Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities
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The process of current urban and accelerating the number of motor vehicles increased rapidly resulting in road traffic pressure is increasing, we need to analyze large data traffic in the city, to guide urban road planning and improve the level of city management, and city operation rules found from traffic data in complex. However, traffic data are characterized by large amount and high dimension, which makes the analysis process difficult. In this paper, the composition, characteristics and application of large data in traffic field are introduced. Mining multi-source heterogeneous data traffic generated by the depth of the traffic data to establish a comprehensive analysis platform and project evaluation subsystem, the formation of integrated traffic system model for multi field, multi-level application requirements. In this paper, we propose a visualization model based on self-organizing feature map neural networks with graph theory. This paper analyzes the traffic data of the whole life cycle, combing the traffic data collection, analysis, discovery, the level of application, and uses big data techniques to guide the city traffic planning, construction, management, operation and decision support.
KeywordsSelf organization Feature mapping Neural networks Graph theory Large traffic data Visualization model
This study was supported by GoPerception Open Project Funding.
- 1.Wang, Z., Yuan, X.: Visual analysis of trajectory data. J. Comput. Aided Des. Comput. Graph. 1, 9–30 (2015)Google Scholar
- 2.Tobler, W.: Experiments in migration mapping by computer. Cartogr. Geogr. Inf. Sci. 14(2), 140–163 (1987)Google Scholar
- 4.Lee, J.G., Jiawei, H.: Trajectory Clustering: A Partition and Group Framework. In: Proceedings of ACM’s Special Interest Group on Management of Data. Beijing, China (2007)Google Scholar
- 7.Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., ... Chen, J.: Deep speech 2: end-to-end speech recognition in english and mandarin. In: International Conference on Machine Learning, pp. 173–182 (2006)Google Scholar
- 8.Chen, Y., Luo, Y., Huang, W., Hu, D., Zheng, R.Q., Cong, S.Z.,. & Wang, X.Y., : Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Comput. Biol. Med. 89, 18–23 (2017)Google Scholar
- 9.Zhu, L.Q., Ma, M.Y., Zhang, Z., Zhang, P.Y., Wu, W., Wang, D.D.,. & Wang, H. Y.: Hybrid deep learning for automated lepidopteran insect image classification. Oriental Insects 51(2), 79–91 (2017)Google Scholar
- 10.Shuo, D., Xiao-Heng, C., Qinghui, W.: Approximation performance of BP neural networks improved by heuristic approach. Appl. Mech. Mater. 411–414, 1952–1955 (2013)Google Scholar
- 11.Shuo, D., Xiao-Heng, C., Qinghui, W.: Pattern classification based on self-organizing feature mapping neural network. Appl. Mech. Mater. 448–453, 3645–3649 (2014)Google Scholar
- 15.Chen, J., Zhiqiang, Y., Zhu, J.: Data visualization technology and its application. Infrared Laser Eng. 30(5), 330–342 (2001)Google Scholar
- 16.Liu, D.: The Research of Large-Scale Data Visualization. Tianjin University, Tianjin (2009)Google Scholar
- 17.Sun, Y., Feng, X.: Survey on research of multidimensional and multivariate data visualization. Comput. Sci. 35(11), 1–17 (2008)Google Scholar