Abstract
The road traffic condition information includes some important traffic control information such as U-turn or left turn which affect driver’s travel. We proposed a novel way to identify road intersection and traffic control information through analyzing floating car trajectory data automatically and timely. First, a difference-based algorithm is proposed to filter the outlier trajectory data. Then a map matching method based on three-level grid is applied. Finally, an automatic algorithm is developed to recognize the road traffic control information timely. The entire floating trajectory data of Fuzhou about 1.5 million records are used to verify the proposed method. Experiment result indicates that the method has high efficiency and accuracy rate. We construct this system based on trajectory data by 6000 taxis a day. The results of the operation show that the correct rate is high 87.7%, which indicates that it is very valuable.
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References
Hofleitner, A., Herring, R., Abbeel, P., Bayen, A.: Learning the dynamics of arterial traffic from probe data using a dynamic Bayesian network. IEEE Intell. Transp. Syst. Soc. 13(4), 1679–1693 (2012)
Cui, Y., Ge, S.S.: Autonomous vehicle positioning with GPS inurban canyon environments. IEEE Trans. Robot. Autom. 19(1), 15–25 (2003)
Wang, Z., Lu, M., Yuan, X., et al.: Visual traffic jam analysis based on trajectory data. IEEE Trans. Visual Comput. Graphics 19(12), 2159–2168 (2013)
Yuan, J., Zheng, Y., Xie, X., Sun, G.: Driving with knowledge from the physical world. In: 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 316–324. CA, San Diego (2011)
Zheng, Y., Xie, X., Ma, W.Y.: Geolife: a collaborative social networking service among user, location and trajectory 33(2), 32–39 (2010)
Calabrese, F., Diao, M., Di Lorenzo, G., et al.: Understanding individual mobility patterns from urban sensing data: a mobile phone trace example 26(1), 301–313 (2013)
Rong, H: Congestion prediction on rapid transit system based on weighted resample deep neural network. In: The Euro-China Conference on Intelligent Data Analysis and Applications, pp. 586–593. Springer, Cham (2018)
Rong, H., Ye, X.: Traffic condition recognition based on vehicle trajectory big data. J. Internet Technol. 18(7), 1587–1596 (2017)
Li, X., Han, J., Lee, J.G., et al.: Traffic density-based discovery of hot routes in road networks. In: International Symposium on Spatial and Temporal Databases, pp. 441–459. Springer, Berlin (2007)
Hu, R., Xia, Y., Kuang, F.J.: Real-time path planning based on dynamic traffic information. In: The Euro-China Conference on Intelligent Data Analysis and Applications, pp. 388–393. Springer, Cham (2017)
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Fang, W., Chen, H., Hu, R. (2020). A Novel Approach to Identify Intersection Information via Trajectory Big Data Analysis in Urban Environments. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 156. Springer, Singapore. https://doi.org/10.1007/978-981-13-9714-1_21
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DOI: https://doi.org/10.1007/978-981-13-9714-1_21
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