.png){kind=icon}
.png){kind=icon}
Return
Offline map matching using time-expanded graph for low-frequency data
.png){kind=icon}
OA
AI
DOI：10.1016/j.trc.2021.103265.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Map matching is an essential preprocessing step for most trajectory-based intelligent transport system services. Due to device capability constraints and the lack of a high-performance model, map matching for low-sampling-rate trajectories is of particular interest. Therefore, we developed a time-expanded graph matching (TEG-matching) that has three advantages (1) high speed and accuracy, as it is robust for spatial measurement error and a pause such as at traffic lights; (2) being parameter-free, that is, our algorithm has no predetermined hyperparameters; and (3) only requiring ordered locations for map matching. Given a set of low-frequency GPS data, we construct a time-expanded graph (TEG) whose path from source to sink represents a candidate route. We find the shortest path on TEG to obtain the matching route with a small area between the vehicle trajectory. Additionally, we introduce two general speedup techniques (most map matching methods can apply) bottom-up segmentation and fractional cascading. Numerical experiments with worldwide vehicle trajectories in a public dataset show that TEG-matching outperforms state-of-the-art algorithms in terms of accuracy and speed, and we verify the effectiveness of the two general speedup techniques.
Keywords:
Offline map matching
Time-expanded graph
Neighborhood search analysis
Fractional cascading
Bottom-up segmentation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.9
Papers:
4.7K
Citations:
3.2W