Research On Map Matching Algorithm Based On Cell Phone Base Station Positioning Data

With the widespread use of location based services(LBS)such as location,navigation,trajectory analysis and trajectory prediction,more and more smartphone users install location based services application,and the location data generated by such apps can bring a lot of information to traffic researchers.Nowadays,LBS has become the technical subject of continuous research and breakthrough by traffic researchers,and the calibration of location data generated by smartphones and electronic maps have become the core of these technologies.There are three ways to obtain location information through smart phones,which is GPS positioning,base stations positioning,and Wi-Fi positioning.However,these three methods have their own advantages and disadvantages.By comparison,we found that although the positioning accuracy of mobile phone base station is not high,its energy consumption and cost are lower.In order to overcome the large positioning error of the base station and provide more accurate and reliable location data,the map matching algorithm is needed to accurately match the vehicle position to the electronic map.This paper proposes a map matching algorithm that adapts to the positioning method of mobile phone base stations to improve the accuracy of mobile phone base station positioning.First,we introduce the data characteristics and analysis process of mobile phone base station positioning and electronic map road network.Then,based on the characteristics of base station positioning data,we propose a buffer domain map matching(BDMM)algorithm to change the influence of traditional HMM model on the map matching process;Finally,we optimized the map matching process through the proposed preferred candidate map matching(PCMM),and compared it with the mainstream HMM algorithm.By designing a reasonable map matching algorithm and the actual road driving is taken as the real data set to test the map matching results.Experimental results show that the accuracy of our proposed map matching algorithm is greatly improved compared with the mainstream map matching algorithm HMM.