Analysis Of Spatial And Temporal Characteristics Of Residents’ Travel Behavior Based On Multi-source Data

With the acceleration of urbanization,the number of motor vehicles has increased year by year,and the traffic problems in cities have become increasingly serious.Although the corresponding optimization management measures have been taken,such as optimizing the urban road network structure and adjusting the urban space layout,the traffic problems of the city have been alleviated to some extent,but the solution to the problem still needs to find new ways and methods.This paper takes Jinan City as an example to analyze the time and space characteristics of urban residents’ travel,and then analyzes the behavioral rules of pedestrian travel and its influencing factors,in order to be able to have time and space characteristics.The proposed traffic optimization management program provides theoretical support and provides an effective complement to the solution to urban traffic problems.As one of the important modes of travel for urban residents,taxi GPS data can reflect the travel rules of residents to a certain extent.At the same time,with the popularity of map data application,map data is gradually applied to the transportation field,providing more comprehensive and effective data support for analyzing the characteristics of residents’ travel.This paper combines taxi GPS data with map data to conduct research.Firstly,data preprocessing is performed on the acquired data,and map matching is performed.Aiming at the low-frequency sample characteristics of GPS data,the TIVMM algorithm which combines the geometric analysis method and the topological analysis method is adopted,taking into account the observation probability and the transmission probability,and improving the map matching accuracy of the data.Secondly,statistical methods are used to analyze the temporal and spatial characteristics of residents’ travel.In the time dimension,the OD point pair extraction is carried out,the trend of the number of passengers getting on and off the taxis in different time periods is statistically analyzed,and the time rule of the residents’ travel time is mined;in the spatial dimension,the nuclear density clustering method is used to respectively operate the taxis.Clustering is performed at point D,and the differentiation of traffic volume in the starting and ending areas is analyzed to extract the spatial characteristics of urban residents’ travel.Thirdly,analyze the impact of POI on the travel needs of urban residents.The POI data is selected as the independent variable,and the taxi trip is the dependent variable.Through the multi-collinearity test,the six POI data are finally determined as explanatory variables,the GWR model is constructed and the parameters are estimated,and the influencing factors are analyzed according to the model results.Finally,the main contents of the paper are summarized,and further research directions are proposed for the existing problems.