Research on the Deep Recognition of Urban Road Vehicle Flow Based on Deep Learning

Shuanfeng Zhao, Chao Wang, Pei Wei and Qingqing Zhao
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Shuanfeng Zhao: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Chao Wang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Pei Wei: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Qingqing Zhao: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Sustainability, 2020, vol. 12, issue 17, 1-16

Abstract: At present, the recognition of vehicle flow is mainly achieved with an artificial statistical method or by intelligent recognition based on video. The artificial method requires a large amount of manpower and time, and the existing video-based vehicle flow recognition methods are only applicable to straight roads. Therefore, a deep recognition model (DERD) for urban road vehicle flow is proposed in this paper. Learning from the characteristic that the cosine distance between the feature vectors of the same target in different states is in a fixed range, we designed a deep feature network model (D-CNN) to extract the feature vectors of all vehicles in the traffic flow and to intelligently determine the real-time statistics of vehicle flow based on the change of distance between vectors. A detection and tracking model was built to ensure the stability of the feature vector extraction process and to obtain the behavior trajectory of the vehicle. Finally, we combined the behavior and the number of vehicle flows to achieve the deep recognition of vehicle flow. After testing with videos recorded in actual scenes, the experimental results showed that our method can intelligently achieve the deep recognition of urban road vehicle flow. Compared with the existing methods, our approach shows higher accuracy and faster real-time performance.

Keywords: vehicle flow; urban road; network; behavior; feature vectors; statistics (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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