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Vehicule identification from inductive loops
Application : travel time estimation for a mixed population of cars and
trucks
C´
edric Le Bastard, David Guilbert, Antoine Delepoulle, Abderrahmane Boubezoul, Sio-Song Ieng
and Yide Wang
Abstract— This paper addresses the use of existing
widespread Inductive Loops Detector (ILD) Network for re-
alizing an estimation of individual travel time for a mixed
population of cars and trucks. The aim is to provide traffic in-
formation to both users and traffic managers. The identification
of vehicles is realized by comparing the destination inductive
signature features with the origin inductive signature features
using an identification method. In this paper, we propose to
use three identification methods : a Bayesian based learning
approach, a fuzzy logic method and the SVM method. These
methods are evaluated on a real site. In order to increase the
level of identification, several propositions are carried out and
discussed.
I. INTRODUCTION
For some decades, people and goods flows have been
French government’s central preoccupation due to the prob-
lems related to the reduction of congestions, dangerous
driving or, more recently, pollution. Today, it is acquired
that environment-friendly solutions depend on good traffic
management on existing network. To effectively manage road
traffic, in real-time, data collection and information analysis
are essential. Thus, research in the traffic domain is interested
in the use of various powerful systems of detection: video,
Lidar [1], magnetometer [2],...
In spite of the realized advanced research on these systems,
the Inductive Loop Technology (ILT) still remains the sensor
the most largely installed in traffic network in many countries
such as France and the USA. This system is more discrete
and robust than cameras and it works in all weathers.
Moreover, it preserves the user’s privacy.
In the past, ILT allowed the estimation of volume, speed,
occupancy, presence and vehicle classification by the length.
In the nineties, new efficient electronic devices provide
signals, called ”electromagnetic signatures” sufficiently com-
plex to provide useful characteristics of vehicles. More
comprehensive information is thus available in real time.
This information is used to increase vehicle classification
robustness [3]. Recently, the vehicle signature has allowed,
firstly, to estimate the individual travel time [4], [5], [6]
and secondly to estimate the origin-destination matrices [7].
Some authors have focused on identification methods based
A part of this paper has been carry out during the internship of A.
Delepoulle. A. Delepoulle is student at INP of Grenoble.
C. Le Bastard and D. Guilbert are researchers in CETE de l’Ouest, France,
cedric.lebastard@developpement-durable.gouv.fr
A. Boubezoul, S.-S. Ieng are researchers in the IFSTTAR, France
Y. Wang is professor in the IREENA Laboratory, Polytech’Nantes, France
on neural networks [6], [9], conventional distance measures
and spatio-temporal distance measures from signature data
[6]. Unlike individual reidentification, [8] proposes to use
the sequences of vehicle lengths derived from loop detectors
for matching platoons or groups of vehicles. In this paper,
we focus on the recognition of electromagnetic signatures of
individual vehicle to provide real-time information by using
three other methods: the Bayesian based learning approach,
the fuzzy logic method and the SVM method. The two
first methods, i.e. the Bayesian based learning approach and
the fuzzy logic method, have already been used in [4], [5]
to estimate the travel time of cars only. In this paper, we
propose to generalize this study by taking into account the
entire population of vehicules. Moreover, we also suggest
using one of the most powerful supervised machine learning
algorithms, the SVM algorithm. Then, we will analyse the
behavior of the used identification methods for three types
of population: cars, trucks and mixed population (car-truck).
The principle of vehicles recognition by loops is based
on the comparison of signature features collected on two
successive loops of a section. These recognition methods are
called in this paper ”reidentification methods”, associated
with ”electromagnetic loops” allow us to identify, and re-
identify vehicles and thus to anonymously carry out vehicle
tracking. Thus, the estimation of travel time can be achieved.
II. EXP ERI MENTA L SIT E
This study has been carried out from two databases where
the measurements were taken in Angers, France (Fig. 1).
Before starting the experiments, the clocks of the two used
systems (cameras and electromagnetic loop stations) were
synchronized with the clock of the acquisition computer.
The information stored on different areas of measurements
was the electromagnetic signatures and video. Then, a data
processing is performed to combine the electromagnetic
signatures with video information. The characteristics of this
site are shown in Table I. It is worth noting that the distance
between the two areas is short but the difficulty of the vehicle
individual identification lies in the number of vehicles that
does not pass through the two instrumented areas. Fig. 2
shows the histogram of speed in km/hfor both populations:
cars and trucks. Fig. 2 shows that trucks have a lower average
speed than cars. Moreover, we note that the speed distribution
of trucks is less spread. These speed distributions will allow
us to establish the parameters of time windows which will
TABLE I
SITE CHARACTERISTICS
Site
Distance between the 2 areas 560 m
Speed control 70 km/h
Non-instrumented inputs 1
Non-instrumented outputs 1
Origin Destination
Number of lanes 2 2
Number of cars 1844 2881
Number of trucks 210 237
Number of vehicles passing through the 2 areas
Number of cars 1538
Number of trucks 206
Observations Short distance, uncongested
Fig. 1. Site: urban freeway. Fig. 2. Distribution with regards to speed.
be used with the identification methods presented in section
III.
III. IDENTIFICATION METHODS
A. data
In these experiments, the license plate recognition is
necessary and is carried out manually. An operator views
the video and identifies all the vehicles which passed over
the instrumented areas. Different fields are observed and
collected including the license plate, the vehicle type (car,
truck, van, ...), the timestamp, the file name containing the
signature associated with this vehicle. After this analysis, a
database is obtained with vehicles that have been in one or
two instrumented areas.
In these experiments, various characteristics are also mea-
sured from the Inductive Loops Detector (ILD) such as
sensor number, signature, timestamp. The ILD measures the
electromagnetic signature. This is a measurement of the
variation of the deformation of the magnetic field during
the passage of a vehicle on an electromagnetic loop. For
this study, the sampling frequency of ILD is 500 Hz. Fig. 3
presents the electromagnetic signatures of a car (signature
with one maximum) and a truck (signature with several
maxima). In the first approach, we can see a large difference
between these two signatures. In the studies presented in
[6], [10], [9], in order to compare the two signals of the
same vehicle passing over two different loop areas, various
Fig. 3. Example of car and truck signatures.
preprocessings were made. In this study, we have carried out
two preprocessings. Firstly, the speed of the vehicle could be
different between the two loop areas. Indeed, for example,
the first area can be composed of a lightly congestioned
flow whereas the second is composed of a non-congestioned
flow. Thus, to bypass this problem, the signal is normalized
to 96 points on the abscissa, and this is for each speed.
Secondly, the signal amplitude depends on the vehicle’s
type. Especially, it depends on the metal mass height of
the vehicle with regards to the sensor, and it also depends
on the vehicle’s position on the loop. Since the aim is to
compare the two signals coming from the same vehicle
but on two different sensors far away from each other, the
trajectories will be different and, therefore, their amplitude
too. To bypass this problem, each signal was normalized in
amplitude. Indeed, we have chosen to normalize the signature
maximum at the value 5000. Finally, in order to eliminate
the electronic noise of the signal, only values above a certain
threshold were recorded.
In this paper, each signature is re-sampled at 96 points,
whatever the vehicle type is (cars or trucks). This value
was chosen in order to conserve the maximum information
(especially for trucks), but also to keep a certain computa-
tional speed. We think that some extracted features should
be easier to use than the signature because they should give
a shorter summary. The choice of reliable features is not
really straightforward. [11] has already investigated some
properties of four features which are extracted from both
raw and normalized signatures. As in [4], [5], 206 features
are calculated from the signatures: 96 values associated
with the signature, 47 frequency variables coming from the
Fourier transform and 63 called ”global variables”’ which
are obtained by calculation. Among the features we are
interested in, there are for example : the so-called shape
parameter (SP) [11], the kurtosis, the mean, the median,
the Standard deviation, Normalized signature Fourier Trans-
form (FT) features among others. Thus, each signature is
characterized by 206 features, which present many data.
There are certainly redundancies. This also leads to great
calculation burden. Our goal is to select a few features among
the hundred collected. As in [5], we use the classical PCA
method in order to analyse the correlation between features
and to select independent features that contribute to the
most part of signature information. The goal is to select
the variables the most representative and the least correlated.
Unlike in [4], [5], in this paper, the database is composed
of cars and other vehicles (trucks, bus,...) . This analysis is
used to select the variables the most relevant for each type
of population. Three types of population have been analysed
: cars (Class 1 in the French norm [12]), other vehicles
(composed mainly of trucks and called in the following
”trucks”, Class 2 to 10 in the French norm [12]) and car-
truck population (Class 1 to 10).
In this analysis, each feature of vehicle is also important.
The features have been normalized (mean = 0 and standard
deviation = 1). In order to keep the most significant infor-
mation in the original data, we have selected 11 features
for cars, 9 features for trucks and 12 features for the mixed
population (cars and trucks). In the latter case, the database is
composed of 90% of cars and 10% of trucks. For cars, three
of these features are derived from the sampled signal (42nd,
56th and 90th signature components) , five are derived from
the Fourier transform (the real part of the 9th component, the
imaginary part of the 4th, 5t h and 6th components and the
module of the third component) and three global temporal
features are also identified: interval inside which all the
values are greater than the maximum value divided by 2,
standard deviation and skewness. For trucks, five features
are obtained from the Fourier transform (the real part of
the 1st and 2nd components, the imaginary part of the 2nd
and 3rd components and the module of the 3rd component).
In addition, four global features are selected (the secondary
maximum, the root mean square, the left root mean square
and the skewness). For the mixed population, twelve features
have been retained. Three features come from the sampled
signal (42nd ,43rd ,44th components of the signature), eight
features are derived from the Fourier transform (the real part
of the 2nd , 3rd and 7th components, the imaginary part of the
2nd , 3rd and 4th components, module of the 2nd component
and the quadratic sum of the eight first modules of the
Fourier transform component. There is also a time feature
which is the sum of the right part of the signature divided
by the sum overall. This subsection allows us to reduce
the feature number by PCA. We have established the most
prelevant features which are used afterward. Thus, each
vehicle is characterized by a vector xof pfeatures as follows
x= (x1,x2,...,xp)T.
In the ILD based vehicle reidentification problem, a down-
stream signature feature vector xdis compared to a set
of upstream vehicle feature vectors xu,iwith i= (1,..,n)
to find a feature vector pair from one single vehicle. An
additive noise n(t)is assumed to represent the measurement
uncertainties. Then, the upstream and downstream signals
can be written as xu,i=xi+nuand xd=x+nd. Errors
between the two feature vectors close to zero signify that
two signatures should likely correspond to the same vehicle.
The aim is to find a feature vector pair that minimizes the
error. In the paper, we propose to use three methods to solve
the problem: the Bayesian based learning approach, a fuzzy
logic method and the SVM method.
Note that in practice, vehicle signatures vary from different
detection stations. Indeed, the vehicle speed, the vehicle
position, the measured maximum amplitude and the signature
can be different for two ILD.
B. Bayesian based learning approach
In this approach, we assume that nis the noise vector in
which all the elements are uncorrelated and Gaussian with
zero mean. The covariance matrix of noise vector is then Σ.
The purpose is to find the best vector possible from a set
of upstream vectors to match a downstream vector [5]. We
can see this problem as the maximization of the probability
of xu,iknowing xd. According to Bayes’ theorem, we have
P(xu,i|xd) = P(xd|xu,i)P(xu,i)/P(xd). We deduce that the
only term P(xd|xu,i)P(xu,i)is to be maximized because P(xd)
is a constant normalization and it is not involved in the
decision. To solve this problem, [5] defines the so-called
discriminant function as :
gxd(xu,i) = −1
2(xd−xu,i)TΣ−1(xd−xu,i) + ln (P(xu,i))
−1
2(ln (|Σ|) + ln (2π)) (1)
The term −1
2(ln (|Σ|) + ln (2π)) is identical for all xu,i, and
we assume in this paper that the upstream candidate vehicles
are considered equiprobable, so equation (1) becomes:
gxd(xu,i) = −1
2(xd−xu,i)TΣ−1(xd−xu,i)(2)
Thus, the goal is to look for the maximum of the cost
function (eq. 2). A learning phase is carried out thanks to
a set of signature pairs and it consists in estimating the
covariance matrix Σ.
C. Fuzzy logic approach
All vehicles do not pass over the loops in the same
location. Thus, the magnitude and the signature are sub-
stantially different. To take into account the uncertainties
and inaccuracies of data, we propose to use a fuzzy logic
method to re-identify the vehicles. Unlike to the Bayesian
based learning approach, this method does not make any
assumption on the noise. However, we need to introduce the
fuzzy sets. Like [5], we use a trapezoid function; for each jth
component xu,i,jof the ith upstream features vector, a fuzzy
set Ei,jis defined as follows :
fE,i,j(xk) =
0 si xk/∈[ak−αk,bk+αk],
1 si xk∈[ak,bk],
1+xk−ak
αksi xk∈]ak−αk,ak[,
1+bk−xk
αksi xk∈]bk,bk+αk[.
(3)
with xk=xd,k−xu,i,kwhere xd,kand xu,i,krepresent the
kth element of vectors xdand xu,irespectively, k∈[1,p];
ak=mk−p1×σk,bk=mk+p1×σkand αk=p2×σk. The
parameters mkand σk, respectively the mean and the standard
deviation, are obtained by using a supervised learning phase.
The parameters p1and p2are determined in order to find the
best results. According to [5], the best result is obtained when
the trapezoid becomes a triangle for ak=bk(p1=0). The
discriminant function is defined by Fi(Xd) = ∑p
k=1fE,i,j(xk).
When the original signature is identical to that destination,
the discriminant function tends to the number of input
variables, such as p=11 for cars. A value that tends to p, the
number of input variables, reflects a great similarity between
the upstream and downstream signatures.
D. Support Vector Machine
The SVM algorithm was introduced by V. Vapnik [13],
this method is considered as the most powerful supervised
machine learning algorithm. This algorithm often achieves
superior classification performance compared to other learn-
ing algorithms in many domains and it is also fairly in-
sensitive to high dimensionality. The SVM algorithm is
based on the structured risk minimization principle. In this
section, we propose to use this method to re-identify the
vehicles. The studied problem can be seen as a binary
classification problem: re-identification of vehicles or not.
For the formalism of SVM, let us define the vector viof
size (p,1)as vi=xd−xu,i, with ithe number of candidates
and pthe number of features. Let A= (v1,u1),...,(vk,uk)
composed of kpairs with vi∈Rpbe the training set. In
this notation, the label uicorresponds to the identification or
non-identification of the vehicle.
In the case of linear separable data the basic idea of
SVM is to find a hyper plane f(v) = hw,vi+bwhich
separates positive uk= +1 and negatives uk=−1 training
examples and maximizes the margin ( 2
kwk) between sam-
ples and the hyperplane. Thus we can find the hyperplane
by minimizing kwksubject to the following constraints:
uk(hw,vki+b)≥1∀k. The prediction class of the vector:
f(v) = sign∑
k
α∗
kukhvk,vi+b, where α∗and bare the
solutions of the dual problem. In the case where the data are
nonlinearly separable, Vapnik proposes to map training data
in higher dimensional space Hby the function ϕ(.)through
dot products h.,.i, i.e. on functions of the form K(v1,v2) =
hϕ(v1),ϕ(v2)i. The prediction class of the vector: f(v) =
sign∑
k
α∗
kukK(vk,v) + b.
In this study, we have used C-SVM of the library LIBSVM
[14]. Afterwards, only the kernel ”RBF” (Radial Basis func-
tion) is presented. Indeed, tests have been carried out with the
linear kernel, RBF kernel and polynomial kernel. From these
tests, the best results were obtained by the RBF kernel. Thus,
several hyper-parameters are to estimate (C and γ) for C-SVM
with ”RBF” Kernel. The generic form of the ”RBF” Kernel is
K(x,y) = exp(−γkx−yk2). In order to use the SVM method
in the re-identification problem, we have taken into account
the distance between the sample and the hyperplane. The re-
identified couple will be the couple whose distance between
the hyperplane and the sample is maximum in the class of
label ”reidentified”.
IV. DISCUSSION
In this section, we propose to test the different identifica-
tion algorithms presented in the previous section. Firstly, the
TABLE II
PERFORMANCE (IR)WI TH THE C ROSS -VA LIDATIO N METH OD
Methods cars trucks Mixed
Bayes 70 % 89.3 % 69.6 %
Fuzzy 68 % 81 % 51.6 %
SVM 64.1 % 87.3 % 62.2 %
algorithms are compared on an ideal database. It contains
only signature pairs, so every destination has an origin and
vice versa. In this case, the removal of disruption has been
achieved thanks to the videos. The disruptive signatures are
the signatures coming from vehicles which are not passed
through the two instrumented areas. Secondly, the methods
are assessed on a database which is composed of signature
pairs and also disruptive signatures. Two criteria are used to
evaluate the performance, the identification rate (IR) and the
representation rate (RR) defined as follows:
IR =N umber o f correct pairs proposed
Tot al number o f cou ples pro posed (4)
RR =Number o f correct pairs pro posed
Tot al number o f cou ples id ent i f ied by vid eo (5)
To assess the methods, the entire database which is composed
of zvehicles is separated in a training-validation set (T-V )
and a test set (T). In this paper, the first set (T-V ) includes
z/3 vehicles.
A. Study of the ideal database
To analyse the methods, the IR has been chosen (in this
case, the IR and the RR represent the same magnitude).
1) Performance with cross-validation method: Firstly, we
use the cross-validation method to determine which one
provides the best results. The cross-validation is a statistical
method which allows us to evaluate and to compare learning
algorithms [15]. The basic form of cross-validation used in
this paper is the k-fold cross-validation. This method is used
on the training-validation set (T-V ). For this study, kis chosen
equal to 5. Knowing that the number of trucks is very low,
we used the cross-validation method on the whole ideal base
divided by k=5 segments. For SVM classifier with RBF
kernel, the two hyper-parameters Cand γare to set. These
parameters were estimated by cross validation for the three
population types (cars, trucks and mixed). Table II shows that
for this ideal base the Bayesian approach obtains the best
results, generally followed by SVM and the fuzzy approach.
Moreover, it can be noted that whatever the method, there is a
performance difference depending on the population. Indeed,
the IR is higher for trucks, followed by the IR for cars and
for the mixed population. The differences of performance
between the populations can be explained, on one hand by
the data size in each population, and on the other hand by
the features used for each population type.
2) Performance on test set : in this case, the first set (T-
V) including z/3 vehicles is used for the training and the test
set (T) is used to assess the methods. In this assessment,
TABLE III
PERFORMANCE (IR)WI THOU T AND WI TH THE T IME W INDOW S USED
without time window with time window
Methods cars trucks Mixed cars trucks Mixed
Real 100 % 100 % 100 % 98.6 % 99.6 % 99.5 %
Bayes 38.8 % 71.7 % 41.4 % 93.5 % 98.6 % 82.4 %
Fuzzy 36.7 % 69.6 % 17 % 92.1 % 97.8 % 81.2 %
SVM 27.9 % 79.7 % 33.6 % 88.2 % 95.7 % 83 %
Fig. 4. Histogram of candidate number for cars and trucks after time
window.
we use the same framework presented in [5], [7]. Thus,
we use a time window which consists of searching an
origin signature belonging to the time window. The time
window positionning is calculated with the traffic parameters
shown in table I. We use a rectangular window centered on
the mean value 60km/h, with half-width of 20km/h. The
rectangular window parameters are limiting factors because
some origin signatures are not inevitably in the time window
as shown in table III. Indeed, the line Real in Table III shows
the theoretical maximum performance with the used time
window. This window allows us to reduce on one hand the
computational burden and on the other hand the number of
candidates.
Table III shows the IR for the three methods with and
without the time window. Firstly, the time window allows
us to reduce the candidate number. Thus, the IR increases
strongly. The car average candidate number is about 8.6
whereas the truck average candidate number is about 1.7
as shown in Fig. 4. For trucks, 47.8 % identifications are
made only by the time window. When the time window
is used, the performance obtained by the mixed population
is less than that obtained by the two other populations.
Thus, in the remainder of the study, we will not use the
mixed population but only the two populations cars and
trucks separately. In practice, the classification ”truck-car”
is already operational on the French ILD which allows us to
process the data separately. Moreover, table III shows that for
this database the Bayesian approach is better, followed by the
Fuzzy approach and then SVM. Furthermore, table III also
shows that the rates obtained by trucks is higher than that
obtained by cars. One reason to explain these results is that
the car candidate number is higher than the truck candidate
number.
TABLE IV
IR AN D RR INTO BRACKETS FOR TRUCKS
Methods Bayes Fuzzy SVM una
Ref 91.3 % 90.6 % 98.5 % 98.5 %
(98.6%) (97.8 %) (95.7 %) (94.9 %)
OwMuD 97.8 % 97.8 % 99.2 % 99.2 %
(98.4%) (97.8 %) (95.7 %) (94.9 %)
Threshold 98.4 % 99.2 % 99.1 % 99.1 %
(89.1%) (89.9 %) (84.1 %) (80.4 %)
TABLE V
IR AN D RR INTO BR ACKE TS FOR C ARS
Methods Bayes Fuzzy SVM una
Ref 47.6 % 46.8 % 66 % 73.6 %
(92.3%) (90.7 %) (87.3 %) (84.3 %)
OwMuD 82.6 % 79.9 % 84.1 % 90 %
(89.2%) (87 %) (83.2 %) (78.3 %)
Threshold 69 % 66.3 % 70.5 % 78.2 %
(85.9 %) (85.6 %) (85.8 %) (80.1 %)
B. Study of the disturbed database
To reduce the error rate, we propose to compare three post-
processings. This study aims to evaluate the performance of
methods in order to select the most effective approach.
Firstly, we use the algorithms to match one unique origin for
each destination. During the signature matching process, it is
possible that two (or more) destination vehicles correspond
to the same origin vehicle. To alleviate this problem, we
firstly propose to search the origin vehicles mentioned several
times in the matching process. Then, for each origin, only
pairs in which the cost function is maximum are retained.
All other pairs with the same origin vehicle are eliminated.
This method is called in this paper ”OwMuD”.
Secondly, we propose to use several methods in parallel in
order to take into account the proposed pairs by each method.
Unlike a majority vote, the unanmous vote does not need an
odd number of methods. However, this type of framework
is only interesting if each method has a different behavior.
In the paper, the method using the unanimous vote will be
called ”una”.
Finally, we also use a threshold on the cost function of each
method in order to distinguish a maximum of false pairs.
Each method calculates the cost for each pair. The higher
the cost is, the higher the probability will be of validity of
the pair. The decision threshold is calculated on the training
database. The introduction of these thresholds implies a
compromise between the IR and the RR. As mentionned in
section IV-A.2, we use the same time window. Tables IV
and V show the IR (and the RR into brackets) for cars and
trucks in four cases : reference, OwMuD, threshold and una.
The reference case is only composed of the time window.
Therefore, the performance of this situation represents the
reference to evaluate the proposed post-processings. The
threshold is chosen such that 90 % of RR on the training
base is retained.
For the reference case, the IR for trucks are especially
Fig. 5. Estimation of individual travel time for cars and trucks.
high for both SVM and vote unanimously. Moreover, the
RR shows that almost all pairs are predicted for trucks.
The car RR is slightly lower than the trucks RR. If we
compare the methods with one another, SVM provides the
best performance. For the reference case, the good rates of
trucks are partly produced by a small number of perturbation
in this population. In comparison with the reference case,
a strong improvement of IR and a slight decrease of RR
are obtained by the OwMuD case for cars. For trucks, the
performance is substantially the same between the reference
and OwMuD configurations. The only observation is the
increase of IR for the methods only. The filter allows us
to cancel a large number of couples containing disruptions.
However, some true pairs are also rejected. The threshold
solution allows us to obtain better IR in comparison with
the reference case. However, a compromise must be found
between the IR and RR which is difficult to make (especially
to choose the threshold values). Tables IV and V show that
the best results are obtained by the OwMuD solution with a
unanimous vote.
C. Estimation of travel time
This section presents an estimation of individual travel
time of cars and trucks. The solution used is the OwMuD
solution with a unanimous vote whose performance is pre-
sented in the previous section. Figure 5 shows travel times
observed and estimated for cars and trucks with regards to
the elapsed time for 15 minutes. Figure 5 shows that there
is a perfect matching between the travel times observed and
estimated for trucks on a elapsed time of 15 minutes. Note
that the IR obtained is about 99 % for the test database. For
the cars, the IR is about 90 %. Thus, some estimated travel
times do not match to the observed travel times and some
observed travel times are not estimated. The estimation error
mean is less than 1 second for the 54 minutes of experiments
for both population types.
V. CONCLUSIONS
This paper is dealing with the use of existing widespread
ILD Network in order to realize an estimation of individual
travel time for a mixed population of cars and trucks. The
identification of the vehicles is realized by the comparison of
destination inductive signature features with the origin induc-
tive signature features by an identification method. Firstly,
we choose the most significant features for three population
types : cars, trucks and mixed population. Then, we propose
to use three identification methods : the Bayesian based
learning approach, the fuzzy logic method and the SVM
method. To avoid mismatching vehicles, several propositions
are made such as the unanmous vote, the use of threshold
on the cost function (or distance sample-hyperplane) and the
use of a filter which allows us to remove the estimated pairs
having an origin for multiple destinations. To estimate the
individual travel time, we have choosen the OwMuD solution
with a unanimous vote. In this case, the travel time estimation
average error is less than 1 second for cars and trucks.
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