Abstract
Vehicular trajectory data contains a wealth of geospatial information and human activity information. This paper proposes a method that uses only a time series, including latitude and longitude information, to classify drivers into four types: dangerous, high-risk, low-risk, and safe. The main contribution of this paper is the creative approach that uses Convolutional Neural Networks (CNNs) in extracting trajectory features and processing raw trajectories into inputs of CNN. After training the CNN network and combining results predicted by segments, the study described in this paper achieved a classification accuracy of 77.3%.
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Acknowledgements
This work was partially supported by National Natural Science Foundation of China (No. 61571241 and 61872423), Industry Prospective Primary Research & Development Plan of Jiangsu Province (No. BE2017111), the Scientific Research Foundation of the Higher Education Institutions of Jiangsu Province (No. 19KJA180006), Six talent peaks project of Jiangsu Province (No. DZXX-008), the Postdoctoral Science Foundation, China (Nos. 2019K026 and 2019M661900), and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX18_0912).
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Yang, X., Ding, F., Zhang, D., Zhang, M. (2020). Vehicular Trajectory Big Data: Driving Behavior Recognition Algorithm Based on Deep Learning. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1253. Springer, Singapore. https://doi.org/10.1007/978-981-15-8086-4_30
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DOI: https://doi.org/10.1007/978-981-15-8086-4_30
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