Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Liu, Zhijia; Fang, Jie; Tong, Yingfang; Xu, Mengyun

doi:10.1049/iet-its.2020.0486

Cited by 13 publications

(5 citation statements)

References 46 publications

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“…In this part, we investigated three NN models and calculate their final mean square error E in Eq. (10). Except for SGMN, we pick the Long Shortterm Memory model (LSTM) and Graph Markov Neutral Network (GMN) for comparison.…”

Section: B Comparative Analysis Of Different Nn Modelsmentioning

confidence: 99%

“…Compared with an algorithm based on high-frequency probes, the MM with low-frequency probes (e.g., ď 1{30 Hz or ě 30 s sampling interval) usually has lower accuracy. Its accuracy further drops significantly with a decrease of its frequency when the sampling interval is ą 60 s [8]- [10]. How to further improve the MM accuracy with low-frequency probes is still challenging work.…”

Section: Introductionmentioning

confidence: 99%

“…The historical data can indicate valuable information. Zheng et al leveraged historical spatial features for MM [21], and many research further use historical temporal features to improve MM accuracy [10], [22]- [24]. In addition, based on the historical path inference results, some studies estimated travelers' path preference and used this information to fix present MM result [9], [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

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

Fang¹,

Wu²,

Lin³

et al. 2022

Preprint

Self Cite

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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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Section: B Comparative Analysis Of Different Nn Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Fang¹,

Wu²,

Lin³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The rough matching strategy is a distance proximity criterion, which is the ratio of the Euclidean distance between the beginning and the end nodes for R(N, A) and R ′ (N ′ , A ′ ); the expression is as follows [19]:…”

Section: Rough Matching Based On Distance Proximitymentioning

confidence: 99%

Studying a matching method combining distance proximity and buffer constraints

Wang

2022

Applied Mathematics and Nonlinear Sciences

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In order to achieve fast matching of global navigation satellite system (GNSS) track data and vector road network data, a rough-to-fine matching method combining distance proximity and buffer constraints is studied. The GNSS track data is preprocessed: A rasterisation method is used to eliminate the low-quality track data and obtain the binary graph; then, the road centrelines are obtained by a skeleton extraction method; the thinning data is converted into line segments or closed lines starting from nodes by the boundary tracking method, which is stored in the vector form. Finally, the boundary tracking method is used to convert the thinning data into closed lines and store them in the vector form. A two-layer rough-to-fine matching strategy is applied: First, the changing state of the road arc is determined according to the rough matching strategy of road segment distance. Next, according to the buffer constraint criterion, a rectangular or circular buffer is selected to achieve precise matching of road network data. The results show that the proposed method has good matching effect, which is especially suitable for working with more GNSS track data.

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“…The vehicle location information and trajectory data are obtained. Various intelligent traffic applications can be developed based on the collected large-scale trajectory data [17][18][19] .…”

Section: Introductionmentioning

confidence: 99%

A vehicle trajectory collection and query system based on crowd sensing

Chen,

Du,

Sun

et al. 2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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Vehicle trajectory information is important for intelligent traffic management. By considering the problems of vehicle trajectory acquisition, a trajectory data collection system that exploits the crowd sensing techniques is proposed in this paper. The smartphone is used to collect vehicle trajectory data. The system consists of trajectory data collection terminal, trajectory data server, and trajectory query terminal. The collection terminal uses a smartphone to take photos of the preceding vehicles and recognize the license plates. It is uploaded to the trajectory data server along with the timestamp and location. The server receives and stores the trajectory data from all mobile collection terminals. The query terminal queries the driving trajectory of a specified vehicle during a specified period from the trajectory database. The queried results are visualized on an electronic map. The proposed system is tested in urban scenes. The experimental results show its feasibility and availability. The designed system for vehicle trajectory collection and query expands the scene of vehicle perception and improves the real-time trajectory data collection. It can be applied to vehicle intelligence management in the field of intelligent transportation.

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Deep learning enabled vehicle trajectory map‐matching method with advanced spatial–temporal analysis

Cited by 13 publications

References 46 publications

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

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

Studying a matching method combining distance proximity and buffer constraints

A vehicle trajectory collection and query system based on crowd sensing

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