Analysis Of Spatial-temporal Characteristics Of Resident Travelling Based On GPS Trajectory Data And POI Data

The study on spatial-temporal characteristics of resident travelling is not only helpful to provide scientific decision support for urban planning,but also good for rational allocation of various urban resources.Traditionally,the study on travelling behavior characteristics of urban residents mostly adopts the way of travelling survey,which not only needs the cooperation of a large number of staff and respondents,but also is easy to be affected by the subjective factors of the respondents,resulting in the deviation between the results of the survey and the actual situation.With the development of Internet,mobile positioning equipment and cloud computing technology,it is possible to obtain massive trajectory data.The large amount of information contained in these trajectory data brings new opportunities for the study on travelling behavior of urban residents.Trajectory data mining costs less and is more reflective of the real situation because of its wide coverage and huge amount of data.Taxi has become an important means of transportation for residents because of its flexibility,convenience and all-weather operation.Additionally,because the parking place of taxi is basically determined by the passengers’ boarding and disembarking place,the trajectory data provided by the vehicle-borne GPS(Global Positioning System)terminal is easy to obtain,which can objectively reflect the spatial and temporal distribution pattern of the travelling behavior of urban residents.POI(Point of Interest)is another kind of important geographic data.Real geographical entities are abstracted as point data with location and attributes,which can not only visually describe the location of entities,but also mark their functional attributes.Compared with the traditional methods on discrimination of urban functional areas based on expert experience or remote sensing images,the method based on POI data is more objective,accurate and low-cost.It can better understand the spatial structure of the city and provide reasonable decision support for urban planning and infrastructure construction.There is a close relationship between travelling behavior of residents and urban spatial structure.A thorough understanding of the relationship will help to explore the potential driving forces behind residents’ travelling behavior,and then make a rational planning of the functional layout of the city.The taxi GPS trajectory data contains the dynamic travelling characteristics of passengers.The urban POI data and road network data can largely reflect the spatial structure and functional area layout of the city.This paper combines the above three forms of data to study the relationship between the traffic demand of residents and urban space.The O/D(Origin / Destination)points in taxi trajectory are extracted,and the extraction algorithm for taxi passenger’s travelling hot spot regional center is designed.Besides,the distribution of hot spot regional center under different time granularity is analyzed.Finally,the relationship between urban residents’ traffic demand and urban space is discussed based on the result of urban functional area division calculated by POI data.This paper mainly focuses on the following aspects:(1)Firstly,a decision rule of O/D points in GPS trajectory data is proposed,and the O/D points are extracted based on this rule.Based on the fact that the original and destination points in the trajectory data can better reflect the travelling characteristics of urban residents and the relationship between travelling behavior and urban spatial structure than the coordinate points during the trajectory,this paper analyses the segment of taxi trajectory from the perspective of taxi drivers,and divides the whole taxi operation process into two driving states--carrying passengers and noloading passengers as well as two parking states--boarding passengers and alighting passengers.According to the correlation between vehicle ID and passenger status,the O/D point decision rule is constructed that last data of each continuous empty car track is O point,and the last data of each continuous passenger track is D point.The O/D point of each car on that day is extracted and stored in the database with the unit of days.(2)Next,a quantitative identification algorithm of urban functional areas based on POI data is improved,and the identification of urban functional areas at block scale is realized.the method of dividing research units by using fixed size grid in the original algorithm is improved to the method of dividing research units by using road network data,which avoids the erroneous judgment results caused by human segmentation.Based on POI data,the criteria of urban functional areas are constructed,and the urban functional areas at block scale are identified.The validity and accuracy of the algorithm are proved by comparing the recognition results with the electronic maps of typical regions.(3)Thirdly,an algorithm for extracting the center points of urban travelling hotspot areas is proposed,and the spatial and temporal distribution characteristics of O/D points in GPS trajectory data are analyzed.For the purpose of analyzing and extracting the spatial and temporal characteristics of urban residents’ travelling,this paper researches the time distribution characteristics of O/D points in trajectory data by using mathematical statistics method at the first step.Then,aiming at the uneven distribution characteristics of urban data in central ring development,an improved method based on OPTICS clustering is proposed.Compared with the traditional clustering method of which the result covers the whole region within the first ring road,the method in this paper extracts the hotspot area centers of taxi passengers boarding and alighting events in the morning,noon and evening peak periods by further calculating the local density peak value of the original clustering results.The travelling spatial characteristics of residents are acquired.(4)In this paper,the extracted taxi O/D hotspot center and urban functional area identification results are superimposed and analyzed,and the basic characteristics of travelling,including the number of passengers,the travelling time distribution characteristics of passengers,and so on.What is more,taking Chengdu as an example,the similarities and differences of residents’ travelling characteristics between weekdays and weekends are analyzed,and the spatial and temporal characteristics of travelling behavior and the driving force behind it are further explored.