Abstract
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.
Highlights
In the application of the wireless sensor networks, traffic information from the senor is very important to control the traffic congestion
If the observation probability does not take into account the topological connectivity among the road sections of the potential points corresponding to the adjacent positioning points, mis-matching will occur
In the actual matching process, we find that the positioning point corresponding to the potential point is unique sometimes by using the error region, direction angle, and other factors to filtering out the potential points
Summary
In the application of the wireless sensor networks, traffic information from the senor is very important to control the traffic congestion. For the low-frequency GPS data, with the increase of sampling time interval, in the complex city road network condition, the vehicle may pass through multiple complex sections, the matching effect and the matching efficiency will be affected greatly To solve this problem, considering the distance, direction, speed, connectivity, and other factors, we propose a map-matching algorithm with the low-frequency floating car data, which can improve the accuracy and efficiency of map matching to select the correct matching path rapidly. The screening process is shown, and in the error region, we can obtain three potential sections L1, L2, and L3, where the floating car speed of positioning point P is 6 m/s. The transition probability represents the possibility of the path between potential point pairs as the actual path of two adjacent positioning points
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