MSTM: A novel map matching approach for low-sampling-rate trajectories

Tuo, Yupeng; Yun, Xiaochun; Zhang, Yongzheng

doi:10.1109/pimrc.2017.8292591

Cited by 1 publication

(4 citation statements)

References 14 publications

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“…Incremental [89] The trajectory points are matched to subpaths by linear model Incremental Probability statistics [108] Set inference rules and weight them Global [90] Construct a search tree for the later selected sections and then evaluate the results using spatial and temporal information Global [104] The probability formula is used to search the candidate segment and the results are obtained by the correlation coefficient method Global [105] Using the Bayesian model to obtain the highest score path Global…”

Section: Citationsmentioning

confidence: 99%

“…As the map-matching techniques grow rapidly, algorithms based on geometric principle, topological structure, probability and machine learning are applied to map matching, which is also the key technique of map-matching techniques [88][89][90][91]. In this section, we classify map-matching algorithms with sampling frequency and data information.…”

Section: Survey Of Selection Algorithmsmentioning

confidence: 99%

“…As shown in Table 6, according to the data information contained in the data sets, the map-matching algorithms can be categorized into four types: geometric principle [88,100,101], topology [28,89,102,103], probability statistics [90,[104][105][106] and advanced model [91,[107][108][109][110][111][112]. These algorithms can be regarded as incremental and global method according to the number of sampling points in map matching.…”

Section: Classification Of Map-matching Algorithms Based On Data Info...mentioning

confidence: 99%

“…Probability statistics algorithm is designed to find a matching path with the maximum probability. Tuo et al [90] used candidate segment to construct map search trees, which have multiple branch paths from root to end and cannot be traced back. Then, considering the spatial and temporal information of trajectory, they evaluated each branch in the map-searching tree.…”

Section: Probability Statisticsmentioning

confidence: 99%

See 3 more Smart Citations

Survey on vehicle map matching techniques

Huang

Qiao

Han

et al. 2021

CAAI Trans on Intel Tech

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With the development of location-based services and Big data technology, vehicle map matching techniques are growing rapidly, which is the fundamental techniques in the study of exploring global positioning system (GPS) data. The pre-processed GPS data can provide the guarantee of high-quality data for the research of mining passenger's points of interest and urban computing services. The existing surveys mainly focus on map-matching algorithms, but there are few descriptions on the key phases of the acquisition of sampling data, floating car and road data preprocessing in vehicle map matching systems. To address these limitations, the contribution of this survey on map matching techniques lies in the following aspects: (i) the background knowledge, function and system framework of vehicle map matching techniques; (ii) description of floating car data and road network structure to understand the detailed phase of map matching; (iii) data preprocessing rules, specific methodologies, and significance of floating car and road data; (iv) map matching algorithms are classified by the sampling frequency and data information. The authors give the introduction of open-source GPS sampling data sets, and the evaluation measurements of map-matching approaches; (v) the suggestions on data preprocessing and map matching algorithms in the future work. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Citationsmentioning

confidence: 99%

Section: Survey Of Selection Algorithmsmentioning

confidence: 99%