Abstract
In recent years, the rapid development of Internet of Things have led to the explosive growth of traffic data. Big traffic data has rapidly developed into a hot topic that attracts extensive attention from academia, industry and governments. The efficient approach to find accompany vehicle is a kind of practices for police criminal investigation department with regard to massive vehicle data retrieval. In this paper, we propose a MapReduce-based approach to find accompany vehicle which contains two MapReduce jobs: the first is to extract the accompany vehicle pairs by traffic monitor position; and the second is to calculate the total frequency of each accompany vehicle pair based on the output of the first job.
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Acknowledgment
This work is supported by the Project of the Ministry of Public Security under Grant 2014JSYJB051. The authors of this paper are members of Shanghai Engineering Research Center of Intelligent Video Surveillance. This work was supported in part the National Natural Science Foundation of China under Grant 61300202, 61332018, 61403084. Our research was sponsored by Program of Science and Technology Commission of Shanghai Municipality (No. 15530701300, 15XD15202000, 16511101700), in part by the technical research program of Chinese ministry of public security (2015JSYJB26).
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Zhao, Y., Wang, P., Wang, W., Hu, L., Xu, X. (2018). MapReduce-Based Approach to Find Accompany Vehicle in Traffic Data. In: Abawajy, J., Choo, KK., Islam, R. (eds) International Conference on Applications and Techniques in Cyber Security and Intelligence. ATCI 2017. Advances in Intelligent Systems and Computing, vol 580. Edizioni della Normale, Cham. https://doi.org/10.1007/978-3-319-67071-3_5
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DOI: https://doi.org/10.1007/978-3-319-67071-3_5
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Publisher Name: Edizioni della Normale, Cham
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