Urban Spatial Structure Information Mining Based On Floating Car Data And Travel Activity

With the expansion of the city scale,the difficulty of effective management and control of the city is increasing.Traditional research methods,especially data acquisition,are often inefficient and costly.In recent years,the emergence of GPS trajectory data has provided a new way and perspective for the study of urban spatial structure.The floating car carries the GPS equipment and can record the trajectory of its own driving.As a representative vehicle in the floating car,the taxi track records the travel information of a large number of people in the city,which reflect the changes in the needs of various types of cities and describe the various cities.The relevance between regions depicts the spatial structure of a city to a certain extent.How to use the appropriate technology to excavate the implicit information of urban structure from massive taxi track data is an important research topic.The hot spot of urban factors is the urban factor area with more travel times and more access flow,which reflects people's intensive travel behavior to some extent.On the basis of the data of the taxi,combining the basic geographic information data of Nanjing City,using the theory and method of data mining and spatiotemporal analysis,the discovery of the hot area of urban essential demand and the analysis of its spatio-temporal pattern are carried out.The temporal and spatial distribution characteristics of factor demand hotspots and the trend of quantity and trend of key elements of different functional types are studied.It is of practical significance for the understanding of urban spatial structure,and is of practical significance to urban traffic guidance and control.It has a reference value for the allocation and management of urban facilities resources,and has a commercial application value for location based services.The relationship between urban spatial structure and people's behavior in urban areas is closely related.The spatial structure of the city is divided into subregions,which have frequent movements in these subregions,while the interaction between subregions and subregions is relatively small.The use of the rich semantic information of taxi track data to build a complex network of urban space and community discovery can help to understand the dynamic characteristics of the urban spatial structure to a certain extent,which is of reference value for urban planning.The research work of this paper is based on the research data of Nanjing taxi track.Through the techniques and methods such as trajectory recovery,data mining,complex network and GIS spatio-temporal data expression,the spatial and temporal distribution rules of urban key demand hot spots are explored,and the urban spatial structure is divided from the perspective of people's travel activities.The article mainly studies from the following three aspects:(1)taxi track data is large and the data itself often have some errors that can not be used directly.In this paper,a technical framework of data preprocessing is set up to achieve the recovery of taxi trajectory.Starting from the original data,the trajectory data organization,data cleaning,trajectory filtering,path network updating based on trajectory point are completed,and a map matching algorithm is improved to restore the trajectory of the taxi and extract the track information.It provides a study for the distribution of urban elements and the extraction of urban structure.Accurate and effective data support.(2)taking the taxis information to extract the upper and lower guest points within the scope of the study area,using the spatial clustering based on density to extract the hot spot of the urban factors of different time periods,analyze the characteristics of the changes of the hot spots of different elements in one day,and compare the difference of the spatial and temporal distribution of the hot spots in the working days and the holidays.Same.According to the statistical analysis of the factors of diferent functional types,the change of demand for different functional urban elements,such as business,transportation,medical stock and residence,was identified,and the visual expression was carried out.(3)the urban spatial interaction network is constructed based on the taxi passenger segment information,and the complex network method is used to analyze the properties of the network.A new network community discovery algorithm,the entropy minimization algorithm,is used to divide the spatial interaction network,and the multilevel Nanjing city is found in different distance and different time periods.Dynamic urban structure,and further analysis of the sub area network,and find the impact of the important nodes of different levels of urban structure on the region.The main contributions of this study are two points,one is to improve the existing map matching algorithm,to improve the accuracy of trajectory recovery in the phase of trajectory data processing,and the two is to study the spatial and temporal distribution of the demand for different types of urban elements by clustering taxis on the up and down space of taxis,and to construct a spatial interaction network for urban nodes.The structure is divided into two parts,so as to provide some reference for mining some characteristics of urban spatial structure.