Spatial Autocorrelation Analysis And Modeling Of Taxi Passenger Flow Based On GPS Data

With the rapid development of the economy,the development of the city is also getting faster and faster,and the population is also increasing.The traffic problems that come with it have become major problems for major cities.The continuous breakthrough of modern science and technology and the development of information technology has led human beings to enter the era of big data.As GPS applications have gradually expanded into the civilian domain,a large number of residents' travel trajectory information has been generated.Compared with traditional information based on residents' travel surveys,this information is more accurate and larger in number,and it can provide a data foundation for large-scale analysis of residents' travel behavior characteristics.The analysis of residents' travel behavior characteristics can provide solid data to support for a city's traffic planning.Taxi is an important part of the urban transport system,and the forecast of its demand can provide convenience for the traffic department to control the taxi industry.The introduction of spatial regression models in predicting taxi demand can more accurately predict taxi demand.This paper takes the taxi GPS data of Xi'an as the research object,analyzes the spatial and temporal characteristics of residents' travel through the processing of taxi GPS data,and verifies the spatial autocorrelation of Xi'an taxi passenger flow,and then selects several factors that have a great influence on the passenger flow of the taxi,establishes respectively and compare the linear regression model,the spatial lag model and the spatial error model.The main research content is as follows:(1)preprocess GPS taxi data of Xi'an to obtain valid data that can be used to analyze the temporal and spatial characteristics of residents' travel behavior;(2)analyze the processed data to obtain the spatial and temporal characteristics of Xi'an residents' travel behavior;(3)explain the theoretical part of the autocorrelation of spatial autocorrelation and test methods,and then illstrate the definition of spatial weight matrix.Finally,use practical examples to prove the spatial correlation of Xi'an taxi passenger flow;(4)select the influencing factors that have a greater impact on the taxi passenger flow,and then use the linear regression model,spatial lag model and spatial error model to conduct passenger flow modeling analysis,and finally compare the three models to draw relevant conclusions.The research result indicates:(1)Residents travel behavior not only has regularity in time,but also has certain regularity in space.On the time,in terms of total daily trips,the total number of trips on rest days is significantly higher than the total number of trips on weekdays.The number of trips on weekdays in per hour is generally lower than the number of departures on rest days.And than the morning and evening rush hour clearly show the characteristics of longer travel time.Spatially,the Bell tower,Xiaozhai and Xi'an station have always been the hot spots for departure and arrival.Other areas become hot spots with timeliness.Overall,the distribution of passenger flow has a greater connection with the nature of land use.(2)The taxi passenger flow in downtown Xi'an has a spatial autocorrelation as a whole.From a local point of view,the H-H and L-L cluster areas are relatively stable in space.The H-H cluster area is basically located in Space City and other places in Nanjiao,Power Plant West Road in Dongjiao,High-tech Industrial Park in Central,and Zhangjiabu in the north.And the L-L cluster area.locates mainly in the urban areas.And the clustering lead fluctuating changes of the spatial autocorrelation with time.(3)When there is a spatial correlation in the passenger flow,each variable in the spatial regression model,compared with the linear regression model,has better explanatory power for the dependent variable and better equation fitting.The four variables of the road network density,the number of bus stops,the number of parking lots,and the number of retail outlets all contribute to the increase in taxi passenger flow.