Research On GPS Map Matching Of Taxi Vehicles Based On

With the economy booming and society developing, road traffic system is becoming more and more complicated, which brings even more serious congestion of traffic. Dealing with traffic jams has become a primary task for each and every city to fulfill. Vehicle location and navigation system is an important sub-system in intelligent transportation system. Map-matching technology correlates vehicle’s positioning information measured by navigation system with road network in electronic map in order to obtain vehicle’s optimal estimated position, which is a key technology for realizing vehicle locating and navigating.Obtaining vehicle’s accurate position is the basic requirement for vehicle location and navigation system. The development of GPS technology provides powerful safeguard to the accurate positioning of vehicles. But both the GPS system and electronic maps that are used to display vehicles’position have errors. So it requires proper map-matching technology to make vehicle’s position correctly displayed in the road network on electronic maps. Map-matching algorithms are designed to solve that problem.This thesis targets the problem of mismatching between vehicles’positions and road networks on electronic maps, analyses the causes of mismatching between vehicles’positions and road networks on electronic maps. Then it analyses and summarizes many existing map-matching algorithms finally raises a modified map-matching algorithm based on weight. This new algorithm builds the error area through buffer analysis, and then calculates matching degrees by weighting candidate roads from four perspectives of distance, two kinds of nearness and relative position in order to figure out the optimal matching road. The whole procedure is conducted based on ArcGIS Desktop and ArcGIS Engine. The final result is displayed on electronic maps. In the end, this thesis compares the result of this map-matching algorithm with the result of map-matching algorithm based on weight. It turns out to be a higher matching rate.