Research On Key Technologies Of Spatial-temporal Trajectory Data Processing Based On Urban Trip

With the rapid development of positioning technology and location-based services,massive position data,also called trajectory data,is generated.The urban travel trajectory is characterized by high density,high information,and high value.Studying the trajectory of the city will help us to further understand the current status of the city,to explore the root cause of urban problems,and to find the theoretical basis for solving urban problems.Map matching,trajectory compression and trajectory characteristic extraction are all key technologies for trajectory data processing.This article focuses on these three technologies for research and discussion.The main contributions of this paper are as follows:(1)An Inward-Recurse Adsorption Map Matching(IRAMM)algorithm is proposed.The algorithm improves the efficiency by performing geometric key point matching and then batch matching,and inward-recursion method reduces the impact of individual error matching,and improves the robustness of the algorithm.(2)Online Map Matching Algorithm Based on Radial-Circles-Based Trajectory Characteristics Extraction is proposed.Based on divide-and-conquer,the map matching problem is decomposed into three problems and different matching strategies are adopted according to different importance and difficulty degrees of map-matching: matching based on fuzzy logic is adopted for the turning segments,while first-matching-and-thenchecking for straight segments.Besides,it adopts parallel computing to improve the matching efficiency.What’s more,trajectory characteristics extracted based on radial circles are used in various stages of map matching.The experiments show that it makes progress in both the accuracy and efficiency.Besides,it is less affected by accidental noise and has high robustness.(3)A general road network standardized processing method is proposed.Both two map-matching algorithms reduce the search range of candidate road segments and avoids duplicate calculations through road network reconstruction and establishment of candidate roads indexes,thereby improving the efficiency of the algorithm.(4)Trajectory compression algorithm based on IRAMM is proposed.The algorithm greatly improves the compression ratio due to the consideration of road network constraints,and reduces compression error by using the matched trajectory points for compression and supplementing the missing key points.The comparative experiments are performed to proves the advantages of the algorithm in terms of compression error and algorithm efficiency.(5)This paper redefines the calculation of time-synchronous Euclidean distance error,and proposes two new trajectory compression error metrics,namely the road network distance error and total distance error,and use matched trajectory to measure the compression error.The compression error metrics proposed is more accurate,more comprehensive,and more practical.(6)This paper proposes the concept of Trajectory Characteristic Extraction(TCE)and TCE based on Radial Circles(RC-TCE).It clearly defines Radial Circle(RC)and related trajectory characteristics extracted based on RC.The advantages of the RC-TEC algorithm are described in detail,which is accuracy,fairness,multi-scale,scalability and ability to identify noise.