Evaluating the Predictability of Future Energy Consumption

Application of Statistical Classification Models to Data from EV

Charging Points

Pietro Faes Belgrado

1,2

Ľuboš Buzna

1,3

, Federica Foiadelli

and Michela Longo

ERAchair for Intelligent Transport Systems, University of Žilina, Univerzitná 8215/5, Žilina, Slovakia

Department of Energy, Politecnico di Milano, Milan, Italy

Faculty of Management Science and Informatics, University of Žilina, Univerzitná 8215/5, Žilina, Slovakia

Keywords: Electric Mobility, Predictability, Energy Consumption, the Netherlands, Statistical Learning.

Abstract: The overall purpose of our study has been to evaluate the predictability of future energy consumption

analysing the electric mobility in the Netherlands. The climate and energy framework, the European energy

production and main developments, as well as the European targets and policy objectives to reduce the current

emissions were first assessed. Then, a deeper look was taken at electric mobility and at Electric Vehicles

(EVs). The adoption and development of EVs in the European Union and charging infrastructure were taken

into account. The Dutch energy production and emissions, as well as, the mobility in the country and its

infrastructure were investigated. Previous studies about electric vehicles and charging points have addressed

the predictability of future energy consumption in larger areas to only very limited extent, so our research

work has concentrated on this gap. A large real-world dataset was used as a basis to create statistical models,

in order to study the users’ behaviour within the charging points infrastructure and to evaluate the

predictability of future energy consumption of the charging points in selected regions of the Netherlands.

Results vary across different regions with the number of charging points, but suggest that statistical models

could be useful in the management of energy production to optimize the dispatch of energy sources.

1 INTRODUCTION

The power supply has granted huge benefits for the

modern society, but it has also come with a not

negligible price tag: most of the energy is generated

from fossil fuels (coal, oil and natural gas). This

human activity is increasing the concentrations of

greenhouse gases (GHG), while it is enhancing the

greenhouse effect, which in turn is contributing to the

warming of the Earth. It is therefore crucial to reduce

GHG emissions, in order to maintain the stability of

the Earth and its climate.

The European primary energy production

includes a range of different energy sources: nuclear

energy, renewable energy sources, solid fuels (largely

coal), natural gas and crude oil. The primary energy

consumption in European countries in 2015

amounted to 1,627 million tonnes of oil equivalent

(Mtoe), while being 1.7% above the 2020 target.

Primary energy consumption in the EU-28 countries

has decreased in the past years, due to energy

efficiency improvements and the economic recession.

Moreover, the share of the energy generated from

hydro, wind and solar has been steadily increasing.

Nevertheless, fossil fuels continue to dominate

primary energy consumption, setting themselves at

72.5% in 2015. Renewable energy was 13% and the

part of nuclear energy in primary energy consumption

was 13.6% in 2015 (EEA, 2016).

In the transport sector, despite the improvements

in fuel efficiency, there have been increases in

passenger and freight transport demand. Higher

transport demand has resulted from increased

ownership of private cars, particularly in the new EU

Member States, as well as from growing settlement

and urban sprawl, leading to longer distances

travelled, and changes in lifestyle. Between 2005 and

2013, final energy consumption in the transport sector

decreased by 6% in the EU-28, but it still accounted

for 32% of total energy consumption, followed by

household sector (27%), the industrial sector (25%)

and the service sector (14%) (IEA, 2017).

Faes Belgrado, P., Buzna, L., Foiadelli, F. and Longo, M.

Evaluating the Predictability of Future Energy Consumption.

DOI: 10.5220/0006815206170625

In Proceedings of the 4th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2018), pages 617-625

ISBN: 978-989-758-293-6

617

The transport sector is therefore the main factor

responsible for air pollution in European cities, as it

produces almost a quarter of all GHG emissions.

There has been a decrease in the emissions since

2007, but they are still higher than in 1990. Road

transport, in particular, was considered responsible

for more than 70% of GHG emissions from transport

sector in 2015. The European Commission has

adopted a low-emission mobility strategy to facilitate

shift towards a low-carbon circular economy. GHG

emissions from transport should be at least by 60%

lower than in 1990 and be firmly on the path towards

zero. The strategy integrates a broader set of measures

and supports creation of jobs, economic growth,

investments and innovations. The strategy will

benefit European citizens and consumers by

improving the quality of the air, reducing the levels

of noise, lowering the levels of congestion and

ameliorating the safety (National Research Council,

2013).

Electromobility and renewable sources of energy

are technologies with significant potential to

contribute to these goals. In order to evaluate the

predictability of future energy consumption, an

analysis of electric mobility is done in this paper. In

particular, this analysis is applied to a large real-world

dataset. Studies based on the operational data from

charging station started to appear in the scientific

literature only very recently. Data collected from

mobile phones and charging stations has been

analysed and combined with mathematical modelling

to analyse the relationship between mobility

behaviour and the demand for electric energy (Colak,

2016). Regression methods were used to estimate the

driving range of electric vehicles (Fetene, 2017).

Combination of regression methods and data time

series, that characterize the charging behaviour, was

used to design a procedure how to predict the load

profile imposed on the electrical grid by individual

charging stations (Bickora, 2016). Classification

methods of statistical learning (k-nearest neighbour,

regression trees, general chi-squared automatic

interaction detector, etc.) when applied to time series

of electric energy consumption can identify

households that are charging electric vehicles with

accuracy higher than 80% (Verma, 2015). Similarly,

classification has been applied to the data from the

charging station network to analyse how the drivers

of electric vehicles use the public charging stations

and to predict their behaviour (Develder, 2016;

Sadegghianpourhamani, 2018). Regression approach

combined with characterization model based on fuzzy

numbers was applied to data coming from 255

charging stations in UK and was used to propose an

index estimating the ability of electrical power grid to

manage the future load imposed by charging electric

vehicles (Xydas, 2016). One of our goals is to apply

regression methods to maintain large interpretability

of results. Interpretability can be enhanced by the use

of methods that contain mechanisms to select

potentially relevant predictors (Taylor, 2015; Hastie,

2009; James, 2013). From the point of view of

optimization, these mechanisms and optimization

methods that can be used to solve associated

optimization problems are subject of very active

research (Bertsimas, 2016; Hastie 2017).

2 BACKGROUND

INFORMATION

Our case study is focused on the Netherlands, which

was chosen as it is one of the European countries with

the greatest developments in electric mobility in the

past years. The advanced economy of the Netherlands

is reflected also in a modern energy system and well-

developed energy markets. The strategic location

helped the country become an important transit and

trade hub for natural gas, coal, oil and electricity. The

Netherlands has significant natural gas production

and a large oil-refining industry (IEA, 2014).

The Netherlands started the transition to a low-

carbon economy by implementing broad spectrum of

measures ranging from Renewable Energy Sources

(RES), Carbon Capture and Storage in the North Sea,

and to the enhancements of the security of oil and gas

supply. The Dutch energy mix is dominated by fossil

fuels, which represent more than 90% of Total

Primary Energy Supply - TPES (IEA - Statistics,

2015). In Figure 1, it is possible to observe that the

electrical energy sources have mainly been natural

gas accounting for 42% and coal with 39%, oil has

only a marginal contribution of 1.3%. In the last ten

years, the renewables sources raised a lot, reducing

the share of fossil fuels from 90.3% in 2002 to 82%

in 2015. Nuclear energy represented 3.7% in 2015.

The renewable sources in the Netherlands are mainly

biofuels 2.7%, waste 3.3% and wind 6.9% (Centraal

Bureau voor de Statistiek, 2016b).

Dutch citizens travelled on average 11,000 km in

2014. 70% of this distance was covered by car (while

nearly three-quarters of the total number of

kilometres as drivers and nearly a quarter as

passengers), 9% of the total distance was done by

train and 9% by bicycle (Centraal Bureau voor de

Statistiek, 2016a).

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The Dutch government’s desire for electric

driving is to reduce CO2 emissions, improve energy

efficiency and decrease the dependency on fossil

fuels (Netherland Enterprise Agency, 2015). Electric

vehicles also help to reduce noise pollution from

traffic, open up new opportunities for the commercial

sector and generally improve the quality of life in

cities.

Figure 1: Generation of electrical energy by source in the

Netherlands (Centraal Bureau voor de Statistiek, 2016b).

Table 1 reports for different years the number of

EVs in the Netherlands.

Table 1: Number of electric vehicles in the Netherlands.

Fully electric

(FEV)

Hybrid

(HEV)

Plug-in hybrid

(PHEV)

2015 7,400 111,800 36,750

2016 9,950 124,400 76,250

2017 13,700 136,000 95,750

There were more than 8 million cars in the

Netherlands at the beginning of 2017, almost 80% of

them run on petrol and 16% on diesel. Nearly 250,000

are electric vehicles but in this group 90% consists of

hybrid cars. There are also more than 13,000 fully

electric cars, with just the electric engine (see Fig. 2).

This number has doubled in the past two years and

there are 26,000 public or semi-public charging

points for EVs across the Netherlands, that is to say

on average 4 electric vehicles per charging point

(Centraal Bureau voor de Statistiek, 2016a).

3 MATERIALS AND METHODS

In this research, data recorded from individual

charging transactions were considered at a more

aggregated level, to give a greater picture of the

interaction between the charging point infrastructure

and the power grid network. As for modelling smart

grids, it is more important to predict how an

aggregated subset of charging points will consume

energy in a specific area, rather than to consider

single points. An extensive amount of data from a

large database gathered in the Netherlands was used

with high temporal resolution, focusing on the

variability in demand for electricity to charge electric

vehicles. This is different from previous studies,

where synthetic time-series were used because of a

small number of transactions recorded (Bessa and

Matos, 2013) or (Develder, 2016).

3.1 Dataset

The Dutch company ElaadNL (ElaadNL, 2016)

provided for this research a dataset which includes an

overview of EVnetNL historic transactions data from

January 2012 until March 2016. The dataset contains

detailed information of more than 1 million

transactions that took place at 1,747 different

charging points, which are installed over the entire

geographical area of the Netherlands. The dataset is a

big data frame (table implemented in R language)

with 17 columns and 1,060,703 rows - corresponding

to every transaction registered.

The analysis on the dataset has been done using

Rstudio - a free open source IDE (integrated

development environment) software program for

statistical analysis. There are many reasons for

choosing this kind of software, above all it has a great

availability of implemented advanced statistical

methods and algorithms and it produces very high-

quality graphs. Overall, Rstudio is a very powerful

but at the same time an easy-to-use software.

3.2 Statistical Methods

Before starting the data mining, it was necessary to

build a statistical frame. Different classification

models were considered to evaluate the predictability

of future energy consumption of the charging points.

There are many possible classification techniques, or

classifiers, that could be used to predict a qualitative

response (James, 2013). Four of the most widely-used

classifiers - logistic regression (Logistic), linear

discriminant analysis (LDA), quadratic discriminant

analysis (QDA) and k-nearest neighbours (KNN) -

were used in the study, in order to build a model to

predict () for any given value of predictors









,…,



.

Evaluating the Predictability of Future Energy Consumption

619

Rather than modelling the response  directly,

logistic regression models the probability that 

belongs to a particular category with the logistic

function:







1p













⋯







(1)

Model parameters 



,….



are estimated by the

maximum likelihood method. For linear discriminant

analysis the distribution of the predictors  is

modelled separately in each of the response classes

and then the Bayes’ theorem is used to flip these

around into estimates. LDA assumes that the

observations within each class are drawn from a

multivariate Gaussian distribution with a class-

specific mean vector μ and variance σ and covariance

matrix Σ that is common to all the classes. By the

symbol 



is represented the prior probability that a

randomly chosen observation comes from the k-th

class. Then, LDA assigns an observation to the class

X = x for which the value:





























2



log





(2)

is the largest. QDA provides the same approach

but it assumes that each class has its own covariance

matrix 



Consequently, Eq. (2) generalizes to:





































log







(3)

The KNN classifier, given a positive integer 

and a test observation 



, first identifies the  points

in the training data that are closest to classified

observation 



within the neighbourhood 



. Finally,

KNN applies Bayes rule and classifies the test

observation 



to the class k with the largest

probability given by:



























(4)

where 



 is indicator function that equals 1

if 



and 0 otherwise.

3.2.1 Shrinkage Methods

In order to enhance the prediction accuracy and

interpretability of the statistical model, Lasso method

(least absolute shrinkage and selection operator) was

implemented (Taylor, 2015). It is a shrinkage method

that performs both variable selection and

regularization. In this way, it is possible to select the

most significant predictors and to eliminate those that

do not contribute to the improvement of the forecast,

obtaining at the end a lower test error. A model can

be fitted containing all  predictors and then select a

subset of them using Lasso that constrains or

regularizes the coefficient estimates, or equivalently,

that shrinks the coefficient estimates towards zero.

4 RESULTS

4.1 Basic Analysis of the Dataset

Having built the statistical frame, the dataset

“Transactions” provided by ElaadNL was analysed.

Four of the seventeen columns were selected:

ChargePoint ID, Connector ID, Lat (latitude) and Lon

(longitude) to create a map of the Netherlands with

the positions, distinguishing the charging points with

one connector from those with two connectors. 613

out of the 1,747 charging points contained in the data

frame are with one connector while 1,134 are with

two connectors. The charging points are well

distributed across the entire territory of the country,

while the majority of them is situated within or close

to the major cities as shown in Figure 2.

Figure 2: Positions of charging points with 1 and 2

connectors derived from the dataset.

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Next, columns from the “Transactions” data frame

were selected, in order to describe the use of

individual charging points: Connected Time, Charge

Time and Idle Time. It was observed from data that

in most cases, the number of transactions is lower

than one thousand and the Connected Time is less

than 10 hours, with a strong presence transactions that

lasted up to 5 hours. Comparing the maximum power

recorded during the transaction in terms of kW and

the total number of transactions for each customer

card, Figure 4 was obtained. Four power categories

are clearly coming out from the scatter plot. The first

category is slightly above 2 kW, the second stays

between 3 and 4 kW, the third about 9 kW and the last

one about 11 kW. Figure 3 gives also rough picture of

the types of vehicles that are utilizing public charging

stations; most of them with a maximum power around

3 kW, that is the typical value for hybrid plug-in

electric vehicles.

Figure 3: Maximum power observed for each customer card

compared to the number of transactions done.

Considering all the points together, the recharge

time was then analysed throughout the course of the

day as represented in Figure 4. For more than half of

the time, when the charging points were connected to

the vehicles, they were not recharged, but simply

occupied a parking position because the state of

battery had already reached the maximum charge. At

a first glance, it is clear that such behaviour of EV

drivers is not favourable.

Figure 5 shows the trend obtained when

considering all charging transaction done within the

period starting from January 2012 to March 2016.

The pattern formed by the initial time of charging

sessions over the course of the working day and

weekends are compared in Figure 5. Working days

follow a completely different pattern than weekends.

During the working days, two peaks are very evident,

the first is at about 7:30, the second is at about 17:30.

Between 9:00 and 15:00 it is possible to observe

approximately constant trend. Over the weekends, it

is completely different: most of the charging

Figure 4: Charge Time and Idle Time as a percentage of the

Connected Time analysed within the course of the day.

Figure 5: Initial time of charging sessions during (a) the

working days and (b) weekends.

transactions start at 13:00, with a wider peak, which

lasts from 10:30 to 15:30.

After taking the look at the charging points, the

users were brought into focus. In the database

“Transactions” there are two records for each

charging session: StartCard and StopCard, that can

be used to attribute charging session to individual

RFID card holders. Having observed that the total

amount of users (customer cards) is 53,850, the

behaviour of the different users was analysed. The

Evaluating the Predictability of Future Energy Consumption

621

first step was to find out the first use and the last use

of the single card, in order to underline how long it

was used during the period between January 2012 and

March 2016.

Significant number of cards (about 15,000) were

used only once. Considering the number of

transactions for each unique card, it was possible to

see that the majority of the cards were used only for

few charging sessions during the period considered:

95% of the cards did less than 100 transactions. In

Figure 6, it is possible to observe that the cards with

a large consumed energy constitute only a small

number and a focus is on the energy consumed.

Figure 6: Number of customer cards as a function of the

total number of realised transactions. The inset shows the

dependency on the consumed energy.

4.2 Predictability of Energy

Consumption

To aggregate the power consumed by individual

charging points the following step was to extract from

the coordinates (latitude and longitude) of the single

charging point the address where each charging point

is located. From the address the corresponding

municipality was obtained and then associated with

the COROP region it belongs to. The COROP are the

Dutch sub-regions traditionally used in the spatio-

statistical research. The association of charging points

to COROP regions is shown in Figure 8, while

different colours are used, depending on the region.

Five out of the 40 COROP regions were selected,

based on the number of charging points available, to

represent broad range of values. In Figure 7, it is

possible to identify different zones, in particular:

 Delfzijl en omgeving indicated with number 1 (3

charging points);

 Zuidoost-Zuid-Holland indicated with number 2

(20 charging points);

 Flevoland indicated with number 3 (41 charging

points);

 Overig Zeeland indicated with number 4 (57

charging points);

 Utrecht indicated with number 5 (172 charging

points).

Through the association of charging stations with

the regions it was possible to extract the aggregated

trend of the energy consumption for the recorded

period (January 2012 - March 2016). The next step

was to restrict five selected benchmarks to the years

2014, 2015 and 2016. This choice was made because

during these years the number of transactions is more

substantial.

Figure 7: Charging points associated to COROP regions.

With the resolution of days, the transactions

carried out on the charging points of a given COROP

region were grouped: the 819 rows, each

corresponding to one day, were recorded with the

following structure of columns: Total Electric Energy

Consumed, Total Connected Time (in hours), Total

Idle Time (in hours), Total Transactions performed

during a day, Total Number of single cards used

during the day and Total Number of Charging Points

used during the day (thus, overall 6 columns). Some

auxiliary columns were subsequently added: the Total

Electric Energy Consumed, the Connection Time and

the Idle Time for each of the 7 days preceding the

concerned day. In addition, the total number of

customer cards used and the number of transactions

made in the previous two days were considered,

resulting in 25 new auxiliary columns.

As the output or response was considered the

binary forecast of the increase (1) or decrease (0) in

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622

energy consumption compared to the previous day.

Thus, a column was added that indicates whether the

total energy consumption of a day was higher or

lower than the day before. This column is called

direction. Values 1 and 0 are the two classes

considered in the statistical methods.

Statistical models estimate the probability that

with a set of predictor variables, the unknown

response belongs to one rather than to the other class.

Basic statistical classification methods, described

in Section 3.2, were applied to each of the five

benchmarks, to find out which method is producing

the lowest test error (James, 2013). For the KNN

method, two versions were considered. First, version

denoted KNN (K=5), where the value of parameter K

was set to 5 and KNN opt, where 10-fold cross

validation method (James, 2013) was used, to find the

value K leading to the minimum value of the test

error. Furthermore, two extended versions of the

methods were considered. First, the logistic

regression was combined with the Lasso shrinkage

and the second, in addition, it was followed either by

the KNN opt (denoted as Logistic Lasso KNN opt) or

LDA (denoted as Logistic Lasso LDA) depended on

what resulted in lower test error. To evaluate the test

error, predicted directions for the future energy

consumption were calculated and evaluated using 10-

fold validation method.

Figures 8, 9 and 10 show the test error rates

considering the region of Delfzijl en omgeving,

Zuidoost-Zuid-Holland and Flevoland. In particular,

in the case (a) are reported the five basic classification

methods and in the case (b) are reported results of two

extended methods (logistic regression combined with

the Lasso shrinkage and logistic regression combined

with the Lasso shrinkage and followed by the optimal

KNN method). To facilitate the comparison between

extended and related basic methods the performance

of basic methods is again shown in panel (b).

Figure 8: Region of Delfzijl en omgeving with (a) five basic

classification methods and (b) two extended methods.

Figure 9: Region of Zuidoost-Zuid-Holland with (a) five

basic classification methods and (b) two extended methods.

Figure 10: Region of Flevoland with (a) five basic

classification methods and (b) two extended methods.

Average test error rates for logistic regression

range from 0.19 to 0.35. On the smallest benchmarks,

Delfzijl en omgeving, Zuidoost-Zuid-Holland, and

Flevoland the logistic regression performed well,

however, better results were obtained through KNN

opt (see Figures 8a – 10a).

In opposite, for the other 2 benchmarks: Overig

Zeeland and Utrecht a slightly better performance

was gained with the logistic regression method (see

Figures 11a – 12a). As expected, KNN opt always

showed

better results than KNN (K = 5), and

corresponding test error was the lowest among all

basic methods for the first three datasets.

Performance of LDA is systematically better than

QDA, which is inferior when compared to all basic

methods, indicating an overfitting.

Figure 11: Region of Overig Zeeland with (a) five basic

classification methods and (b) two extended methods.

Evaluating the Predictability of Future Energy Consumption

623

Figure 12: Region of Utrecht with (a) five basic

classification methods and (b) two extended methods.

Logistic regression was further implemented for

all the datasets together with the shrinkage method, in

order to evaluate the most relevant variables among

the 25 considered and possibly improve the accuracy

of predictions. For the first dataset, leaving out

predictors leads to only very small improvement of

the Logistic regression. However, when applying

KNN opt to the shrunken model (in this case the

model was reduced by Lasso to only one predictor:

the Total Electric Energy Consumed with the lag of

one day) there was a significant improvement making

the error fall below 15%. This is very good result, but

unfortunately it was obtained only if consumption of

only three charging stations was aggregated together

and often less than ten transactions were recorded

during the day, thus the energy consumption was also

very small (see Figure 8). Such situations are not so

relevant from the point of view of smart grid control.

Nevertheless, apart from this exceptional case,

logistic regression finds systematically the lowest test

error for the last benchmark, where the consumption

of the largest number of charging points (172) was

aggregated together. This is aligned with the

expectation that higher aggregation leads to higher

predictability. Moreover, it has also practical

implication as predicting higher loads in smart grids

can be more relevant in the planning and management

of load dispatch. For the last benchmark, the Logistic

regression combined with Lasso shrinkage identified

as the most relevant predictors: Total Electric Energy

Consumed with the lag of 1 and 7 days, Total

Connected Time with the lag of 2, 6 and 7 days, and

Total Idle Time with the lag of 1 day. Interestingly,

most often, relevant predictors have lag of 1 or seven

days. Predictors selected by the Lasso method differ

from one benchmark to another. However, one which

was always present is the Total Electric Energy

Consumed in the previous day. Since the selected

predictors varied among the 25 initially considered,

even the values of coefficients were significantly

different across benchmarks.

5 CONCLUSIONS

The Dutch energy framework and the electric

mobility were investigated using data collected

within the large network of charging stations. The

Netherlands is steadily working towards replacement

of vehicles powered by traditional fossil fuels to build

a future with significantly higher share of electric

vehicles. For such a progressive country - with a low

average length of daily journeys by private cars and a

high number of commutes made by bicycle or public

transportation - this could be possible in a close

future.

The need to develop more and more charging

infrastructure for electric mobility requires a detailed

studies of energy consumption on already existing

charging points. As an initial step, predictive models

to estimate the trend in the electricity consumption

one day ahead were built. At first glance, it seems that

the data used during the study - the charging

transactions done over three years - can be useful to

approach this objective. The models managed to

assess well the trends in five Dutch regions, although

with different results. In summary, three main

interesting findings arose from the analysis:

• It is shown that different models are more suitable

in fitting the data depending on the region (level

of aggregation). Most often, logistic regression or

its combination with other method was found as

the most successful method.

• The average test error ranges from 0.19 to 0.39.

By increasing the number of charging stations

aggregated in the analysed energy consumption

signal, the test error tends to decrease. Thus, the

idea to aggregate the charging points leads to the

higher accuracy of predictions.

• As the most important variables for the

predictions, were identified variables with the lag

of 1 or 7 days, indicating the importance of the

most recent history and the role of the week cycle.

Obtained results and the need for more efficient

management of energy production in future smart

grids justifies further studies where energy

consumption could be modelled in a more detailed

way, e.g. by introducing multi-class classification

models to predict the amount of energy consumed on

hourly basis.

ACKNOWLEDGEMENTS

This work was supported by the research grants

VEGA 1/0463/16 ”Economically efficient charging

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624

infrastructure deployment for electric vehicles in

smart cities and communities”, APVV-15-0179

”Reliability of emergency systems on infrastructure

with uncertain functionality of critical elements”, by

the Alan Turing Institute, call for collaboration in the

Lloyd’s Register Foundation Programme to support

data-centric engineering under grant number LRF16-

05, and it was facilitated by the FP 7 project ERAdiate

[621386]”Enhancing Research and innovation

dimensions of the University of Žilina in Intelligent

Transport Systems”. We thank to Marcelo Masera

(EC, JRC in Petten) and to Nazir Refa (ELAADNL)

for enabling the access to the dataset.

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