An Intelligent Adaptive Spatiotemporal Graph Approach for GPS-Data-Based Travel-Time Estimation

Xu, Mengyun; Fang, Jie; Tong, Yingfang

doi:10.1109/mits.2021.3099796

Cited by 8 publications

(1 citation statement)

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“…W ITH the development of intelligent transportation systems, application of Global Navigation Satellite System (GNSS, including GPS, Beidou, etc) data consisting of vehicle ID and spatial-temporal data has become commonplace. GNSS-based traffic information is now the premise and foundation of intelligent traffic research, such as travel time estimation [1], [2] and traffic speed prediction [3]- [5]. Matching the GNSS trajectory onto the map is usually the first step of GNSS-based research.…”

Section: Introductionmentioning

confidence: 99%

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

Fang¹,

Wu²,

Lin³

et al. 2022

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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 mapmatching accuracy. Furthermore, our method outperforms the others, especially when GNSS probing frequency is ď 0.01 Hz.

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