Research On Travel Laws And Portraits Of Wuhan Private Cars Based On Trajectory Data

With the acceleration of urbanization and the improvement of people's living standards,the travel modes of residents have changed dramatically.In the field of transportation,due to the development of the Internet of Things and cloud storage technology,the collection of traffic data by humans in the past has gradually turned to automatic collection and intelligent processing,especially the time and space trajectory data of vehicle travel collected by intelligent vehicle-mounted equipment.These spatial-temporal trajectory data are different from other types of data.The differences are mainly reflected in the multi-dimensional nature of the data structure and the tight coupling of the spatiotemporal dimensions of the data.Although the preprocessing of the data is often more complicated,it is widely used in the field of geographic information and intelligent transportation.It has important application value in urban traffic management,urban planning and layout,and residents' travel decisions.The previous research on the mining of vehicle trajectory data has mainly focused on the development of floating vehicle data.Floating vehicle travel is affected by many different passengers.Compared with floating vehicles,private cars not only account for a much higher percentage of vehicle travel overall.Floating cars,and private car trips can better reflect the driver's personal wishes.How to effectively use private car trajectory data and analyze and analyze the underlying laws and information contained in them,is of great significance for urban transportation and personal travel.The research content in this paper can be divided into two parts.The main research object is the time and space trajectory data of private cars obtained from Tencent maps.The research objects are analyzed from the whole and the individual.First,on the analysis of the overall travel trajectory,in terms of overall travel rules,the average number of weekend trips is higher than that of working sunrises.Specifically,during the day of the working day,the morning peak trips are much higher than other times,and the distribution of travel time during the 24-hour period on the weekend is relatively flat,which is due to the different trends on weekdays and weekends.In terms of overall travel time and space characteristics,from the perspective of the inflow and outflow between morning and evening regions and the trajectory hotspots in Wuhan,each administrative region has significantly different functional responsibilities,which reflects the separation of occupation and residence in Wuhan to a certain extent.Based on the distribution of Wuhan's travel hotspots and POI hotspots,Wuhan's travel hotspots show a trend of circular outward spread with the main urban area as the center.The Pearson coefficient indicates that the travel hotspot indicators and POI hotspot indicators are strongly positive.Correlation: By analyzing the average travel speed distribution of roads in Wuhan city by combining road network data,it is possible to find areas that are prone to congestion in cities.The Moran index of each time period indicates that the average travel speed of the regional area in the city has strong spatial relationship.Second,the classification decision tree constructed by the POI data is used to extract the resident information label of a single driver and its travel purpose label to improve the driver's travel portrait construction process.Therefore,by identifying urban hotspots and congested areas,improving the city's functional structure and optimizing traffic layout,it can effectively alleviate urban traffic problems.Through the extraction of driver travel portraits,it provides user-based map services to facilitate user's Individual travel.