Analysis And Application Of Traffic Data Based On Time-space Trajectory

With the improvement of the precision of GPS and the widely use of various vehicle mounted sensors,more and more vehicle trajectories data are recorded.There is a large amount of valuable information in the massive trajectories data,so it is necessary to mine valuable information from the large amount of data.The vehicle trajectories data are different from the general trajectories data,which is limited in the constrained road network.Therefore,the commonly used trajectory data mining methods are often difficult to apply directly to the vehicle trajectory mining.Based on this,this thesis proposes a trajectory space-time similarity measure algorithm GTTSD and K-means optimization DBSCAN clustering algorithm,and implements a traffic hotspot area discovery system.The specific work is as follows1.Grid-Based trajectory time-space distance.When the trajectory spatiotemporal similarity measure is carried out,the traditional method based on the continental space is transformed into the spatial grid representation by the dynamic meshing method.after that,the trajectory segmentation is performed according to the breakpoint on the trajectory sequence.Finally,the sub-trajectories of the segmented are calculated by spatiotemporal similarity.This algorithm can reduce the storage cost of the trajectory data and avoid the deviation of the similarity between the same trajectory data in the European space due to the inconsistency of the sampling time.the experimental results show that the algorithm can obtain accurate trajectory space-time distance and high execution efficiency.2.DBSCAN algorithm based on K-means optimization.DBSCAN algorithm based on K-means optimization is proposed for the traditional DBSCAN clustering algorithm,which is influenced by the artificially set parameter value.Firstly,the K-means clustering algorithm is used to quickly cluster the data objects,and the clustering results are statistically analyzed to obtain the values of the initial neighborhood radius and neighborhood density threshold in the DBSCAN algorithm.Firstly,the K-means clustering algorithm is used to quickly cluster the data objects,and the clustering results are statistically analyzed to obtain the values of the initial neighborhood radius and neighborhood density threshold in the DBSCAN algorithm.Based on this,The algorithm performs DBSCAN clustering on the data object and dynamically adjusts the value of the neighborhood radius during the clustering process.Finally,The experimental results show that the algorithm has good clustering effect on the trajectory data.3.Traffic hotspot area discovery system.The system includes trajectory data extraction,trajectory space-time distance measurement,trajectory clustering,road network hotspot mining four modules.The system can be based on user needs different trajectory data for different operations and have a strong practical value.