Design and Implementation of a Multiple Traffic Parameter Detection Sensor Developed With Quantum...

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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

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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.

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



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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).



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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.



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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%



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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.



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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.



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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.



> Sensors-11917-2015 <

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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.

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