Measuring Real-Time Road Traffic Queue Length: A Reliable Approach Using Ultrasonic Sensor

被引:4
|
作者
Mandal, Avirup [1 ]
Sadhukhan, Pampa [1 ]
Gaji, Firoj [2 ]
Sharma, Prolay [3 ]
机构
[1] Jadavpur Univ, Sch Mobile Comp & Commun, Kolkata 700032, India
[2] Indian Inst Technol Kharagpur, Dept Comp Sci & Engn, Kharagpur 721302, W Bengal, India
[3] Jadavpur Univ, Dept Instrumentat & Elect Engn, Kolkata 700032, India
关键词
Congestion detection; Congestion density; Traffic queue; Vehicle; Ultrasonic sensor node (USN);
D O I
10.1007/978-981-15-0829-5_38
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Traffic congestion not only lengthens the travel time of a commuter but also increases the fuel consumption as well as air pollution. Thus, traffic congestion management has drawn significant research interest over the past few years. On the other hand, a reliable estimation of the congestion density via traffic queue length measurement at any segment of the road in real time is highly desirable to control the traffic congestion in the urban areas. Thus in this paper, we focus on how to estimate the congestion density in a reliable way and propose a real-time traffic queue length measuring approach for the signalized intersections using ultrasonic sensor node (USN). The experimental results demonstrate that our proposed approach achieves almost 100% success in vehicle detection if the vehicle queue is formed within a distance of 2.5 m from the position of USN. Moreover, our proposed approach also attains high success rate in the detection of vehicle queue even when USN is placed 2 m away from the vehicle queue and it lies in between two vehicles by having the intervehicle distance falls within the range of 30-80 cm.
引用
下载
收藏
页码:391 / 398
页数:8
相关论文
共 50 条
  • [1] Queue-Length Estimation Using Real-Time Traffic Data
    Amini, Zahra
    Pedarsani, Ramtin
    Skabardonis, Alexander
    Varaiya, Pravin
    2016 IEEE 19TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), 2016, : 1476 - 1481
  • [2] A probability approach for estimating real-time queue length at lane level
    Guo, Yajuan
    Yang, Licai
    2017 CHINESE AUTOMATION CONGRESS (CAC), 2017, : 3126 - 3129
  • [3] Real-Time Traffic Density Estimation without Reliable Side Road Data
    Ajitha, T.
    Vanajakshi, L.
    Subramanian, S. C.
    JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2015, 29 (02)
  • [4] REAL-TIME IMAGE-PROCESSING APPROACH TO MEASURE TRAFFIC QUEUE PARAMETERS
    FATHY, M
    SIYAL, MY
    IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING, 1995, 142 (05): : 297 - 303
  • [5] A Distributed Sensor Network for Real-Time Acoustic Traffic Monitoring and Early Queue Detection
    Barbagli, B.
    Manes, I. Magrini G.
    Manes, A.
    Langer, G.
    Bacchi, M.
    2010 FOURTH INTERNATIONAL CONFERENCE ON SENSOR TECHNOLOGIES AND APPLICATIONS (SENSORCOMM), 2008, : 173 - 178
  • [6] Real-time Road Traffic Density Estimation using Block
    Garg, Kratika
    Lam, Siew-Kei
    Srikanthan, Thambipillai
    Agarwal, Vedika
    2016 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV 2016), 2016,
  • [7] An image processing based approach for Real-time road traffic applications
    Siyal, M. Y.
    Choudhary, B. S.
    Rajput, A. K.
    Proceedings of the INMIC 2005: 9th International Multitopic Conference - Proceedings, 2005, : 616 - 619
  • [8] Estimating online vacancies in real-time road traffic monitoring with traffic sensor data stream
    Wang, Feng
    Hu, Liang
    Zhou, Dongdai
    Sun, Rui
    Hu, Jiejun
    Zhao, Kuo
    AD HOC NETWORKS, 2015, 35 : 3 - 13
  • [9] An Adaptive Approach for Real-Time Road Traffic Congestion Detection Using Adaptive Background Extraction
    Al-Najdawi, Nijad
    Abu-Roman, Asma
    Tedmori, Sara
    Al-Najdawi, Mohammad
    INTERNATIONAL ARAB JOURNAL OF INFORMATION TECHNOLOGY, 2016, 13 (6B) : 1075 - 1083
  • [10] Real-Time Queue Length Estimation With Trajectory Reconstruction Using Surveillance Data
    Sheng, Zihao
    Xue, Shibei
    Xu, Yunwen
    Li, Dewei
    16TH IEEE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2020), 2020, : 124 - 129