Queueing Process Sensing and Prediction at Intersection Based on Video

被引：0
|
作者：
Yu Z. [1 ,2 ]
Huang L.-H. [1 ,2 ]
Li X.-Y. [1 ,2 ]
Li B. [1 ,2 ]
Zou B. [1 ,2 ]
机构：
[1] Research Center of Intelligent Transport System, Sun Yat-Sen University, Guangzhou
[2] Guangdong Provincial Key Laboratory of Intelligent Transport System, Guangzhou
来源：
Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology | 2020年 / 20卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Intelligent transportation; Queueing process; Traffic parameters; Urban intersection; Vehicle trajectory reconstruction;
D O I：
10.16097/j.cnki.1009-6744.2020.01.006
中图分类号：
学科分类号：
摘要：
Queueing process consisting of queueing, accumulation, and disappearing phase plays a very important role for the analysis and optimization of traffic management and signal controls. However, most of existing methods are lack of detailed sensing and correlation between different queue stages, which leads to uncomprehensive and rough detection in traffic parameters. In this paper, we present a video-based method, by sensing in stages first and correlating later to sense and predict queueing process. Firstly, the detection algorithm of objects and edges are adopted to obtain information of road and vehicle. Secondly, fusing road information and vehicle edges to detect queueing area and queueing length. Finally, reconstructing vehicle trajectory by sensing and correlating different queue stages, which are divided by queueing area. On this basis, the various parameters are calculated with vehicle trajectory, and the experimental results show that the reconstruction accuracy reaches 95.7% when there are no detection errors, in addition, proposed method is robust in detecting various traffic parameters in practical applications. Copyright © 2020 by Science Press.
引用
收藏
页码：33 / 39
页数：6
相关论文
共 11 条
  • [1] Zanin M., Messelodi S., Et al., An efficient vehicle queue detection system based on image processing, 12th International Conference on Image Analysis & Processing, (2003)
  • [2] Satzoda R.K., Suchitra S., Et al., Vision-based vehicle queue detection at traffic junctions, 6th IEEE Conference on Industrial Electronics & Applications, (2011)
  • [3] Ma D.F., Lou X.Q., Et al., Estimating maximum queue length for traffic lane groups using travel times from video-imaging data, IEEE Intelligent Transportation Systems Magazine, (2018)
  • [4] Zhang H.L., Li K.P., Et al., The analysis of the vehicle delay model using comprehensive method, International Conference on Transportation Engineering 2009, (2009)
  • [5] Khan M.A., Ectors W., Et al., Unmanned aerial vehicle-based traffic analysis: A case study for shockwave identification and flow parameters estimation at signalized intersections, Remote Sensing, 10, 3, pp. 458-474, (2018)
  • [6] Cao Q.X., Luo D.Y., Wang Z.W., Detection of traffic operation evaluation parameters based on video at urban signalized intersections, Journal of Transportation Systems Engineering and Information Technology, 15, 6, pp. 60-66, (2015)
  • [7] Zhang Y., Chen Y.Z., Et al., Extraction method of traffic parameters based on detection traffic waves at intersection, The 3rd International Conference on Transportation Information and Safety, (2015)
  • [8] Yang D.L., Chen Y.Z., Et al., Real-time detecting and tracking of traffic shockwaves based on weighted consensus information fusion in distributed video network, IET Intelligent Transport Systems, 8, 4, pp. 377-387, (2014)
  • [9] Wang Z.B., Research and application of scene understanding in traffic monitoring, (2013)
  • [10] Zhu J.L., Ai Y.F., Et al., Real-time vehicle queue estimation of large-scale traffic scene, 20172nd International Conference on Image, Vision and Computing, (2017)
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