Research On Urban Resident Travel Characteristics Identification And Influencing Factors Analysis Based On Multi-Source Data

The complex transportation network of big cities supports the flow of urban residents and materials,in which urban public transportation plays a fundamental role.Urban public transportation generally includes rail transit,taxis,buses and emerging shared bicycles.Residents can complete daily travel activities through these methods.However,due to the inherent development laws of cities,the distribution of residents and the distribution of transportation networks are usually uneven.This causes a mismatch between travel resources and demand and affects the operation efficiency of the city.The in-depth study of these issues needs to be carried out from the perspective of urban development layout and urban travel characteristics.In the past,it was relatively difficult to collect research data,and the lag of collected data was very serious.It has caused certain obstacles to the comprehensiveness and timeliness of the research.Therefore,based on the data of residents’ travel,this paper makes an in-depth mining on the spatial-temporal law and group structure of residents’ travel in the city.Based on the temporal and spatial regularity of travel distribution and the group structure,the relevant factors and spatial differences affecting the distribution structure of residents’ travel are studied based on the geographically weighted logistic regression model.The research results provide a new perspective on the optimization and adjustment of the urban transportation network and the reasonable allocation of transportation resources.The specific research in several aspects of this thesis is as follows:First,based on the travel data of the residents’ four modes of transportation(shared bicycles,buses,taxis,and subways),a detailed study of the temporal and spatial regularity of residents’ travel was carried out.Firstly,it analyzes the overall characteristics of the four modes of transportation.Secondly,it analyzes the characteristics of residents’ travel volume in time and space.Among them,the time characteristic mainly refers to the trend of the residents’ travel volume in a day with time,and the spatial distribution characteristics mainly include the difference in the spatial distribution of the residents’ travel volume and the difference in the distance of the residents’ travel.Second,based on residents’ travel data,the flow coding model is used to identify residents’ travel characteristics and make corresponding evaluations.Firstly,it analyzes the related methods of complex network research,and discusses the reasons for choosing the stream coding model as the basic model.Secondly,based on the classic information entropy theory,the working principle of the stream coding model is introduced.According to the characteristics of the traffic data,a clustering model suitable for recognizing the structure of spatial clusters is constructed based on this model.Then,the structural characteristics of residents’ trips were analyzed in depth.Finally,the evaluation index system of residents’ trip characteristics was established.It verifies the superiority of the model in group structure recognition.Third,starting from the reasons for the formation of the residents’ travel group structure,an in-depth analysis of the choice of transportation modes and influencing factors is carried out.The first is to construct the indicator structure of the model research.Secondly,ordinary least square regression model(OLS)and geographically weighted multiple logistic regression model(GWMLR)were used to model and fit the choice of residents’ travel mode.Then according to the output results of the model,a comparative analysis and evaluation were carried out,and the model and the result meeting the requirements were obtained.Finally,according to the output of the model,the characteristic analysis and case analysis of the influencing factors are carried out.It shows the concrete practical value of the model and results,which is beneficial to the planning of urban transportation network and the rational allocation of transportation resources.