| With the rapid development of the technology of connected vehicles,the "box"data recording the real-time locations and the state of the vehicles emerges.Through the statistics and mining of the "box" data,health status of the vehicles can be analyzed,as well as the characteristics of the drivers' behaviors,and the traffic operations can thus be monitored.The map matching process of location information is the foundation of making the most of the data.In this paper,firstly,the electronic map is divided into grids,and the positioning data of the map is matched and an initial result set is established,which includes each location with all its possible matching results.Secondly,an improved weight optimal matching algorithm is proposed based on the Markov model,which introduces the actual distance between the adjacent points and the heading of vehicles as the factors of the model.The observation probability is determined regarding the error of angle and the error of distance,and the transition probability is determined regarding the ratio of actual distance and the shortest path length on the electronic map.The solution of the model is calculated by the Viterbi algorithm.Based on the topological relations among the objects of the map,the matching result is corrected to ensure the accessibility between adjacent links and the reasonability of matching paths.Finally,based on the travel time of the sections,average velocities of each section are extracted.Daily variation tendencies of the average velocities of the ring roads are calculated for expressways,arterial roads and trunk roads respectively.The results are analyzed in comparison with the monitoring results of the FCD.It shows that for private cars and taxis,the average velocities and trends of the velocities are similar,indicating the feasibility of the presented algorithm.With the influence of vehicle condition and other factors,the average velocities of private cars are slightly higher than taxis of the same area. |
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