Detecting and correcting map-matching errors in location-based Intelligent Transport Systems

Map-matching (MM) algorithms integrate the data from positioning sensors (such as GPS) with a digital map in order to identify firstly, the road link on which a vehicle is travelling from a set of candidate links; and secondly, to determine the vehicle’s precise location on that segment. Due to errors in positioning sensors, digital maps and the map-matching process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link could mislead users and may degrade the performance of the ITS services and reduce their efficiency. Therefore, the main objective of this paper is to improve a topological mapmatching (tMM) algorithm by error detection, correction and performance re-evaluation. Errors in a tMM algorithm are determined using data (62,887 positioning points) collected in three different countries (UK, USA and India). After map-matching, each mismatched case was examined to identify the primary causes of the mismatches and a number of strategies were developed to correct these mismatches enabling enhancement of the tMM algorithm. An independent dataset (5,256 positioning points) collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The result suggests that the enhanced tMM algorithm correctly identified road segments 97.8% of the time. With the same positioning data, the success rate was found to be 96.5% before the enhancement. The enhanced tMM algorithm developed in this research is simple, fast, efficient and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications.