Online Map-Matching Algorithm Using Object Motion Laws

Kang, Wei; Li, Shun; Chen, Wei; Lei, Kai; Wang, Tengjiao

doi:10.1109/bigdatasecurity.2017.31

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…In [18], Jagadeesh and Srikanthan combine the HMM model with a route choice model to reassess the HMM-generated partial route for accuracy improvement. Kang et al [19] extends the HMM-based model by introducing constraints based on motion laws (e.g., speed limitation). In [39], Yin et al propose a feature-based model for determining transition probability in HMM and the model can be applied to both offline and online map matching.…”

Section: Related Workmentioning

confidence: 99%

Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

Luo

Hou

Cai

et al. 2020

Information Sciences

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Section: Related Workmentioning

confidence: 99%

Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

Luo

Hou

Cai

et al. 2020

Information Sciences

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“…The trajectory data of various vehicles can reflect the operational status of urban traffic, which can help traffic management departments to optimize road facilities and plan traffic lines ( Chen et al, 2020a ; Makris et al, 2021a ; Li et al, 2020 ; Chen et al, 2020b ). Pedestrian trajectory data can be used to analyze restaurants and tourist areas that are of interest to pedestrians, as well as to assist shops in choosing business locations ( Jiagao et al, 2015 ; Ji et al, 2016 ; Kang et al, 2017 ). At the same time, the huge amount of track data files can cause a lot of problems for data users.…”

Section: Introductionmentioning

confidence: 99%

A trajectory data compression algorithm based on spatio-temporal characteristics

Zhong

Kong

Zhang

et al. 2022

PeerJ Computer Science

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Background With the growth of trajectory data, the large amount of data causes a lot of problems with storage, analysis, mining, etc. Most of the traditional trajectory data compression methods are focused on preserving spatial characteristic information and pay little attention to other temporal information on trajectory data, such as speed change points or stop points. Methods A data compression algorithm based on the spatio-temporal characteristics (CASC) of the trajectory data is proposed to solve this problem. This algorithm compresses trajectory data by taking the azimuth difference, velocity difference and time interval as parameters in order to preserve spatial-temporal characteristics. Microsoft’s Geolife1.3 data set was used for a compression test to verify the validity of the algorithm. The compression results were compared with the traditional Douglas-Peucker (DP), Top-Down Time Ratio (TD-TR) and Opening Window (OPW) algorithms. Compression rate, the direction information of trajectory points, vertical synchronization distance, and algorithm type (online/offline) were used to evaluate the above algorithms. Results The experimental results show that with the same compression rate, the ability of the CASC to retain the forward direction trajectory is optimal, followed by TD-TR, DP, and then OPW. The velocity characteristics of the trajectories are also stably retained when the speed threshold value is not more than 100%. Unlike the DP and TD-TR algorithms, CASC is an online algorithm. Compared with OPW, which is also an online algorithm, CASC has better compression quality. The error distributions of the four algorithms have been compared, and CASC is the most stable algorithm. Taken together, CASC outperforms DP, TD-TR and OPW in trajectory compression.

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“…The spatial features, including geometry (e.g., location and network's "shape") and the topology (e.g., roads' connectivity and bearing), are most commonly used [11], and many research matched trajectories based on spatial data alone [12]- [16]. Furthermore, some MM algorithms combine the spatial features with the temporal features, such as travel time [17], [18] and speed [19], [20], and sometimes their results could be better. In general, present two GNSS probes are the basic information for all MM algorithms.…”

Section: Introductionmentioning

confidence: 99%

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

Fang¹,

Wu²,

Lin³

et al. 2022

Preprint

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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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Online Map-Matching Algorithm Using Object Motion Laws

Cited by 8 publications

References 14 publications

Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

Incremental route inference from low-sampling GPS data: An opportunistic approach to online map matching

A trajectory data compression algorithm based on spatio-temporal characteristics

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

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