A map-matching algorithm with low-frequency floating car data based on matching path

• Published in: EURASIP journal on wireless communications and networking Vol. 2018; no. 1; pp. 1 - 14
• Main Authors: Yuan, Ling, Li, Dan, Hu, Song
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 14.06.2018; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Algorithms and Architectures for Industrial Wireless Sensor Networks; Communications Engineering; Energy conservation; Engineering; Floating structures; Graph matching; Information Systems Applications (incl.Internet); Intelligent transportation systems; Low-frequency floating car data; Map matching; Matching path; Networks; Positioning point; Potential point; Remote sensors; Run time (computers); Signal,Image and Speech Processing; Traffic information; Wireless sensor networks; Matching path; Map matching; Low-frequency floating car data; Potential point; Positioning point
• ISSN: 1687-1499; 1687-1472; 1687-1499
• DOI: 10.1186/s13638-018-1154-x

With the wide application and rapid development of Intelligent Transportation System (ITS), the floating car has been widely used in the collection of traffic information, which is also very important in the application of the wireless sensor networks. In addition to the high-frequency floating car, energy-saving low-frequency floating car has attracted great attention, but the low-frequency GPS data have a poor effect on map matching. Taking consideration of the distance, direction, speed, and topology of road and vehicle, we propose a global map matching algorithm with low-frequency floating car data based on the matching path. The proposed algorithm preprocesses the floating car data and road network data to determine the potential points and sections by constructing the error region. Then, we calculate the potential matching path graph with the analysis of time and space. Finally, we can obtain the matching result by parallel computing with section division methodology. The experiment results demonstrate that the proposed map-matching algorithm can improve the running time and matching accuracy compared with the existing methods.