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Dual roadside seismic sensor for moving road vehicle detection and characterization.

Wang H, Quan W, Wang Y, Miller GR - Sensors (Basel) (2014)

Bottom Line: In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT).The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle.Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

View Article: PubMed Central - PubMed

Affiliation: School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73 Huanghe Rd., NanGang Dist., Harbin 150090, China. wanghua@hit.edu.cn.

ABSTRACT
This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization information, including vehicle speed, axle spacing, detection of both vehicle axles and moving direction, can also be extracted from the collected seismic signals as demonstrated in this paper. The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle. Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

No MeSH data available.


TDOA estimation for moving sources. Results correspond to captured vehicle data depicted in Figure 2.
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f3-sensors-14-02892: TDOA estimation for moving sources. Results correspond to captured vehicle data depicted in Figure 2.

Mentions: Figure 3 shows the estimation of TDOA for the interval period shown in Figure 2. The peak shown in Figure 3 indicates that there is a seismic source whose TDOA is 3.46 × 10−5 s.


Dual roadside seismic sensor for moving road vehicle detection and characterization.

Wang H, Quan W, Wang Y, Miller GR - Sensors (Basel) (2014)

TDOA estimation for moving sources. Results correspond to captured vehicle data depicted in Figure 2.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3958236&req=5

f3-sensors-14-02892: TDOA estimation for moving sources. Results correspond to captured vehicle data depicted in Figure 2.
Mentions: Figure 3 shows the estimation of TDOA for the interval period shown in Figure 2. The peak shown in Figure 3 indicates that there is a seismic source whose TDOA is 3.46 × 10−5 s.

Bottom Line: In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT).The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle.Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

View Article: PubMed Central - PubMed

Affiliation: School of Transportation Science and Engineering, Harbin Institute of Technology, No. 73 Huanghe Rd., NanGang Dist., Harbin 150090, China. wanghua@hit.edu.cn.

ABSTRACT
This paper presents a method for using a dual roadside seismic sensor to detect moving vehicles on roadway by installing them on a road shoulder. Seismic signals are split into fixed time intervals in recording. In each interval, the time delay of arrival (TDOA) is estimated using a generalized cross-correlation approach with phase transform (GCC-PHAT). Various kinds of vehicle characterization information, including vehicle speed, axle spacing, detection of both vehicle axles and moving direction, can also be extracted from the collected seismic signals as demonstrated in this paper. The error of both vehicle speed and axle spacing detected by this approach has been shown to be less than 20% through the field tests conducted on an urban street in Seattle. Compared to most existing sensors, this new design of dual seismic sensor is cost effective, easy to install, and effective in gathering information for various traffic management applications.

No MeSH data available.