The Data Mining Of Floating Car Trajectory And Its Application In Modern Logistics

With the expansion of urbanization and the increasing of population in our country, the residents' needs are also more and more, while traffic congestion of urban roads currently is causing longer time consuming of logistics and poor consLumer experience. The rapid development of modern logistics has made higher and higher demands on urban transport system, we can see the inevitability and urgency of solving the problem of urban traffic.Based on this, considering the main source of traffic flow is the residents travel, and analysis results of the residents' travel characteristics can reflect the basic traffic conditions of a region. This paper analyzes the temporal and spatial distribution characteristics of urban traffic in Beijing and its application in modern logistics through the excavation of residents' travel behavior characteristics, taking the track data of 28,000 floating cars in Beijing for one month,the specific contents are summarized as follows:(1) The technology research related Hadoop ecosystem, its mainly study the architecture of Hadoop Distributed File System and the principle of Spark data processing, then building the distributed cluster contains three nodes which can improve the efficiency of data processing.(2) Floating car trajectory data preprocessing, this paper proposed a new method to identify and clean the repetitive data by combining the data filtering and probability statistics,and an improved Douglas-Peucker algorithm was also presented as well,which can effectively identify floating car drift data. And this paper also provided a set of complete floating car trajectory data preprocessing framework.(3) The analysis of roads' traffic time characteristics as well as visualization and application, to achieve the traffic trips, peak hours and other characteristics of the statistical analysis, compared the differences of holidays, workdays and weekends, and combined with visualization of its vivid and intuitive presented at the same time analyzes its application in modern logistics.(4) The research of urban traffic space hotspot area distribution, through the comparision of a variety of clustering algorithms, the use of DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to find the hotspots identification; comparative analysis of the number of hot spots at different times, geographical distribution, heat size and application,and verified the accuracy of clustering results from the semantic level by the map.The research are not only deepen our understanding of traffic network, but also explain the traffic demand of different regions and times from the traffic management level, which provides a reference for the intelligent decision making of modern logistics, and new perspective to solve traffic congestion, moreover has a great signifinance to the development of traffic planning and modern logistics.