Abstract
Traffic congestion has become a worldwide problem, seriously restricting the performance of transport network. Prediction of traffic congestion levels composes a significant part of traffic management for improving network performance. With respect to congestion level prediction, although there have been many works, only a few are conducted from the view of overall status of the road network. In this paper, in order to achieve congestion level forecasting, a novel methodology is proposed, which is based on treating network status transformation as a video and processing traffic data from the view of video processing technology. By implementing the model, the experiments based on traffic simulator VISSIM obtain high accuracy (above 0.89), proving the effectiveness of the proposed method.
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Xu, W., Yang, G., Li, F., Yang, Y. (2018). Traffic Congestion Level Prediction Based on Video Processing Technology. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_95
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DOI: https://doi.org/10.1007/978-3-319-77383-4_95
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