Abstract
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.
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05 December 2022
This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1007/s10586-022-03907-6
References
Wang, Z., Yuan, X.: Visual analysis of trajectory data. J. Comput. Aided Des. Comput. Graph. 1, 9–30 (2015)
Tobler, W.: Experiments in migration mapping by computer. Cartogr. Geogr. Inf. Sci. 14(2), 140–163 (1987)
Kapler, T., Wright, W.: GeoTime information visualization. Inf. Vis. 4(2), 135–146 (2005)
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)
Chetverikov, D., Szabo, Z.: A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves. In: Proceedings of the 10th International Conference on Computer Analysis of Images and Patterns. Goningen, The Netherlands (2003)
Scheepens, R., Willems, N., Van de Wetering, H.: Composite density maps for multivariate trajectories. IEEE Trans. Vis. Comput. Graph. 17(12), 2511–2532 (2011)
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)
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)
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)
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)
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)
Cao, J., Shridhar, M.: A hierarchical neural network architecture for hand written numeral recognition. Pattern Recognit. 30(2), 280–294 (1997)
Pierre, P.: An Introduction to the Modeling of Neural Networks. Cambridge University Press, London (1992)
Defanti, T.A., Brown, M.D., Mccormick, B.H.: Visualization: expanding scientific and engineering researchopportunities. Computer 22(8), 10–21 (1989)
Chen, J., Zhiqiang, Y., Zhu, J.: Data visualization technology and its application. Infrared Laser Eng. 30(5), 330–342 (2001)
Liu, D.: The Research of Large-Scale Data Visualization. Tianjin University, Tianjin (2009)
Sun, Y., Feng, X.: Survey on research of multidimensional and multivariate data visualization. Comput. Sci. 35(11), 1–17 (2008)
Heer, J., Robertson, G.G.: Animated transitions in statistical data graphics. IEEE Trans. Vis. Comput. Graph. 13(6), 1240–1247 (2007)
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This study was supported by GoPerception Open Project Funding.
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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s10586-022-03907-6
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Chen, X., Wang, H.H. & Tian, B. RETRACTED ARTICLE: Visualization model of big data based on self-organizing feature map neural network and graphic theory for smart cities. Cluster Comput 22 (Suppl 6), 13293–13305 (2019). https://doi.org/10.1007/s10586-018-1848-1
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DOI: https://doi.org/10.1007/s10586-018-1848-1