期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 180, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2021.115037
关键词
Traffic congestion; Convolutional neural network; Vehicle detection; Deep learning; Traffic flow parameter
类别
资金
- National Key Research and Development Program of China [2019YFB1600100]
- National Natural Science Foundation of China [61973045]
- Shaanxi Province Key Development Project [S2018YFZDGY0300]
- Fundamental Research Funds for the Central Universities [300102248403]
- Joint Laboratory of Internet of Vehicles - Ministry of Education [213024170015]
- Joint Laboratory of Internet of Vehicles - China Mobile [213024170015]
- Application of Basic Research Project for National Ministry of Transport [2015319812060]
This study proposes a new image-based traffic congestion estimation method, which first defines the traffic congestion accurately and integrates a traffic parameter layer into a CNN model. By training and testing with a large dataset of traffic images, the proposed method shows better efficiency and stability in various traffic conditions and weather scenarios.
Traditional image-based traffic congestion estimation methods generally include two steps, which first extract the vehicles from the surveillance images, then calculate the congestion index using the vehicle counts. When working with vast amount of video frames, these approaches are time-consuming and hardly guarantee the real time detection of traffic congestion. In this study, firstly a specific and accurate definition of traffic congestion is proposed to quantify the level of traffic congestion. Then we construct an image-based traffic congestion estimation framework, in which a traffic parameter layer is integrated to the basic convolutional neural network (CNN) model. The proposed framework can directly perform traffic congestion calculation and estimation, which shortens the processing time and avoids the complicated postprocessing. A dataset of 1400 traffic images including 66,890 vehicles is collected for training the proposed CNN model. Another new dataset of 2400 traffic images including 113,516 vehicles is collected to test the proposed method on estimating traffic congestion. Experimental results show that our proposed approach has better efficiency and stability in both free flow and congested traffic conditions, as well as sunny and rainy scenes.
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