Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (1): 93-97.

### Road vehicle congestion analysis model based on YOLO

1. College of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
• Received:2018-07-19 Revised:2018-08-24 Online:2019-01-10 Published:2019-01-21
• Supported by:
This work is partially supported by the National Natural Science Foundation of China (61572325, 60970012), the Research Fund for the Doctoral Program of Higher Education of China (20113120110008), the Key Science and Technology Research Project of Shanghai (14511107902, 16DZ1203603), the Construction Project of Shanghai Engineering Center (GCZX14014), the Project of Shanghai Smart Home Large-scale Common Technology Engineering Center (GCZX14014), the Shanghai Top-class Discipline Construction Project (XTKX2012), the Hujiang Foundation of China (C14001).

### 基于YOLO的道路车辆拥堵分析模型

1. 上海理工大学 光电信息与计算机工程学院, 上海 200093
• 通讯作者: 陈庆奎
• 作者简介:张家晨(1993-),男,四川成都人,硕士研究生,主要研究方向:人工智能、GPU并行计算;陈庆奎(1966-),男,黑龙江哈尔滨人,教授,博士生导师,博士,CCF会员,主要研究方向:网络计算、物联网、并行计算、GPU集群。
• 基金资助:
国家自然科学基金资助项目（61572325，60970012）；高等学校博士学科点专项科研博导基金资助项目（20113120110008）；上海重点科技攻关项目（14511107902，16DZ1203603）；上海市工程中心建设项目（GCZX14014）；上海智能家居大规模物联共性技术工程中心项目（GCZX14014）；上海市一流学科建设项目（XTKX2012）；沪江基金研究基地专项（C14001）。

Abstract: To solve traffic congestion problems, a new road condition judgment model was proposed. Firstly, the model was based on YOLOv3 target detection algorithm. Then, according to the eigenvalue matrix corresponding to the picture, the difference between adjacent frames was made by the eigenvalue matrix, and the difference value was compared with preset value to determine whether the current road was in a congested state or a normal traffic state. Secondly, the current calculated road state was compared with previous two calculated road states. Finally, the state statistics method in the model was used to calculate the duration of a state (congestion or patency) of road. The proposed model could analyze the states of three lanes of a road at the same time. Through experiments, the average accuracy of model to judge the state of single lane could reach 80% or more, and it was applicable to both day and night roads.

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