A Quick Map-Matching Algorithm by Using Grid-Based Selecting

Floating car data (FCD) is an important material for a broad range of application such as traffic management and control, traffic conditions computation. The traditional map-matching algorithms were more focused on the accuracy of the positioning on the road network than on the computational speed of the algorithms. This approach designs a structure of road network which divides the road network into two levels, and the idea of partitioning the road network into mesh is introduced. Using the information about the position and the direction of the vehicle traveling and the topological feature of the road network, a quick map-matching algorithm which is applicable to real-time handle large-scale FCD is proposed. Examples are provided on a large data set for the Beijing area. The paper demonstrates the efficiency of the algorithm in terms of accuracy and computational speed.

[1]  Xin Gao,et al.  A Heuristic Map-Matching Algorithm by Using Vector-Based Recognition , 2007, 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI'07).

[2]  Tae-Kyung Sung,et al.  Development of a map matching method using the multiple hypothesis technique , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  Washington Y. Ochieng,et al.  A general map matching algorithm for transport telematics applications , 2003 .

[4]  Dieter Pfoser,et al.  Capturing the Uncertainty of Moving-Object Representations , 1999, SSD.

[5]  Jian Huang,et al.  A heuristic path-estimating algorithm for large-scale real-time traffic information calculating , 2008 .

[6]  Enrique Vidal,et al.  Computation of Normalized Edit Distance and Applications , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Fabrice Marchal,et al.  Monitoring a road system’s level of service: The Canton Zurich floating car study 2003 , 2004 .

[8]  Nectaria Tryfona,et al.  Practical data management techniques for vehicle tracking data , 2005, 21st International Conference on Data Engineering (ICDE'05).

[9]  Kay W. Axhausen,et al.  Efficient Map Matching of Large Global Positioning System Data Sets: Tests on Speed-Monitoring Experiment in Zürich , 2005 .

[10]  Hackney K. W. Axhausen map-matching of large GPS data sets - Tests on a speed monitoring experiment in Zurich , 2004 .

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  Dieter Pfoser,et al.  On Map-Matching Vehicle Tracking Data , 2005, VLDB.

[13]  Stefan Lorkowski,et al.  New Approaches for Traffic Management in Metropolitan Areas , 2003 .