Map-matching algorithm for large-scale low-frequency floating car data

• Published in: International journal of geographical information science : IJGIS Vol. 28; no. 1; pp. 22 - 38
• Main Authors: Chen, Bi Yu, Yuan, Hui, Li, Qingquan, Lam, William H.K., Shaw, Shih-Lung, Yan, Ke
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.01.2014; Taylor & Francis LLC
• Subjects: Accuracy; Algorithms; Dynamic programming; Global positioning systems; GPS; map matching; mobile objects; mobility
• ISSN: 1365-8816; 1362-3087; 1365-8824
• DOI: 10.1080/13658816.2013.816427

Large-scale global positioning system (GPS) positioning information of floating cars has been recognised as a major data source for many transportation applications. Mapping large-scale low-frequency floating car data (FCD) onto the road network is very challenging for traditional map-matching (MM) algorithms developed for in-vehicle navigation. In this paper, a multi-criteria dynamic programming map-matching (MDP-MM) algorithm is proposed for online matching FCD. In the proposed MDP-MM algorithm, the MDP technique is used to minimise the number of candidate routes maintained at each GPS point, while guaranteeing to determine the best matching route. In addition, several useful techniques are developed to improve running time of the shortest path calculation in the MM process. Case studies based on real FCD demonstrate the accuracy and computational performance of the MDP-MM algorithm. Results indicated that the MDP-MM algorithm is competitive with existing algorithms in both accuracy and computational performance.