Abstract
Map-matching is the process of matching the GPS locus to the road network on the digital map. However, due to the most existing map-matching algorithms that are based on high sampling rate, when the sampling interval is increased, the correct rate of the algorithm will be greatly reduced. Based on this, this paper proposed a new algorithm of map-matching for low sampling rate GPS trajectories. The algorithm gave full consideration to the road network of the geometric structure and topological structure and the mutual influence between adjacent points (time, speed information) by calculating the probability of each trajectory point of candidate points to determine matching results. At the end of this paper, we use the data of Beijing UCAR Inc.’s car in a case study. This case demonstrates: For low sampling rate matching track points in the complex road, the algorithm has a good uptime, and an exact match was found.
Highlights
As the result of maturity development of the Internet technology, the wisdom city can be built and developed quickly, including intelligent transportation infrastructure as one, which is indispensable
Its core steps are involved in GPS to accurately position the GPS track data of vehicles on the road, in other words, the map-matching [1]
In view of the track point with a low sampling point, the paper will put forward an improved algorithm of map-matching
Summary
As the result of maturity development of the Internet technology, the wisdom city can be built and developed quickly, including intelligent transportation infrastructure as one, which is indispensable. The construction of intelligence transportation includes several areas: vehicle navigation, traffic flow analysis, and satellite positioning which has not been intensively studied. At present, the algorithm of map-matching is only for processing GPS data with high sampling rate (usually 10~30 s every one track point) [7]. When they use points with low sampling rate as their data, the matching error is over 50% [8, 9]. We have to calculate the candidate points in the section by making use of the point-to-curve in the present geometric map-matching algorithm
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