Traffic Vehicle Counting in Jam Flow Conditions Using Low-Cost and Energy-Efficient Wireless Magnetic Sensors.
The jam flow condition is one of the main traffic states in traffic flow theory and the most difficult state for sectional traffic information acquisition. Since traffic information acquisition is the basis for the application of an intelligent transportation system, research on traffic vehicle counting methods for the jam flow conditions has been worthwhile. A low-cost and energy-efficient type of multi-function wireless traffic magnetic sensor was designed and developed. Several advantages of the traffic magnetic sensor are that it is suitable for large-scale deployment and time-sustainable detection for traffic information acquisition. Based on the traffic magnetic sensor, a basic vehicle detection algorithm (DWVDA) with less computational complexity was introduced for vehicle counting in low traffic volume conditions. To improve the detection performance in jam flow conditions with a "tailgating effect" between front vehicles and rear vehicles, an improved vehicle detection algorithm (SA-DWVDA) was proposed and applied in field traffic environments. By deploying traffic magnetic sensor nodes in field traffic scenarios, two field experiments were conducted to test and verify the DWVDA and the SA-DWVDA algorithms. The experimental results have shown that both DWVDA and the SA-DWVDA algorithms yield a satisfactory performance in low traffic volume conditions (scenario I) and both of their mean absolute percent errors are less than 1% in this scenario. However, for jam flow conditions with heavy traffic volumes (scenario II), the SA-DWVDA was proven to achieve better results, and the mean absolute percent error of the SA-DWVDA is 2.54% with corresponding results of the DWVDA 7.07%. The results conclude that the proposed SA-DWVDA can implement efficient and accurate vehicle detection in jam flow conditions and can be employed in field traffic environments.
Key Laboratory for Traffic and Transportation Security of Jiangsu Province, Huaiyin Institute of Technology, Huai'an 223003, China. baoxu@hyit.edu.cn.
Xu
X
Bao
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China. lihaijian@bjut.edu.cn.|Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA. lihaijian@bjut.edu.cn.
Haijian
H
Li
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310014, China. dongweixu@zjut.edu.cn.
Dongwei
D
Xu
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China. jialm@vip.sina.com.
Limin
L
Jia
Department of Civil and Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA. bran@wisc.edu.
Bin
B
Ran
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China. jrong@bjut.edu.cn.
Jian
J
Rong
jam flow; traffic engineering; vehicle counting; vehicle detection algorithm; wireless magnetic sensor