Abstract
The network of city Tunnel monitoring systems has accumulated a great deal of multi-source heterogeneous monitoring data, which mainly consists of video data, traffic data, environmental data, sensor data and so on. This paper discusses how to use the monitoring data to realize city tunnel traffic situation awareness. First, we need to mine a set of strong associated characteristic groups according to the association rules, and then determine the corresponding traffic awareness for each characteristic group to build our training data set for machine learning methods. Finally we can aware the situation of city tunnel traffic by using machine learning methods. We solve the problem of how to build the training data in the background of complex multi-source data, which is a prerequisite for many machine learning methods.
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References
Endsley, M.R.: Design and evaluation for situation awareness enhancement. In: Human Factors Society 32nd Annual Meeting, Santa Monica, CA, pp. 97–101 (1988)
Wang, H., et al.: A review on Situation awareness of Internet. Computer Science 33(10), 5–10 (2006)
Stanton, N.A., Chambers, P.R.G., Piggott, J.: Situational awareness and safety. Safety Science (39), 189–204 (2001)
Endsley, M.R., Sollenberger, R., Stein, E.: Situation awareness: A comparison of measures. In: Proceedings of the Human Performance, Situation Awareness and Automation: User-Centered Design for the New Millennium (2000)
Endsley, M.R., Rodgers, M.D.: Distribution of attention, situation awareness, and workload in a passive air traffic control task: Implications for operational errors and automation. Air Traffic Control Quarterly 6(1), 21–44 (1998)
Lan, F., Chunlei, W., Guoqing, M.: A Framework for Network Security Situation Awareness Based on Knowledge Discovery. In: International Conference on Computer Engineering and Technology (ICCET), pp. 226–231 (2010)
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp. 207–216 (1993)
Gu, S., Guo, Y.: Learning SVM Classifiers with Indefinite Kernels. AAAI (2012)
Wassantachat, T., Li, Z., Chen, J., Wang, Y., Tan, E.: Traffic Density Estimation with On-line SVM Classifier. In: AVSS, pp.13–18 (2009)
Kumaraswamy, R., Prabhu, L.V., Suchithra, K., Pai, P.S.S.: SVM Based Classification of Traffic Signs for Realtime Embedded Platform. In: ACC, pp. 339–348 (2011)
Xie, Y., Tang, S., Huang, X.: A new model for generating burst traffic based on hierarchical HMM. In: FSKD, pp. 2212–2216 (2011)
Fang, H.: Support vector machine multi-classification algorithm 2009(4) fragments based on the SVM algorithm. In: BMEI 2011, pp. 1738–1742 (2011)
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Li, L., Wu, W., Zhong, L. (2013). Training Data of City Tunnel Traffic Situation Awareness. In: Yang, Y., Ma, M., Liu, B. (eds) Information Computing and Applications. ICICA 2013. Communications in Computer and Information Science, vol 392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53703-5_10
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DOI: https://doi.org/10.1007/978-3-642-53703-5_10
Publisher Name: Springer, Berlin, Heidelberg
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