big Data

With the development of society,environmental pollution problems have gradually become serious,and energy conservation,emission reduction,and green development have become key topics of common concern around the world.Electric vehicles have received widespread attention for their good environmental and social benefits such as zero exhaust emissions and low noise pollution.However,during the development of electric vehicles,on the one hand,the vehicle-to-pile ratio has not yet reached the expected plan.On the other hand,a large number of installed charging piles are idle and cannot be fully utilized.Consumers have "mileage anxiety" during the charging process of electric vehicles.It still exists and has not been alleviated.The above-mentioned all have exposed the actual problems in the location and capacity planning of electric vehicle charging stations,which greatly restricts the future development of electric vehicles.As a typical representative of public transportation,electric taxis have both the attributes of private transportation and the characteristics of public transportation.They have received extensive attention during the promotion of electric vehicles.In view of this,this article starts from the original data of electric vehicle driving trajectory,and is devoted to the empirical analysis of the location and capacity of electric taxi charging stations.Based on the in-depth analysis of the big data of taxi trajectory,this thesis uses the no-load peak period of the taxi as the charging hotspot time period.According to the operating characteristics of electric taxis,combined with the visualization capabilities of Arc GIS software,the no-load starting point is extracted as the demand point.Based on this,the site selection and capacity planning of electric vehicle charging stations are carried out.First,use the P-median model and the spatial calculation and statistical capabilities of Arc GIS software to select the location of the charging station,and analyze and evaluate the planning results of the charging station based on the area covered by the charging station.Next,choose the population distribution,road network distribution and ride point density of the study area as indicators for judging whether the charging station is attractive to taxi drivers.Using the Huff model,the comprehensive attractiveness and charging distance provide for the charging station.Analyze the number of service requirements.Secondly,the queuing theory is used to calculate the capacity of the charging station,which is calculated according to the service provided by the charging station,and the cost of different charging station construction plans is obtained.According to the comprehensive planning results,it is found that after the Huff model is used to optimize the station construction plan,the number of charging stations is reduced,and the coverage waste of charging station planning is reduced,which also contributes to reducing the planning cost of charging stations.Finally,based on this,the input-output indicators are established,and the SBM super-efficiency model in the data envelopment analysis is used to evaluate and select the station construction plan.The innovation of this thesis lies in: First,this article uses real taxi trajectories to analyze demand points,which makes up for the distortion of the previous calculation examples to study the planning results.second,use the spatial visualization capabilities of Arc GIS software to plan and analyze demand points according to the actual road network attributes,making the calculation results more intuitive and more convincing.Third,this article comprehensively considers the station construction investment,the total distance impedance in the planning results and the coverage of the charging station to the demand area,and uses the SBM super-efficiency analysis model to comprehensively analyze and select the charging station planning scheme.