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DEVELOPMENT AND TECHNOLOGY TRANSFER OF A WEIGH IN MOTION
SYSTEM IN ARGENTINA
Conference Paper · December 2016
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DEVELOPMENT AND TECHNOLOGY TRANSFER OF A WEIGH IN MOTION
SYSTEM IN ARGENTINA







J. JORGE I. MORETTI J. AMADO D. PUNTILLO C. CANIGLIA
National Institute of Industrial Technology,
Argentina


Abstract
With a transportation system centered on the roads, and a very deficient railway
infrastructure, it is increasingly important to do proper planning and design of Highways of
our country and more specifically of our province. In this context, the Department of Roads of
the Province of Córdoba and the National Institute of Industrial Technology (INTI) initiated a
joint project for the development and construction of an inexpensive, adaptable to the
conditions of the region, and with local support prototype of a high-speed weigh in motion
system that it could be produced in our country. This paper presents the process of design,
implementation, calibration, testing and technology transfer of a WIM system for statistical
purpose, developed entirely in an institution of the national state and transferred to the
productive sector. The current status of WIM in Argentina and future prospects are also
mentioned.

Keywords: Weigh In Motion, high-speed WIM, WIM development kit.


Resumen
El sistema de transporte argentino está centrado en las carreteras por lo que cada vez es más
fuerte la necesidad de realizar una correcta planificación y diseño de las rutas de nuestro país,
y puntualmente de nuestra provincia. En este contexto, la Dirección de Vialidad de la
Provincia de Córdoba y el Instituto Nacional de Tecnología Industrial (INTI) inician un
proyecto conjunto para el desarrollo y construcción de un sistema de pesaje dinámico de bajo
costo, adaptable a las condiciones de la región, con soporte local, y que pueda ser producido
en nuestro país. En esta publicación se describe el proceso de diseño, desarrollo,
implementación, verificación, calibración, pruebas, y transferencia tecnológica de este
sistema, completamente desarrollado en una institución del estado y transferido al sector
productivo. Se muestra el estado actual del área en Argentina y se mencionan las perspectivas
futuras.

Palabras Claves: Pesaje en movimiento, WIM de alta velocidad, prototipo WIM.

1. Introduction
Argentinian transportation system is centered on the roads; we have a very deficient railway
infrastructure. Road transport in Argentina has a decisive weight in the total cargo movement
(which includes exports, imports and domestic cargo). Currently more than 90% of the total
load is delivered by road transport (Barbero, J. 2013). In contrast, the railway carries only a
little more than 5% of the loads and water transport 1.5%, Figure 1 depicts this distribution
and exports distribution.

Figure 1 – Transport type participation for total cargo and export only cargo.

Argentinian export cargo volume is expected to increase by about 25% in the next decade. It
is increasingly important to do proper planning and design of Highways of our country and
more specifically of our province. To accomplish this objective it is essential to have
characterization of current traffic volumes on the road network. In addition, it is also
necessary to avoid the circulation of overloaded vehicles. Current controls are not being
effective because Argentina only has static weighing stations and a very small number of
operators. There are some groups of operators with portable static scales but permanent
stations are essential. It is very important to have a more effective load control and new road
enforcement systems to avoid premature deterioration of the highways.

This scenario serves as an indispensable starting point in the implementation and design of
traffic monitoring and control plan that includes WIM systems as data providers. In this
context, in 2009 the Department of Roads of the Province of Córdoba and the National
Institute of Industrial Technology initiated a joint project for the development and
construction of an inexpensive, adaptable to the conditions the region, and with local support
prototype of a high-speed weigh in motion system that it could be produced in our country.

The paper presents the process of design, development, implementation, calibration,
validation, and technology transfer of a WIM system for statistical purpose, developed
entirely in an institution of the national state and transferred to the productive sector. The
most important aspects of the development process, verification and calibration of the system,
methodologies, current applications, its scope and limitations are also summarized.

Given the incipient progress in this area in our country, it has also begun to outline a draft of
metrological and technical requirements as well as the methods of certification for high-speed
weighing equipment that allows direct enforcement, which currently cannot be performed due
to the absence of corresponding legislation. The current status of WIM in Argentina and
future prospects are also mentioned.
2. Development Process
A prototype driven development (PDD) approach was adopted. Each prototype was used to
test different components of the system. The reason why PDD was adopted by this project
was that specific knowledge of the whole system and specific requirements were not available
at the beginning of the project. By continuously creating, improving, and interacting with
prototypes devices can be launched faster, with higher quality and with a stronger focus on
the customer’s needs.

In this context, a prototype is a good tool to learn form a technology. Each prototype was
developed using a reduced version of the V model (Turner, R. 2013). The main advantage of
V model is that test plan and test design is conducted as soon as the design process is done.
Then once an artifact has been implemented, the testing stage can take place.

Initial planning for the first prototype took lot of time. INTI development group members had
already had an experience with WIM sensors back in 1998, but by the time the development
started the technology had evolved a lot. After the initial planning a sketch of the initial
requirements and an architecture design was made. With that in mind each group started a
detailed design and implementation of the components. Meanwhile in parallel the team started
the building of the WIM site and performed the sensors installation. Once the sensors where
in place, the team proceeded with the capture of real signals to aid the development process in
the lab.

The first prototype “P0” was just a charge amplifier, and a small system capable of integrate
the signal and measure the time between axles. With this device integration capabilities and
weight calculation algorithms were designed. By that time the weight calculation was done
after the each field measurements back in the lab. The next prototype “P1” added a control
module that gives it full weigh-in-motion capabilities. This new device was able to measure
speed, weight and classify the vehicles. This new capabilities where tested and the need of
axle inconsistency detection arises. The last prototype included a printed circuit board (PCB)
redesign (noise reduction), system calibration and performance evaluations, and major
software upgrades (Figure 2).
3. The final Prototype
The system is designed to work in a distributed fashion. This means several data acquisition
equipment are spread in different roads and transmit collected data to a predefined statistical
data center (Figure 2). This allows remote monitoring of traffic flow of different places. Data
acquisition devices can also operate autonomously, as a measurement station, and data can be
extracted locally.

Figure 2 – Photograph of the final PCBs

3.1 Block Diagram
This block diagram (Figure 3) depicts the main sections of the final prototype and its
interactions. Vehicles interact with sensors, Signal conditioning block transmits voltage signal
to the acquisition block. This last block preprocesses the signal and re transmits the most
important information to the processing unit. Once the weight, speed and type of vehicle are
calculated, all this information is packed into a register that is sent to a concentrator.


Figure 3 – Simplified block diagram

3.1.1 Sensors
This prototype has five sensors, two inductive loops, two 6' (1.82m) Class I (WIM) RoadTrax
BL Piezo Sensors and one temperature sensor.
Data Adq.
Signal Conditioning
Processing

3.1.2 Signal Conditioning
The idea of this block is to transform charge signal into voltage, extract some indicators and
reduce the dynamic range of the signals at the input of the ADC.
3.1.3 Data Acquisition
This block contains a 100 MIPS 8 bit microcontroller that converts the analog data into digital
and preprocesses the signal. It acquires raw data and transmits this data to the processing
block.
3.1.4 Data Processing
This block is based on an x86 32bit system, capable of receiving real time raw data and post
process this data in order to produce relevant information such as weight, speed, classification
etc. This block also performs surveillance over the performance of the whole system.
3.1.5 Data Transmission
Data transmission is performed over a network of computers and it is independent of the
hardware, it can be throw an Ethernet connection, a GPRS modem or a WIFI connection
depending on the available infrastructure.
3.1.6 Statistical Data Center
This is a software application that concentrates information from different devices and
processes it in order to give statistical information of each device. The software was
programmed to meet the needs of local road authorities
3.2 Piezo electric sensor characterization
At the beginning of development only nominal transfer function of the sensor (pC / Nt) and
the maximum error along it where known. In order to acquire a detailed knowledge of the
sensor's response, and especially its variation with respect to temperature, extensive
measurements were performed on the sensors installed on the road in the experimental site.
Measurements were performed at different speeds, different weights and different
temperatures; this helped us to characterize the variation of the sensor gain against these
parameters. Then algorithms that compensate for these variations were developed. Obtained
results are similar to that of Kwon (2016).
3.3 Test site description
Field measurements where done in an experimental test site. This site was chosen in order to
help the development and the serve as a permanent WIM site and real time traffic monitoring
station. The site was constructed with the aid of the national direction of the Department of
Roads of the Province of Córdoba. The chosen place corresponds to road C45, at 40km from
Cordoba city (Figure 4). This road is often used for legume and cereal transport from the
farms to the nearest silo. This road was also chosen because it has some conditions similar to
most Argentinean roads; it’s made of asphalt and has some deformations.
The site corresponds with criteria for the choice of WIM sites according to the literature and
the European WIM specification (1999), Chapter 5, and was evaluated as Acceptable.
All the electronic was located in a bunker at approximately 15 m from the road, to achieve a
distance that is safe enough to avoid accidents and give more comfortable measurement
conditions and not so far to use shorter cables.

Figure 4 – sensors and test truck

4. Prototype Validation
The system validation included a complex set of tests, unit tests integration tests and system
tests. This section will summarize some of the most important tests and its results
4.1 Weigh validation
The weighing process includes the measurement of vehicle speed and the sensor signal
integral calculation. Each block was first individually validated, and then integrated. The
system speed measurements were first validated in laboratory. Using a digitalized signal
obtained from a real vehicle. The signals were replicated using an arbitrary signal generator.
The speed measurement error was within the 0.2% for all measurements. Some integration
tests where done in software. Some integration tests and functional tests were done in order to
check that the results obtained in the unit tests against simulated values reach the data base
and the frontend. Then the system was validated in field against INTI national speed reference
device. All measurements errors where less than 1.5% Integral calculations were also
validated separately using a laboratory signal generation pattern. This pattern is also validated
against international signal generator patterns. Integral measurements revealed that errors was
allays less than 0.8%. The temperature sensor was also calibrated against INTI temperature
patterns.
4.1.1 Calibration
The calibration method implemented was the “Pre-Weighed Calibration Lorries” according
with COST 323. The main task was to estimates the variation of the thermal sensibility of the
two piezo electric sensors. The estimation function was a lineal approximation according to
passing some test pre-weighed vehicles over the WIM system. This test plan calibration
consisted in 30 events of different vehicles, weights and speed levels. The class of vehicle
was type 2, the weights were between 8t and 16t and the speed levels were between 30km/h to
90km/h. This process consisted of three consecutive days of measurements over six months.

According to Principles of Weigh-in-Motion using Piezoelectric Axle Sensors (Brown, R. H.
2001), the weight of the vehicle is calculated by integration of the axle-crossing waveform.
The integral I must then be scaled (multiplied) by the vehicle speed V. This quantity (time
integral of charge or voltage, multiplied by speed) is then proportional to the total load
applied during the axle crossing. Then, the weight P is calculated as Equation 1:

P = I . V. k (1)

where k is the sensibility of the sensor and depends on the temperature. The calibration
process goal is to estimate this function. This approximation result for the first sensor is given
in Figure 5.

Figure 5 – Thermal Sensibility estimation of sensor 1

4.1.2 Accuracy verification
According to the European WIM specification (COST 323, 1998) the site was evaluated as
“III Acceptable”. The site was chosen because it represents the most common route condition
in Argentina. For the calibration of the prototype we used Pre-Weighed Calibration Lorries.
According to the test plan the environmental repeatability and reproducibility was (II) limited
environmental reproducibility and (R2) extended repeatability conditions. The test for the
accuracy verification was at different days, vehicles and weights. In total there were 196
independent events. The results were measured with 5 (five) accurately weighed calibration
vehicles, possessing two axles with a gross and axles weight distributed according to Table 1.
The vehicles speed varied between 29 and 86 km/h during the measurements. The vehicles
were ford cargo 1722, the first (front) axle has rigid axle suspension and second (rear) axle
has full floating suspension.
Due to budget restrictions the test site only had two half lane sensors installed consecutively.
The system calculates the right wheel weight; the calculated weight is the average of the
estimation of both sensors. Dynamic wheel weight calculation error was within 25% for the
95% of the measures, so the prototype cloud is classified as Type I in accordance with ASTM
1318 2009.
The axle weight information was estimated by multiplying the weight of the wheel by two in
order to obtain a COST 323 classification. This is merely illustrative because sensor
configuration does not allow obtaining a full axle dynamic weigh.

Table 1 – Static loads/weights Ws (kg) calculated in concordance to COST323 procedures

Vehicle GW A1 A2
A 8000 4041 3959
B 12240 5039 7201
C 13090 4391 8699
D 15640 4858 10782
E 16240 5059 11181

Table 2 describes some event and the relative error of the measurements.

Table 2 – Some events and its relative error

Nº T(ºC) V(km-h) Type
WD(kg) WS(kg) Relative errors(%)
GW A1 A2 GW A1 A2 GW A1 A2
1 14,3 40 2 8067 4095 3972 8000 4041 3959 0,84 1,34 0,33
35 8,7 36 2 12283 5086 7196 12240 5039 7201 0,35 0,93 -0,07
77 10,4 41 2 13144 4339 8805 13090 4391 8699 0,41 -1,18 1,22
113 10,9 49 2 15708 4924 10784 15640 4858 10782 0,43 1,36 0,02
162 16,5 58 2 16444 4946 11498 16240 5059 11181 1,26 -2,23 2,84

After that we calculated the mean and standard deviation of relative error of GW and SA the
results are described in Table 3.

Table 3 - Relative errors (%) statistics

GW SAL
number 196 392
mean 0,64 1,35
st.dev 10,88 13,67

Table 4 summarizes experiments with the vehicles mentioned in Table 1 in the chosen site.
The COST 323 European WIM specification accuracy calculated result is E(30).

Table 4 – Accuracy calculated results

Conditions (1) Test plan Envt

Initial verification (Yes=1, No=0): 0
R2 II
SYSTEM Number Identified Mean Std deviat o Class  min c 
Entity (%) (%) (%) (%) (%) (%) (%) (%)
gross weight 196 100,0 0,64 10,88 96,5 D(25) 25 24,7 24,7 96,7
single axle 392 100,0 1,35 13,67 96,8 E(30) 36 31,0 26,0 98,8



4.2 Classification algorithm and validation
The classification is performed according to the requirements of the National Roads Authority
as published in Dirección Nacional de Vialidad (2003), which is based on the separation of
the vehicles axles. The classification algorithm was performed by a key-value structure,

where vehicles are grouped by type. Each element of the structure contains a list of all
vehicles with the same number of axles, whenever it presents a new vehicle it is only
necessary compare it against all vehicles having the same number of axes. Besides, it was
implemented an automatic sort system that permits to locate the last vehicles identified at the
beginning of the list. This classification algorithm was tested in the WIM site and the results
are displayed in Table 5.

Table 5 – Classification results

Vehicle
type
Outline Categ.
No.
No. of
axles
No. of
vehicles
No. of classified
vehicles
Percentage
(%)
Car

2 2 30 28 93,33
Pickup
truck
3 2 9 9 100
Truck 11

6 2 34 34 100
Pickup +
trailer
3 3 4 2 50
Semi truck
111
10 3 1 1 100
Semi truck
112
11 4 7 7 100
Truck 11
12
9 5 15 14 93,33
Semi 11
(1) 2
12 5 3 3 100
Semi 113

12 5 2 2 100

5. Technology transfer
The developed technology is being transferred to national enterprises for production stages.
The main idea is that these enterprises take the development and could make improvements
and evolutions of the system to get new commercial products. The transfer process includes
training about system development, operation, calibration, use and regulations. And it also
provides technical assistance for installation of the measuring station and start up the system.
6. WIM in Argentina
The use of WIM system in Argentina is very poor, there is only a few sites in the whole
country, but the need of monitoring the traffic weight is more and more important. In general,
the organisms that manage the roads don't have automatic systems to monitor their state. Even
the current legislation does not allow overload punishment with dynamic weighing systems.
So, the control of load traffic is made using expensive and inefficient static weighing systems.
The main important objectives of this project were develop a more inexpensive system that
could be produced by national industry, and to produce knowledge in the area of weigh-in-
motion of vehicles.

7. Conclusion
A prototype of high-speed weigh-in-motion system has been developed, according to the
conditions of the road network in this country. The system is adaptable, flexible, versatile and
inexpensive. It has been proven correct operation of the equipment and characterized his error
under various conditions of use. Several experiments have been performed, both by
simulation and field measurement, verifying the accuracy of the prototype and its high degree
of reliability. The next step is to bring the system to a commercial product, working with
several companies. The main idea is that Argentinian enterprises be able to evolve this
technology and produce commercial devices. In this work we develop the most important
parts of a WIM system: characterization of pavement-resin-sensor response, temperature
compensation system, analog signal conditioning, digitalization, data processing, storage and
data transfer, etc. Besides, very special and refined algorithms had been designed for vehicle
classification and calculation of weight, even correcting some common phenomena such as
removing spurious axles. Now, all this technology can be produced in Argentina, which is an
important innovation and a great advance for the national industry. It has been presented
errors study and statistical processing of the results of field measurements, which show
adequate accuracy and precision for the development model. It is expected that in production
stage the performance of the system can be improved considerably. For example, due to
various problems in our project (mainly related to budget restrictions), we had to develop the
prototype using two half lane sensors, with the measurement problem that this represents.
Now, we are working together with enterprises, so we can implement new measurement sites
detecting the whole lane and to achieve a significantly reduction of the errors. Besides, this
project also reached the important goal of research and produce knowledge in the area of
WIM systems of vehicles, which will allow us to initiate more complex projects, such as the
drafting of a technical regulation for dynamic weighing. Given importance which traffic
monitoring is taking today in Argentina, we have begun to write a technical regulation draft,
in order to normalize the use of these equipment in our country.
8. References
 Barbero, J. and Castro, L. (2013), “Infraestructura logística. Hacia una matriz de cargas
para la competitividad y el desarrollo sustentable”. CIPPEC.
 Jacob, B., O'Brien, E., Jehaes, S. (2002), "European WIM Specification", COST
323"Weigh-in-Motion of Road Vehicles" Final Report (1993-1998)
 Brown ,R. H. (2001), “Principles of Weigh-in-Motion using Piezoelectric Axle Sensors”
 ASTM international (2009), “E1318-09, Standard Specification for Highway Weigh-in-
Motion (WIM) Systems with User Requirements and Test Methods”.
 Turner, R. (2013),"Toward Agile Systems Engineering Processes".
 Kwon, T. M. (2016), "Implementation and Evaluation of a Low-Cost Weigh-in-Motion
System", Minnesota Department of Transportation.
 Dirección Nacional de Vialidad (2003), “TABLA DE CLASIFICACION DNV_ARG3”
http://www.vialidad.gov.ar/division_transito/Tabla_clasificacion.htm
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