Abstract
Map-matching between a road network and a raw GPS trajectory must be done in order to analyze the urban traffic computing. A weight-based map-matching algorithm has proposed some important features to solve this problem, such as perpendicular distance between a raw GPS point and a road segment, bearing difference and connectivity. However, the connectivity of a map-matching problem becomes complex when the raw trajectory traveled a parallel multi-lanes road network segments, even humans will have difficulty selecting the correct road segment. To solve this problem, a dijkstra-based selection map-matching (DBSMM) algorithm is asserted by us. Candidate segment set formation, dijkstra-based selection and a friendly driver tagging system are presented in this paper. With the driver-tagged actual paths of our tagging system, it is possible to evaluate the DBSMM algorithm. Therefore, the precise map-matched network traffic data can be the basis for more further traffic researches.
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Acknowledgement
This project was partly supported by the Ministry of Science and Technology of Taiwan under grant NSC101-2221-E-001-021-MY3 and NjiSC100-2219-E-001-002.
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Lin, M.CH., Huang, FM., Liu, PC., Huang, YH., Chung, Ys. (2016). Dijkstra-Based Selection for Parallel Multi-lanes Map-Matching and an Actual Path Tagging System. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9622. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49390-8_49
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DOI: https://doi.org/10.1007/978-3-662-49390-8_49
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