Abstract
With the rapid growth of motor vehicles and the popularity of the Global Positioning System, massive traffic trajectory data is accumulated. Traffic trajectory data contains a large amount of structured knowledge such as time, space and the relationship between various traffic elements. Comparing the trajectory of the owner’s travel, mining the travel mode of the owner has become a hot topic in academic research. At present, commonly used methods for measuring similarity include dynamic time warping (DTW), Euclidean distance (ED), longest common subsequence (LCSS), and edit distance (EDR). DTW is currently recognized as the most anti-jamming and anti-deformation similarity evaluation method, but its disadvantages that high time and space complexity are also obvious. This paper proposes a new trajectory similarity measure, without using the DTW algorithm, we also can solve the problem of inaccurate matching by noise. The matching accuracy of this method is similar to or equal to DTW, which can be applied to traffic trajectory similarity evaluation, the matching speed is about 2–5 times faster than DTW.
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Luo, S., Zou, F., Cai, Q., Guo, F., Xu, W., Li, Y. (2020). A Novel Measure for Trajectory Similarity. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_17
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DOI: https://doi.org/10.1007/978-981-15-3308-2_17
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