| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [1] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270901 Belo Horizonte, MG - Brazil
[2] Univ Ottawa, Sch Elect Engn & Comp Sci, Ottawa, ON K1N 6N5 - Canada
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | ACM COMPUTING SURVEYS; v. 53, n. 5 OCT 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
The increasing availability of vehicular trajectory data is at the core of smart mobility solutions. Such data offer us unprecedented information for the development of trajectory data mining-based applications. An essential task of trajectory analysis is the employment of efficient and accurate methods to compare trajectories. This work presents a systematic survey of vehicular trajectory similarity measures and provides a panorama of the research field. First, we show an overview of vehicle trajectory data, including the models and some preprocessing techniques. Then, we give a comprehensive review of methods to compare trajectories and their intrinsic properties. We classify the methods according to the trajectory representation and features such as metricity, computational complexity, and robustness to noise and local time shift. Last, we discuss the applications of vehicular trajectory similarity measures and some open research problems. (AU) | |
| FAPESP's process: | 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS) |
| Grantee: | Antonio Alfredo Ferreira Loureiro |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/24494-8 - Communications and processing of big data in cloud and fog computing |
| Grantee: | Nelson Luis Saldanha da Fonseca |
| Support Opportunities: | Research Projects - Thematic Grants |