A Map-matching Algorithm with Extraction of Multi-group Information for Low-frequency Data

TL;DR

This paper introduces a novel map-matching algorithm that leverages multi-group data, including other vehicles' information, to improve accuracy in low-frequency GNSS data scenarios, outperforming existing methods.

Contribution

It proposes a new data grouping and scoring approach, integrating multi-source information for enhanced map-matching accuracy in low-frequency data conditions.

Findings

All scoring methods improve accuracy.

The proposed method outperforms baselines.

Significant gains at low GNSS frequency (<0.01 Hz).

Abstract

The growing use of probe vehicles generates a huge number of GNSS data. Limited by the satellite positioning technology, further improving the accuracy of map-matching is challenging work, especially for low-frequency trajectories. When matching a trajectory, the ego vehicle's spatial-temporal information of the present trip is the most useful with the least amount of data. In addition, there are a large amount 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 map-matching studies only used the ego vehicle's data and ignored other vehicles' data. Based on it, this paper designs a new map-matching method to make full use of "Big data". We first sort all 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, a score for historical usage, and a score for traffic state using the spectral graph Markov neutral 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 map-matching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is less than 0.01 Hz.