Online Map Matching With Route Prediction

Abstract

Map matching is a procedure that estimates the route traveled by vehicles or people by using observed coordinates. It is an important preprocessing procedure for location services based on global positioning system (GPS) data obtained from probe vehicles. One recently proposed major map matching approach is the hidden Markov model (HMM)-based method. However, HMM-based approaches suffer from latency, because they rely on the availability of future GPS points. This latency limits the ability of real-time traffic sensing and location services. This paper presents a novel online map matching algorithm that uses a probabilistic route prediction model instead of future GPS points. The probabilistic route prediction model can be trained by using historical trajectory data. Our experimental results show that the accuracy of the untrained proposed model is competitive with a naïve online HMM-based method without any latency. Moreover, the results show that the trained model obtains even higher accuracy. The experimental results also show that the proposed method is faster than the online HMM.