Portable Roadside Sensors for Vehicle Counting, Classification, and Speed Measurement

被引:126
作者
Taghvaeeyan, Saber [1 ]
Rajamani, Rajesh [1 ]
机构
[1] Univ Minnesota, Minneapolis, MN 55455 USA
关键词
Magnetic sensors; portable traffic sensor; roadside traffic sensor; vehicle classification; vehicle detection; vehicle speed measurement; MODEL;
D O I
10.1109/TITS.2013.2273876
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper focuses on the development of a portable roadside magnetic sensor system for vehicle counting, classification, and speed measurement. The sensor system consists of wireless anisotropic magnetic devices that do not require to be embedded in the roadway-the devices are placed next to the roadway and measure traffic in the immediately adjacent lane. An algorithm based on a magnetic field model is proposed to make the system robust to the errors created by larger vehicles driving in the nonadjacent lane. These false calls cause an 8% error if uncorrected. The use of the proposed algorithm reduces this error to only 1%. Speed measurement is based on the calculation of the cross correlation between longitudinally spaced sensors. Fast computation of the cross correlation is enabled by using frequency-domain signal processing techniques. An algorithm for automatically correcting for any small misalignment of the sensors is utilized. A high-accuracy differential Global Positioning System is used as a reference to measure vehicle speeds to evaluate the accuracy of the speed measurement from the new sensor system. The results show that the maximum error of the speed estimates is less than 2.5% over the entire range of 5-27 m/s (11-60 mi/h). Vehicle classification is done based on the magnetic length and an estimate of the average vertical magnetic height of the vehicle. Vehicle length is estimated from the product of occupancy and estimated speed. The average vertical magnetic height is estimated using two magnetic sensors that are vertically spaced by 0.25 m. Finally, it is shown that the sensor system can be used to reliably count the number of right turns at an intersection, with an accuracy of 95%. The developed sensor system is compact, portable, wireless, and inexpensive. Data are presented from a large number of vehicles on a regular busy urban road in the Twin Cities, MN, USA.
引用
收藏
页码:73 / 83
页数:11
相关论文
共 25 条
  • [1] Ahdi F., 2012, Traffic data collection and anonymous vehicle detection using wireless sensor networks
  • [2] Alpaydin E., 2010, Introduction to Machine Learning, V2
  • [3] Wireless sensor networks for traffic monitoring in a logistic centre
    Bottero, M.
    Dalla Chiara, B.
    Deflorio, F. P.
    [J]. TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, 2013, 26 : 99 - 124
  • [4] Caruso M. J., 1999, Proceedings SENSORS EXPO Baltimore, P477
  • [5] Vehicle detection and classification using model-based and fuzzy logic approaches
    Cheng, Heng-Da
    Du, Haining
    Hu, Liming
    Glazier, Chris
    [J]. INFORMATION SYSTEMS AND TECHNOLOGY, 2005, (1935): : 154 - 162
  • [6] Traffic measurement and vehicle classification with single magnetic sensor
    Cheung, SY
    Coleri, S
    Dundar, B
    Ganesh, S
    Tan, CW
    Varaiya, P
    [J]. DATA INITIATIVES, 2005, (1917): : 173 - 181
  • [7] Ding N, 2008, PROCEEDINGS OF THE 11TH INTERNATIONAL IEEE CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, P1015, DOI 10.1109/ITSC.2008.4732648
  • [8] Engelberg S, 2008, SIGNALS COMMUN TECHN, P3
  • [9] Detection and classification of vehicles
    Gupte, S
    Masoud, O
    Martin, RFK
    Papanikolopoulos, NP
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2002, 3 (01) : 37 - 47
  • [10] Vehicle Classification Method Based on Single-Point Magnetic Sensor
    He, Yao
    Du, Yuchuan
    Sun, Lijun
    [J]. 8TH INTERNATIONAL CONFERENCE ON TRAFFIC AND TRANSPORTATION STUDIES (ICTTS), 2012, 43 : 618 - 627