Abstract
Traffic state identification of road networks is always a key issue in intelligent transportation systems. Most of the traffic state identification methods use fixed traffic state thresholds to identify the traffic state of the road networks, which are set manually. The accuracy of the traffic state identification depends heavily on these thresholds. Namely, better traffic state thresholds can greatly improve the traffic state identification accuracy. Thus, it is very important to select reasonable traffic state thresholds. In this paper, an adaptive method for generating the traffic state thresholds on road networks is proposed, which can automatically generate reasonable traffic state thresholds and ensure the traffic state identification accuracy. In the proposed method, the traffic state is predicted by constructing a Hidden Markov Model firstly, and then the distribution of traffic state on speed is fitted by a two-dimensional Gaussian distribution. Finally, the thresholds are generated according to the properties of two-dimensional Gaussian distribution. The rationality of the traffic state thresholds is verified by experiments. Also, the traffic state identification accuracy using these thresholds is evaluated by experiments. All the results show that the proposed method can generate reasonable traffic state thresholds and ensure the traffic state identification accuracy of the road networks.
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Acknowledgments
This work is supported by China NSFC Program under Grant NO. 61603257 and 61906121.
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Wu, J., Zhu, W., Xiao, J. (2023). An Adaptive Method for Generating the Traffic State Thresholds on Road Networks. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14089. Springer, Singapore. https://doi.org/10.1007/978-981-99-4752-2_2
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DOI: https://doi.org/10.1007/978-981-99-4752-2_2
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