The results reported in the following paragraphs refer to measurements collected during a 1 year test including different traffic, lighting, time interval, climate and weather conditions, as shown in Table 1.
Moreover, an “average profile” summarizing the behavior resulting from the average of the measurements collected during sunny/clear, winter, daylight conditions, was defined.
The results related to the devices were synthesized as shown in Fig. 4, where, for every collection interval, the “average profile” is expressed in terms of the expected error (e
m
) and its uncertainty (U). Values fluctuations due to variations of temperature (winter/summer), luminous intensity (day/night), traffic condition (low/medium/high level) are shown in scale reporting their increment/decrement with respect to the “average profile”.
The behavior of the device in severe weather condition (rain) was also evaluated from a qualitative perspective adopting a scale with 3 severity levels (high/medium/low).
6.1 Laser scanner system results
The laser system tested in the survey was composed of two devices mounted above the road on the overpass bridge inside the test site, at the centre of the lanes. Each device included six laser diodes that scanned the detection area, providing a three-dimensional vehicle profile (length, height and width). Vehicle characteristic data were processed by a recognition algorithm to supply vehicular classification. Additional parameters provided by the system were speed, headway, gap, presence of stopped vehicles and of vehicles heading in the wrong direction.
The outcomes of the survey relative to counting accuracy are reported in Fig. 3. As it can be seen, in the collection interval of 20 s the average error is equal to 40.1%. Due to such a high value the system doesn’t seem to be significantly influenced by environmental and traffic conditions. For larger collection intervals, the average error decreases to 24.1% in the period set for road design (5 min) and to 19.8% in that fixed for statistical analysis (15 min).
As shown in Fig. 3, the system performance depends on lightning condition. In summertime, when the intensity of the light is high, the average error increases with respect to its average profile, while in wintertime, when solar radiation weakens, its performance tends to improve. It can also be seen a slight difference in the behavior of the device between day and night; in particular, the performance of the system improves at daytime and worsen at nighttime.
Furthermore, regular traffic condition composed of vehicles moving at low speed, enables the device to acquire more vehicle information, so improving its performance.
No variation of the device response was detected in rainy condition.
Different results were observed between the devices installed on the two lanes. The device mounted on the fast lane provided far less precise results with respect to those observed on the traffic lane, so worsening the global average profile of the system.
6.2 Video image system results
The video image system installed at the test site was composed of a colour camera, a processing unit and a supervisor unit. The camera, set to control both lanes, was mounted at the centre of the gantry. The system was designed to count and classify vehicles, display average speed, vehicular density and traffic conditions. It was also equipped with a sophisticated image processing algorithm, based on neural network for motion detection and object tracking, to detect abnormal traffic conditions.
Video image systems performance is usually affected by temperature and lightning conditions, and, as it can be seen in Fig. 4, the results achieved for the tested device seems to confirm this assumption. At day time the system shows better performance with respect to the average profile of 34,1%, while at night its performance decays drastically, due to the dim lighting of the area.
As many other electronic devices, its performance also reduces with high temperature, showing a better behaviour at winter time.
The system just allowed the collection of aggregated data within a minimum time interval of 15 min, so it was not possible to assess its performance within the time intervals of 20 s and 5 min.
No variation of the device performance was detected in rainy condition.
6.3 Double technology system results
The system was composed of two consolidated technologies, laser and radar, to guarantee the maximum reliability in detecting the most relevant parameters, such as vehicles count, classification and speed.
The laser scanner, installed at the centre of the gantry, was designed to control traffic flow, while radars, mounted at the scanner sides, were focused on measuring speed.
Speed measurements were based on Doppler effect, i.e. on frequency change between the signal emitted by radars and the signal reflected from vehicles passing through the propagation path. The frequency difference between the transmitted signal and the reflected signal is proportional to the instantaneous vehicle speed. Among non intrusive devices, the radar is the most accurate in measuring speed.
Vehicle classification and counting were accomplished by a laser scanner able to analyse the vertical profile of vehicles running on the carriageway. Vehicles classification was based on their height measurement. The longitudinal profile of the vehicle height allowed to reconstruct its shape and consequently to classify it.
This kind of system is known to be insensitive to environmental conditions, such as light, fog, rain and snow. As a matter of fact, the survey outcomes seem to confirm this trend. Little variations with respect to the average profile were detected for short time intervals. The best performance was recorded for long time intervals, where accuracy and precision assume their highest values (em = 2.6 ± 0.6%) (Figure 5).
6.4 Triple technology system results
The triple technology system installed at the test site was composed of two devices, mounted on a gantry above the street, at the centre of the lanes. Each device was made of three different kind of sensors:
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a radar sensor for vehicles speed measurements;
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an ultrasonic sensor, composed of an acoustic wave generator and a receiver, that measure the delay between the emitted and the reflected signal from objects moving across the detection area, revealing their passage and providing vehicles counting and classification;
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a passive infrared laser sensor for the measurement of occupancy, vehicles counting and classification in conjunction with the ultrasonic sensor. The sensor technology was made of an infrared sensitive material gathering the energy emitted from the road pavement or from the surface of the vehicles in the detection area. The occupancy of the road is revealed by their energy difference, that is directly proportional to the absolute temperature of the vehicle and the emissivity of its metallic surface.
The system was also able to measure vehicles headway and gap, and detect queue presence.
The performance of the system is shown in Fig. 6.
As it can be seen the average error of counting measurements decreases as the collection interval widens, reducing its value from 10.9% (real time application) to 3.5% (statistical analysis). Besides, despite the system is known to be sensitive to temperature and air turbulence, its performance was not affected by climatic, lighting, traffic and rainy conditions, except for small variations detected in the narrow interval of 20 s.
6.5 WIM Piezoelectric system results
The WIM piezoelectric system tested in the survey was composed of two piezoelectric sensors and one inductive loop per lane. The piezoelectric sensors measured vehicle weight, speed, length and axes gap, while the inductive loop provided the occupancy time.
Data accuracy of this kind of system depends on the configuration installed. It is possible to adopt cheaper configurations with a single piezoelectric sensor and two inductive loops with less interesting performance.
To optimise its performance and accuracy, the system installed at the test site was equipped with a neural network algorithm to compensate sensors response to temperature variations.
Despite this compensation, the system seems to be extremely sensitive to temperature. As it can be seen in Fig. 7, the average error tends to decrease in summer, when the temperature of the road surface is high, and to increase in winter, when the temperature lowers. The average profile shows a better system performance for longer time intervals.
Variations due to light intensity and traffic are negligible. Modest changes with respect to the average profile were found in rainy conditions for short time intervals.
6.6 WIM Quartz system results
The WIM quartz system tested in the survey was composed of 2 bars of quartz sensors coupled with 2 inductive loop detectors on each lane. The quartz crystals were mounted on a strip of aluminum, covered with elastic material. Quartz is a material characterized by accurate piezoelectric properties, capable to generate an electric potential difference when subjected to mechanical solicitations.
The parameters measured by the system were vehicle count, speed, classification, weight, length, headway, vehicle and axis gap.
The results relative to counting measurements are reported in Fig. 8. As it can be observed, in the time collection interval of 20 s the average error is about 30.7%; enlarging the collection interval to 5 min, the average error reduces to 17.6% and to 12.3% when the time range widens to 15 min. The system performance is also resulted to be strongly affected by climatic conditions: specifically, a worsening of the performance was detected during winter (low temperature) and an improvement during summer (high temperature). The lighting and rain influence on the system performance can be attributed to temperature variations as well.
Furthermore, in short collection intervals, regular traffic conditions, composed of vehicles moving at low speed, enables the device to acquire more vehicle information, so improving its performance, while high vehicular speed occurring with low traffic volumes tends to deteriorate it.
6.7 Inductive loops system results
The system tested in the survey was made of a couple of inductive loops of rectangular shape embedded in the road pavement. Vehicles counting and classification were operated sending to the loops a variable frequency signal from 10 kHz to 200 kHz, that resonates at frequencies depending on their inductance value. When a vehicle passes over the loops, a decrement of its inductance is generated. This decrement increases the oscillation frequency of the detection circuit, inducing an impulse indicating the presence of a stopped or moving vehicle in the electronic part of the device.
The system was able to count and classify vehicles, measuring speed, gap and occupancy.
The performance of the system is shown in Fig. 9. As it can be seen the average error of counting measurements decreases as the collection interval widens, reducing its value from 14.8% (real time applications) to 9.4% (statistical analysis). The device performance doesn’t seem to be significantly affected by lightning and traffic conditions. Differently, in summertime, with high temperature, the average error increases, while in wintertime, with low temperature, performance tends to improve. No variation of the device performance was detected in rainy condition.