A Map-Matching Algorithm With Extraction of Multigroup Information for Low-Frequency Data

The growing use of probe vehicles generates a huge number of global navigation satellite systems (GNSS) data. Limited by satellite positioning technology, further improving the accuracy of map matching (MM) is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is most useful with the least amount of data. In addition, there is a large number of other data, e.g., other vehicles' state and past prediction results, but it is hard to extract useful information for matching maps and inferring paths. Most of the MM studies have used only the ego vehicle's data and ignored other vehicles' data. Based on those, this article designs a new MM method to make full use of "big data." We first sort all the data into four groups according to their spatial and temporal distance from the present matching probe, which allows us to sort for their usefulness. Then we design three different methods to extract valuable information (scores) from them: a score for speed and bearing, one for historical usage, and another for traffic state using a spectral graph Markov neural network. Finally, we use a modified top-K shortest-path method to search the candidate paths within an ellipse region and then use the fused score to infer the path (projected location). We test the proposed method against baseline algorithms using a real-world dataset in China. The results show that all scoring methods can enhance MM accuracy. Furthermore, our method outperforms the others, especially when the GNSS probing frequency is ≤ 0.01Hz.