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Abstract—Quantum tunneling composites (QTCs) are extremely sensitive to external force due to the quantum tunnel effect and have been developed into various sensors used in many fields such as force sensors and tactile sensors. In this paper, a new type of traffic detection sensor is developed based on QTCs which are fabricated by adding spiky spherical nickel powders as functional fillers into silicone rubber. The developed sensors with different sensitivities were incorporated in an experimental road and a real road, and their performances for traffic detection were investigated. Test results show that the QTC sensors with high sensitivity can accurately detect the passing of different vehicles under different vehicular speeds and test environments. The double-rows QTC sensors can measure vehicle speeds easily and precisely. It is possible to measure vehicle speeds with one QTC sensor per lane. A lot of other traffic data including vehicle presence, vehicle weigh-in-motion, position, vehicle occupancy rate, vehicle count and vehicle classification in real-time traffic can be achieved by the QTC sensors with the advantages of high detection precision, fast response and recovery excellent robustness, energy saving, easy installation and maintenance. The obtained results prove the applicability of the QTC sensors for long-term real-time traffic parameter detection
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1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
1
Abstract—Quantum tunneling composites (QTCs) are
extremely sensitive to external force due to the quantum tunnel
effect and have been developed into various sensors used in many
fields such as force sensors and tactile sensors. In this paper, a
new type of traffic detection sensor is developed based on QTCs
which are fabricated by adding spiky spherical nickel powders as
functional fillers into silicone rubber. The developed sensors with
different sensitivities were incorporated in an experimental road
and a real road, and their performances for traffic detection were
investigated. Test results show that the QTC sensors with high
sensitivity can accurately detect the passing of different vehicles
under different vehicular speeds and test environments. The
double-rows QTC sensors can measure vehicle speeds easily and
precisely. It is possible to measure vehicle speeds with one QTC
sensor per lane. A lot of other traffic data including vehicle
presence, vehicle weigh-in-motion, position, vehicle occupancy
rate, vehicle count and vehicle classification in real-time traffic
can be achieved by the QTC sensors with the advantages of high
detection precision, fast response and recovery excellent
robustness, energy saving, easy installation and maintenance. The
obtained results prove the applicability of the QTC sensors for
long-term real-time traffic parameter detection.
Index Terms—Nickel, quantum tunneling composites, traffic
detection, sensors, smart materials.
I. INTRODUCTION
ITH the dramatic increase in traffic volume and the
limited construction of transport facilities, traffic
congestion, traffic violation, traffic incident and road damage
become significant challenges to traffic safety and public
facility management. Since these fluctuate continuously, it is
requisite to detect traffic information for traffic control and
management in real-time manner. Traffic detection is a
fundamental part of modern intelligent transportation systems
(ITS) that provide comprehensive traffic data including traffic
This work was supported in part by the National Science Foundation of
China (grant Nos. 51428801 and 51178148), the Program for New Century Excellent Talents in University of China (grant No. NCET-11-0798), and the
Fundamental Research Funds for the Central Universities (DUT15LK09).
Baoguo Han, Siqi Ding, Yan Yu and Sufen Dong are with the School of Civil Engineering, Dalian University of Technology, Dalian, 116024 China (
email: [email protected],[email protected]; dsq19910909@mail.
dlut.edu.cn; [email protected]; [email protected]). Xun Yu is with the Department of Mechanical and Energy Engineering,
University of North Texas, Denton, TX 76203 USA (email: [email protected]).
Jinping Ou is with the School of Civil Engineering, Harbin Institute of Technology, Harbin, 150001 China (email: [email protected]).
presence, volume, speed, density, headway, occupancy and
classification for traffic control and planning. The surveillance
data is commonly collected by many kinds of traffic detection
sensors. Currently, inductive loops and video cameras are the
most commonly used sensing technologies. Although inductive
loop detectors can detect the passage and presence of vehicles
precisely and cost-effectively, the main drawback of the
equipment is that it requires two loops per lane to measure
vehicle speeds and have much maintenance issues due to the
incompatibility with concrete. Moreover, the loop is
susceptible to external electromagnetic interference.
Compared to inductive loops, video sensors are easy to install
and can yield additional traffic information. However, the
detection accuracy is poor due to their poor robustness,
especially in sophisticated traffic conditions. High equipment
cost, high maintenance cost, and poor performance in
inclement weather such as rain, fog, and snow also limit video
sensors for unrestrained application[1-5]. Recently, Han et al.
proposed a self-sensing pavement fabricated with
piezoresistive multi-walled carbon nanotubes reinforced
cement-based materials. As being integrated into the pavement,
the sensor has great potential for traffic-monitoring
applications. However, it is high cost and needs a complicated
and time-consumed preparation process due to the difficulty in
the dispersion of the nano-sized functional fillers [6-8].
Therefore, novel sensor technologies are strongly desired to
accomplish efficient traffic surveillance and control
management.
Conductive rubber-based composites have been developed
into various sensors used in many fields such as force sensors,
gas sensors, and tactile sensors for their high sensitivity, fast
response time, low cost, easy installation, and long serve life
and corrosion resistance. They are fabricated by adding
conductive fillers including carbon (e.g. carbon blacks, carbon
fibers, carbon nanotubes, pyrolytic carbons and graphite) and
metal (e.g. nickel, gold, aluminum and silver and their metallic
oxides) into rubber-based materials [9-15]. The electrical
resistivity of conductive rubber usually decreases under
compression as the separation between the fillers becomes
close, it therefore can be sensed through measurement of the
electrical resistance. This renders it suitable for serving as
pressure-sensitive sensors, providing a new way for developing
vehicle detection sensors.
Generally, the pressure of tires on the ground caused by a
passing vehicle is not very high (<1MPa), so a high sensitivity
Design and Implementation of A Multiple Traffic
Parameter Detection Sensor Developed with
Quantum Tunneling Composites
Baoguo Han, Siqi Ding, Yan Yu, Xun Yu, Sufen Dong, Jinping Ou
W
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
2
is beneficial and necessary for valid vehicle detection. The
pressure-sensitive property of conductive rubber-based
composites depends on the type and morphology of filler
particles. Among different fillers, spiky spherical nickel
powder possessing sharp surface protrusions were found to be a
kind of perfect conductive fillers for developing rubber-based
composites with high pressure-sensitive property [9, 16-18].
Peratech Ltd [16]firstly developed the nickel rubber-based
composites also called quantum tunneling composites (QTCs),
which were fabricated by wetting spiky spherical nickel
powders with silicone rubber intimately. QTCs showed
ultrahigh pressure-sensitive property and extremely sensitive to
deformation. Under modest compression the electrical
resistance can fall drastically from about 1012-1013 Ω to less
than 1Ω due to the “quantum tunnel” effect. It means that QTCs
can switch from good insulators to good metal conductors when
suitably deformed [9, 16]. The combination of high sensitivity
and tunable electrical behavior from the insulating to
conductive, makes QTCs the ideal candidate for traffic
monitoring applications. Compared to the electromagnetic
inductive materials and the self-sensing cement-based
composite, this kind of composite is superior in many other
respects, such as insensitivity to electromagnetic external field,
broad working temperature range depending on the polymer
matrix, simple and cost-effective fabrication process and very
robust to environmental impact. Therefore, QTCs have
tremendous potential to become a new type of traffic detection
sensors for a long-term or temporary, cost-effective and
accurate traffic monitoring.
In this paper, we propose a new type of traffic detection
sensor based on QTCs and investigate the performance of the
QTC sensors for traffic detection by measuring the
pressure-sensitive response of QTC sensors with different
sensitivities to different types of vehicles and different vehicle
speeds on an experimental road. Furthermore, a road test of
real-time traffic is performed on a dual-lane road to investigate
the feasibility of the QTC sensors for practical applications.
II. MATERIALS AND EXPERIMENTAL METHOD
A. Materials
The raw materials used to prepare QTCs are similar to a
previous report [9], including silicone rubber (type 781, Dow
Corning, USA) and spiky spherical nickel powders (type 123,
Inco Ltd, CA). The typical physical characteristics of spiky
spherical nickel powders and silicone rubber after solidification
are given in Table Ⅰ. Previous researches have shown that the
sensitivity of QTCs is correlated with the concentration of the
filler particles in insulating matrix materials. The higher filler
loading means that there are more physical contacts to form
conductive paths inside the composites under external forces
[19, 20]. In this paper, the weight ratios of spiky spherical
nickel powders to silicone rubber were chosen as 3.0:1 and
4.0:1. According to the process as described elsewhere [20], a
mixer was used to mix liquid silicone rubber and spiky
spherical nickel powders carefully for 10 min. The mixing
should avoid injuring sharp protrusions on the surface of spiky
spherical nickel powders [21]. After mixing, the viscous
mixture was placed in vacuum to remove trapped air, and then
molded by pressure forming to prepare the flat samples of QTC
sensors. As shown in Fig. 1, two kinds of QTCs (low-sensitivity
QTC (LS-QTC) and high-sensitivity QTC (HS-QTC)) have
different sensitivities. QTCs with higher nickel powder content
exhibit higher sensitivity.
TABLE Ⅱ
SPECIFICATION OF THE QTC SENSORS (UNIT: mm)
Component Length Width Thickness Spacing
Steel plate 1200 150 1 -
Copper electrode 1000 10 0.1 - QTC (integrated) 1000 5 1 - QTC (distributed) 250 5 1 25
Fig. 2. Schematic illustration of the structure of the QTC sensors. (a) Layout of the QTC sensors. (b) Distributed-sensors. (c) Integrated-sensors.
TABLE I TYPICAL PHYSICAL CHARACTERISTICS OF SPIKY SPHERICAL NICKEL
POWDERS AND SILICONE RUBBER AFTER SOLIDIFICATION
Spiky spherical
nickel
powder
Fisher sub-sieve
size (µm)
Bulk density
(g/cm3)
Electrical resistivity
(Ω·cm)
3-7 1.8-2.7 6.84×10-4
Silicone
rubber
Density (g/cm3)
Tensile strength (MPa)
Ductility (%)
1.087 6.7 400
0.0 0.5 1.0 1.5 2.00.1
1000
1E7
1E11
1E15
w3.0:1
w4.0:1
lg R(
oh
m)
Compressive stress(MPa)
Fig. 1. Effect of content of spiky spherical nickel powders on the
pressure-sensitive property of QTCs (wn:1 denotes weight ratio of spiky
spherical nickel powders to silicone rubber is n. Compressive experiment was
performed by using a Shimadzu AG-250KN I mechanical testing machine
(Shimadzu, Japan). Electrical resistance was measured with a Keithley 2100
(Keithley Instruments, USA) multimeter using a two-probe method. Testing
speciman size is 2cm×2cm).
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
3
B. Fabrication of the QTC sensors
Fig. 2 shows the schematical structure of the QTC sensors.
As shown in Fig. 2(a), they were fabricated by mounting the
QTC sheets between two copper electrodes with super glue
(502 cyanoacrylate adhesive super glue). A steel plate is
arranged at the bottom of the QTC sensors serves as a support
platform to avoid stress concentration and protect the sensors.
Two kinds of QTC sensors, integrated-sensors and
distributed-sensors, were fabricated in this paper. For the
integrated-sensors as shown in Fig. 2(c), the QTCs were
processed to form 1000mm×5mm×1mm of elongated sheets.
For the distributed-sensors, as shown in Fig. 2(b), the only
difference is that the length of the QTC sheets and the copper
electrodes were shortened into 250mm evenly. Three pieces of
QTCs with this size were distributed in a straight line along the
surface of the steel plate with separation of 25mm. Two electric
wires were embedded into the two electrodes of each QTC
sensor for measurement. After all the wires were embedded, the
QTC sensors were encapsulated with rubberized fabric along
the surface and edges for fixing and protection. The detailed
specification of the QTC sensors is given in Table Ⅱ.
C. Measurement
Road tests for the sensing property of the QTC sensors were
conducted at a newly-built experimental pavement in Dalian
University of Technology (DLUT) and a local dual-lane
pavement next to the DLUT for real-time traffic monitoring.
The prepared QTC sensors were fixed on the road with
transparent rubberized fabric in parallel with spacing of 2 m.
The sensors array covers half of the road surface in width to
ensure that at least one sensor can be passed over by the
vehicle’s tire. Fig. 3 shows road test sites. Two test vehicles that
have different weights and wheelbases were used to pass over
the fixed QTC sensors for compression in experimental road
test. The detailed parameters and actual appearance of the two
test vehicles are listed and depicted in Table Ⅲ and Fig. 4,
respectively.
The sensing mechanism of the QTC sensors is that the
electrical resistivity of the sensors would change when the
sensors are subjected to external forces. The QTC sensors in
unstressed state are near-perfect insulator as the intimate
coating of rubber is a non-conductive matrix material that
prevents the nickel powders from direct physical contact, thus
conductive network fails to form in the composites. In addition,
the sharp nano-tips on the surface of spiky spherical nickel
powders are restrained by the rubber coating. Once the QTC
sensors are deformed by compression, the inter-particle
separation distance between the nickel particles is reduced,
increasing the amount of conductive paths across the matrix
material. The most dominant factor is that the sharp surface
features of the nickel particle are released when deformed. A
high local electrical field is generated around sharp surface of
the nickel particle, i.e. the barrier height and breadth between
the nickel particles decrease and the potential energy of charge
carriers through tunneling barrier increases when the external
constant voltage injected into the both ends of the QTC sensors.
As a result, the electrical resistivity of the QTC sensors
exponentially decreases when the sensors are deformed [9, 10,
17, 22, 23].
As mentioned earlier[24, 25], the change in electrical
resistivity of the QTC sensors caused by vehicular passing is
the same as that in electrical resistance, which is equal to the
change in electrical voltage signal, i.e.,
where ρsi is electrical resistivity of the ith QTC sensors, Rsi is
electrical resistance of the ith QTC sensors, Usi is voltage at
both ends of the ith QTC sensors.
According to (1) and Ohm’s law, the voltage signal at both
ends of the QTC sensors was regarded as indices for detecting
passing vehicles. The measurement circuit diagram of the QTC
sensors is depicted in Fig. 5. Each QTC sensor is connected
∆ρsi
ρsi
⁄ = ∆Rsi Rsi⁄ = ∆Usi Usi⁄ (1)
Fig. 3. Road test sites. (a) Experimental road test in DLUT. (b) Real-time road
test in the city of Dalian, China.
TABLE Ⅲ
PARAMETERS OF THE VEHICLES
Vehicle model Weight (kg) Wheelbase (mm) Size (mm)
2009 Besturn B70 ~1450 2675 4705/1782/1465
2011 KIA Sportage ~1600 2630 4350/1840/1730
Fig. 4. Road test vehicles. (a) 2009 Besturn B70. (b) 2011 KIA Sportage.
Fig. 5. Signal acquisition system and measurement circuit diagram.
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
4
with a constant reference resistance (15Ω) in series for
voltage-sharing. Under the zero-pressure, the electrical
resistivity of the QTC sensors is more than 109 Ω•m, which can
be regarded as insulators [20]. The measurement circuit is an
open circuit when the QTC sensors are in non-operating state.
Therefore, the QTC sensors are energy saving.
As shown in Fig. 3 and Fig. 5, an assembled signal
acquisition system was used to collect the sensing signal of the
QTC sensors. A power supply (a 1.5V battery, Digitian, CHN)
is used to power the sensing circuit. A DAQ card (NI
USB-6009, National Instruments Corporation, USA) with a
program of Labview 2012 is used for voltage sweeps and data
collection, and a laptop is used to configure the sensors and
download archived data. The accuracy of the DAQ card is 7
mV. The sampling rate of the voltage signals is 1500 Hz. In
addition, a digital camera was used to provide a real-time
digital video of traffic for verifying test results.
III. RESULTS AND DISCUSSION
A. The response of the QTC sensors with different sensitivities
to vehicle passing
Fig. 6 shows the typical detection results of Besturn B70
passing over the QTC sensors with different sensitivities at the
same speed (20 km/h). In the voltage time-history curves, a
vehicle wheel passing over the QTC sensors generates a
significant negative peak (higher load will induce a deeper
ditch pick, i.e., smaller measured voltage), which demonstrates
that the QTC sensors are available and practicable to be used as
traffic detectors. For the QTC sensors with a specific sensitivity,
the signal generated by each vehicle changed consistently, with
high repeatability and reversibility of signal variation.
From this figure, we can also see that the responses to vehicle
passing over the QTC sensors with different sensitivities are
strikingly different. The peak point voltage of LS-QTC sensors
and HS-QTC sensors are shown in Table Ⅳ. Passing over a
HS-QTC sensor results in relatively larger fractional change in
voltage output (23.8%), showing up as a longer negative peak
than that of a LS-QTC sensor. In addition, during the test, it was
observed that there were missed peaks in the case of LS-QTC
sensors, especially in which the rear-wheel passes at a fast
speed. It is likely due to the relatively short contact time of tires
with the sensors and small contact area between the passing
vehicle tire and the sensors at a relatively fast speed. As shown
in Fig. 6 (zoom-out), the contact process of tire and sensors
consists of a response process and a recovery process, which
differs in the LS-QTC sensors and the HS-QTC sensors. Noise
captured in the response process (the second peak) and the
recovery process (the first peak) indicates that there exists delay
to some extent in the LS-QTC sensor outputs, whereas the
recovery process in the HS-QTC sensors is ultrafast within
milliseconds and thus sharper peaks are formed in the HS-QTC
sensor outputs. This ensures that the HS-QTC sensors can
adequately and accurately measure traffic flow parameters at
congested traffic. The difference might be attributed to
resistance relaxation existing in viscoelastic conductive rubber.
Generally, the elastic modulus of matrix increase with the
increase of conductive fillers content, following by faster
response and shorter relaxation time (faster recovery) [26, 27].
As a pressure-sensitive sensor, the more inconspicuous the
stress relaxation is, the better the sensor performance.
Therefore, it can conclude that the HS-QTC sensors with high
content of spiky spherical nickel powders in matrix are more
suitable for traffic detection. The following tests were all based
on the HS-QTC sensors and the QTC sensors below all refer to
the HS-QTC sensors unless otherwise stated.
B. The response of the QTC sensors to different vehicles
Fig. 7 shows a comparison of sensor responses to two
vehicles that having different weights (the vehicle parameters
are shown in Table Ⅲ , the KIA Sportage is about 150kg
heavier than Besturn B70. As indicated above, a higher load
will lead to smaller measured voltage). The vehicles pass over
the sensors at a same speed of about 20km/h. The data acquired
Fig. 6 Detection results of Besturn B70 passing over different sensitivity of
the QTC sensors. The zoom-out Fig. shows the detailed signal changes when the front wheel and the rear wheel of the vehicle passing. (a) Low-sensitivity
(LS-QTC) sensor. (b) High-sensitivity (HS-QTC) sensor.
TABLE Ⅳ
PEAK POINT VOLTAGE
Sensor type Peak point voltage (mv)
Average Fractional
change Front-wheel Rear-wheel
HS-QTC sensor 508 800 654 56.4% LS-QTC sensor 913 1110 1011.5 32.6%
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
5
from four repeated tests was processed by sorting peak point
voltage of the front-wheel and the rear-wheel of the two
vehicles in ascending order, respectively. The reason of
difference between the points representing same wheel of the
specific vehicle is that every time of the test car passing is not
absolutely consistent but stochastic, which is caused by some
uncertain factors such as uneven pavement, inconsistent
contacting area between the passing vehicle tire and the sensors.
As shown in Fig. 7, average voltage that refers to the average
value of peak point voltage of each vehicle’s front-wheel and
rear-wheel is adopted to characterize response. The accuracy
(>80%) of the QTC sensors is acceptable because changes in
average voltage of both cars is within the range of 300mv.
Compared with the average voltage of the QTC sensors for
Besturn B70 and KIA Sportage, all the points in the plot of the
former are larger than those of the latter. Besides, from the
comparison between the voltage value as front-wheel and
real-wheel of the two vehicles pass over the sensors, it can be
found that the points of front-wheel are generally lower than
that of rear-wheel. This suggests that the body weight of the
two vehicles is concentrated on the head part. The results
coupled with Fig. 1 indicate that there exists a functional
relationship between the change amplitude in voltage and the
vehicle weight, which verifies the feasibility to use the QTC
sensors as pressure sensors for weigh-in-motion detection. The
monitoring of vehicle weights can be more convenient and
effective if the weighing is performed on the highway while the
vehicle is moving normally.
C. The response of integrated-sensors and distributed-sensors
to vehicle passing
As described above in Section Ⅱ-B, the difference between
integrated-sensors and distributed-sensors is that the latter is
composed of three pieces of 250mm QTCs straightly lined up
with spacing of 25mm. In order to make data collection without
mutual interference, the length of each piece is substantially
larger than the width of the two vehicle tires. We arrayed the
two types of parallel sensors in two rows within an interval of 2
m, and drove the car past them at a constant speed. Fig. 8 shows
the response of the integrated sensors and the distributed
sensors. Two sensors behaved differently. As expected, only
one part of the distributed-sensors will show sharp peak of
voltage variation (sensor-2 in this set of results). It indicates
that the QTC sensors can be applied to reflect the lane
occupancy rate by responding to vehicle passage and presence
like when they are located at stoplines on multilane roadway. It
should be noted that, since all three parts of the distributed
sensor were installed on a whole steel plate, there are some
small disturbing signals caught by sensor-1 and sensor-3 as
shown in Fig. 8(b) and (d) (red arrows).
D. The response of the QTC sensors to different vehicle speeds
Inductive loop detectors with a double-loop arrangement are
generally adopted for real-time traffic speed estimation [1].
Similarly, for the QTC sensors, if a double row of the QTC
sensors is used in parallel array, vehicle speed V can be
calculated as
Where L is the distance between two rows of sensors as shown
in Fig. 3, T=T2-T1 is defined as the time interval of front-
wheel contacting the first row of sensors (T1) and the second
row of sensors (T2), T’=T2’-T1’ is the time interval of the
V = 2L (∆T+ ∆T')⁄ (2)
Fig. 7. Detection results of different vehicles. According to the data of four
tests, peak point voltage and the average value (solid) of the front-wheel and the rear-wheel of Besturn B70 (dash) and KIA Sportage (dash dot) were listed
in ascending order for comparison.
Fig. 8. Detection results of integrated-sensors and distributed-sensors (KIA
Sportage, 20km/h). (a) Integrated sensor. (b) Distributed sensor-1. (c)
Distributed sensor-2. (d) Distributed sensor-3.
Fig. 9. Detection results of double-row QTC sensors (KIA Sportage, 20km/h).
TABLE Ⅴ
VEHICLE SPEED DETECTION WITH TWO ROWS OF THE QTC SENSORS (KIA
SPORTAGE)
Vr
(Km/h)
T1
(s) T1’ (s)
T2 (s)
T2’ (s)
T
(s)
T’
(s)
Vc (km/h)
20 8.315 8.833 9.101 9.599 0.786 0.766 18.6
20 7.176 7.680 7.951 8.465 0.775 0.785 18.5 20 7.745 8.240 8.496 8.979 0.751 0.739 19.3
40 5.760 6.032 6.177 6.451 0.417 0.419 34.5
40 5.066 5.337 5.479 5.741 0.413 0.404 35.6 40 4.455 4.724 4.865 5.129 0.41 0.405 35.3
Vr is Reference velocity, Vc is Calculated velocity.
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
6
real-wheel. Note that the peak point is assumed to be each
vehicle passing over the sensors as shown in Fig. 9, i.e. T(T’)
is the time interval between two peaks.
Table Ⅴ provides the results of KIA Sportage passing two
rows of the QTC sensors at 20km/h and 40km/h, which are
calculated according to (2). Herein, note that the distance
between two rows of the QTC sensors is 4m and the reference
velocity Vr is based on the speedometers, which is
comparatively close to the calculated value. It can be seen that a
double-row QTC sensors can measure vehicle speeds easily.
Fig. 10 shows the detection results based on Besturn B70
passing over a QTC sensor at reference speeds of about 20km/h,
40km/h and 60km/h, respectively. The distance between two
voltage peaks is degraded proportionally with speed increasing,
likewise the half-peak width. If only one QTC sensor per lane
is used, according to time interval between the front and rear
wheel, vehicle speed V can be expressed as
Where Wb is the wheelbase of a vehicle, i.e. the distance
between the center of front-wheel and rear-wheel, which is
given in Table Ⅲ. T refers to time interval, which is defined as
T = TF - TR. TF and TR represent the time of front-wheel and
rear-wheel contacting the QTC sensors, respectively as shown
in Fig. 10. The criteria used to identify the contacts of a tire to
the sensors are similar to two rows of the sensors as shown in
Fig. 9. In addition, according to the interaction time between
wheels and sensors, vehicle speed V can also be denoted as
Where Ws is the width of QTC sensors, which is 5mm in this
study, and t(t’) is the contact time of front-wheel
(rear-wheel) with the QTC sensors (i.e. half-peak width) as
shown in Fig. 9(zoom-out). Note that the criteria used to
identify the first and last contacts of a tire to the sensors are the
point at which voltage value begins to decrease with a range
over 50 mV and the point at which voltage value stops
increasing with a range over 50 mV, respectively. A is the
amplification coefficient of the QTC sensors due to the total
width of the part of tire contacting sensors(i.e. the ground
contact length of the tire) is much larger than the sensor width,
which is a function of sensor width.
According to (3) and (4), A can be calculated by:
Herein, in order to investigate speed measurement accuracy
of the double-rows QTC sensors, we check the results by
comparing with that of the single-row QTC sensor. According
to (2), (3) and (4), Table Ⅵ provides the comparison of vehicle
speed detection which is based on the results of KIA Sportage
passing over the QTC sensors at 20km/h. Based on the result
analysis, the following conclusions can be drawn:
1) Compared with the results of (2) and (3), the double-rows
QTC sensor can directly and accurately measure vehicle speeds
within a high measurement accuracy, which is almost the same
as that of the single-row QTC sensor.
2) Due to the information of wheelbase is not available for
all passing vehicles, it is hard to measure vehicle speeds by a
single-row QTC sensor according to (3). However, consider
conversely, wheelbase can be calculated for vehicle
classification if the information of vehicle speed is available.
3) The amplification coefficient A almost remains constant
in the three tests, which means that it is possible for a
single-row QTC sensor with a specific sensor width to measure
vehicle speed directly.
A = 2T Ws Wb (∆t + ∆t') ⁄ (5)
V = 2AWs (∆t + ∆t') ⁄ (4)
V = Wb T ⁄ (3)
2 4 6 8 100
500
1000
1500
500
1000
1500
500
1000
1500
Vo
lta
ge
(mv
)
Time (s)
T2
Vo
lta
ge
(mv
)
T3
60km/h
40km/h
20km/h
Vo
lta
ge
(mv
)
T1
Fig. 10. Detection results of Besturn B70 passing over a QTC sensor at different vehicle speeds. The zoom-out Fig.s show the detailed signal changes
when the front-wheel and the rear-wheel of the vehicle passed.
TABLE Ⅵ
COMPARISON OF VEHICLE SPEED DETECTION (Vr = 20KM/H)
No.
Equation (2)
T1
(s)
T1’
(s)
T2
(s)
T2’
(s) T
(s)
T’
(s)
Vc
(km/h)
1 8.315 8.833 9.101 9.599 0.786 0.766 18.6 2 7.176 7.680 7.951 8.465 0.775 0.785 18.5
3 7.745 8.240 8.496 8.979 0.751 0.739 19.3
No.
Equation (3)
First row Second row Va
(km/h) T(s) Vc(km/h) T(s) Vc(km/h)
1 0.518 18.3 0.498 19.0 18.7
2 0.504 18.8 0.514 18.4 18.6 3 0.495 19.1 0.483 19.6 19.4
No.
Equation (4)
First row Second row Aa
t(s) t’(s) A t(s) t’(s) A
1 0.028 0.025 27.0 0.027 0.027 28.5 27.8
2 0.029 0.024 27.7 0.032 0.025 29.2 28.5
3 0.027 0.024 27.1 0.028 0.028 30.5 28.8
Vc is Calculated velocity, Va is Average velocity, Aa is Average
amplification coefficient.
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
7
E. Real-time traffic detection
As discussed above, the QTC sensors can be used for traffic
detection such as vehicle presence, vehicle weigh-in-motion,
position, vehicle inner structure distribution, occupancy rate
and speed. In order to further investigate the feasibility of the
QTC sensors for practical application, a road test of real-time
traffic was made on a dual-lane road in the light of the results
above-mentioned.
Fig. 11 shows the detection result of one QTC sensor per lane
on the real road for about 6 minutes. As a sequence of vehicles
pass over the sensors, the curve produces a corresponding
sequence of peaks. Vehicle count (25) and unit time traffic load
(about 5 per minute) can be simply achieved from the output
curve. The correct rate of vehicle count is 100%, which is
validated by the video record on the scene. Even in
discriminating between vehicles moving bumper-to-bumper in
congested traffic, the QTC sensor shows excellent performance
in traffic count.
Fig. 12 shows the output curves of various vehicles passing
over the QTC sensors. It can be seen that the outputs of the
QTC sensors are substantially different from each other
according to the types of the passing vehicles. For example, the
dump truck with three rows of wheels is easily distinguished
from the other vehicles by the longer three peaks. Moreover, it
is possible to differ between even different classes of SUVs as
shown in Fig. 12 (b) and (f). Therefore, based on the
comparison of output curves generated by QTC sensors, the
realization of vehicle classification process in real-time traffic
can be significantly simplified. Furthermore, by knowing the
speed of the vehicle, the wheelbase can be calculated, which
gives another idea for vehicle classification. It is necessary to
note that durability of the QTC sensor is good. In our study, it
was never out of operation even after sever tons of loading in
thousands of cycles.
IV. CONCLUSION
This paper proposed a new type of traffic detection sensors
based on QTCs and investigated the feasibility and
performance of the QTC sensors for traffic detection. The QTC
sensors can accurately detect the passing of different vehicles
under different vehicular speeds and test environments. The
HS-QTC sensors with high concentration level of spiky
spherical nickel powders in matrix are more suitable for traffic
detection due to higher sensitivity, faster response and recovery.
The double-rows QTC sensors can measure vehicle speeds
easily and precisely. It is possible to measure vehicle speeds
with one QTC sensor per lane. The QTC sensors have the
advantages of high detection precision, fast response and
recovery, high cost-efficiency, excellent robustness, easy
installation and maintenance, good durability and energy
conservation. The QTC sensors can provide a lot of traffic data
including vehicle presence, vehicle weigh-in-motion, position,
vehicle occupancy rate, speed, vehicle count and vehicle
classification in real-time traffic. The QTC sensors installed on
road surface or embedded in road joint may have other potential
functions. Traffic violations such as illegal parking, illegal
U-turns, run a red light and jaywalk and so on, can be detected
as illustrated in Fig. 13. The results demonstrated the feasibility
and excellent performance of traffic detection by using the
QTC sensors that provide data support for traffic control and
management. For further investigation, our future work on the
QTC sensors will include the effect of environment conditions
(such as temperature and humidity) on the performance of the
QTC sensors, and developing the function of the QTC sensors
to measure vehicle weigh-in-motion.
Fig. 11. Detection results of real-time traffic detection. Each red star indicates
a vehicle-passing signal.
12.3 12.4 12.5 12.6 12.7 12.80
300
600
900
1200
1500
Vo
lta
ge (
mv
)
Time (s)(a)
86.1 86.2 86.3 86.4 86.5 86.60
300
600
900
1200
1500
Vo
lta
ge (
mv
)
Time (s)(b)
128.8 128.9 129.0 129.1 129.2 129.30
300
600
900
1200
1500
Vo
lta
ge (
mv
)
Time (s)(c)
150.0 150.5 151.0 151.50
300
600
900
1200
1500
Vo
lta
ge (
mv
)
Time (s)(d)
251.4 251.5 251.6 251.7 251.8 251.90
300
600
900
1200
1500
Vo
lta
ge
(mv
)
Time (s)(e)165.0 165.5 166.0 166.5 167.00
300
600
900
1200
1500
Vo
lta
ge
(mv
)
Time (s)(f)
Fig. 12. Output curves of various vehicles passing over the QTC sensor. These curves of the seven vehicles correspond to the 2th, 5th, 9th, 10th, 19th, 11th
and 12th peaks in Fig. 10, respectively. (a) Midsize car. (b) Heavy-type SUV.
(c) Minibus. (d) Dump truck with three rows of wheels. (e) Six-passenger
MPV. (f) Two vehicles moving bumper-to-bumper, a nine-passenger bus and
a midsize SUV.
Fig. 13. Schematic illustration of QTC sensors for traffic detection.
1530-437X (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Seehttp://www.ieee.org/publications_standards/publications/rights/index.html for more information.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI10.1109/JSEN.2015.2429674, IEEE Sensors Journal
> Sensors-11917-2015 <
8
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Baoguo Han received his Ph.D. in the field of smart materials
and structures from the Harbin Institute of Technology, China,
in 2005. He is a professor in Dalian University of Technology,
China. His main research interests include smart materials and
structures, sensors, structural health monitoring, traffic
detection, and nanotechnology. He is the author of 1 book, 7
book chapters and more than 50 published papers.
Siqi Ding received the B.S. degree in material science and
technology from Dalian Jiaotong University, Dalian, China in
2013. He is currently pursuing the M.S. degree at the School of
Civil Engineering, Dalian University of Technology, Dalian,
China. His current research interests include smart materials
and structures, sensors, and traffic detection.
Yan Yu (M’12) received the Ph.D. degree in disaster
prevention and reduction engineering from the Harbin Institute
of Technology, Harbin, China, in 2006. He is currently an
Associate Professor at Dalian University of Technology,
Dalian, China. He is the author of over 50 published papers. His
research interests include wireless sensor networks, data fusion,
structural health monitoring, and intelligent building.
Xun Yu received his Ph.D. in mechanical engineering from the
University of Minnesota-Twin Cities in 2006. He then joined
the Department of Mechanical and Industrial Engineering at the
University of Minnesota-Duluth, where he worked as an
assistant professor from 2006-2010 and associate professor
from 2010-2011. Since 2011, he is an Associate Professor at the
Department of Mechanical and Energy Engineering at the
University of North Texas. His research areas include
nanotechnology-based smart materials and smart structures,
sensors, actuators and controls.
Sufen Dong received the M. S. in the field of materials science
and engineering from the Chongqing University, China, in
2009. She is currently pursuing the Ph.D. degree in Dalian
University of Technology. She is a lecture in the Inner
Mongolia University of Science and Technology. Her main
research interests include smart materials and structures, and
cement and concrete materials. She is the author of 2 book and
more than 10 published papers.
Jinping Ou received the Ph.D. degree in Harbin Institute of
Technology, P.R. China, in 1987. He is a professor in Harbin
Institute of Technology and Dalian University of Technology.
His main research interests include structural damage,
reliability and health monitoring, structural vibration and
control, smart material and structures. He is the author of 4
books and more than 200 published papers in his research areas.