01627616

Component

https://doi.org/10.3141/2645-08

With the development of smartphones and portable GPS devices, large-scale, high-resolution GPS data can be collected. Map matching is a critical step in studying vehicle driving activity and recognizing network traffic conditions from the data. A new trajectory segmentation map-matching algorithm is proposed to deal accurately and efficiently with large-scale, high-resolution GPS trajectory data. The new algorithm separated the GPS trajectory into segments. It found the shortest path for each segment in a scientific manner and ultimately generated a best-matched path for the entire trajectory. The similarity of a trajectory segment and its matched path is described by a similarity score system based on the longest common subsequence. The numerical experiment indicated that the proposed map-matching algorithm was very promising in relation to accuracy and computational efficiency. Large-scale data set applications verified that the proposed method is robust and capable of dealing with real-world, large-scale GPS data in a computationally efficient and accurate manner.

01628860

17-03683

English

pp 67–75

2017

Transportation Research Record: Journal of the Transportation Research Board

9780309460408

Digital/other

Figures (3) ; References (36) ; Tables (1)

Global Positioning System; Shortest path algorithms; Vehicle trajectories

Map matching algorithms

Data and Information Technology; Highways; Operations and Traffic Management; Planning and Forecasting

TRIS, TRB, ATRI

Dec 8 2016 11:24AM

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