energies
ArticleVehicular Fuel Consumption and CO2 Emission Estimation
Model Integrating Novel Driving Behavior Data Using
Machine Learning
Ziyang Wang 1, * , Masahiro Mae 1 , Shoma Nishimura 2 and Ryuji Matsuhashi 1
1

2*

Citation: Wang, Z.; Mae, M.;
Nishimura, S.; Matsuhashi, R.
Vehicular Fuel Consumption and CO2
Emission Estimation Model
Integrating Novel Driving Behavior
Data Using Machine Learning.

Department of Electrical Engineering and Information Systems, The University of Tokyo,
Tokyo 113-8656, Japan; mmae@ieee.org (M.M.); matu@enesys.t.u-tokyo.ac.jp (R.M.)
Department of Digital Business Design, Aioi Nissay Dowa Insurance Co., Ltd., Tokyo 150-8488, Japan;
shoma-nishimura@aioinissaydowa.co.jp
Correspondence: wang-ziyang@ieee.org; Tel.: +86-156-5291-1197

Abstract: Fossil fuel vehicles significantly contribute to CO2 emissions due to their high consumptionof fossil fuels. Accurate estimation of vehicular fuel consumption and the associated CO2 emissions
is crucial for mitigating these emissions. Although driving behavior is a vital factor influencing fuelconsumption and CO2 emissions, it remains largely unaddressed in current CO2 emission estimation
models. This study incorporates novel driving behavior data, specifically counts of occurrencesof dangerous driving behaviors, including speeding, sudden accelerating, and sudden braking, as
well as driving time and driving distances on expressways, national highways, and local roads,
respectively, into monthly fuel consumption estimation models for individual gasoline and hybrid
vehicles. The CO2 emissions are then calculated as a secondary parameter based on the estimated
fuel consumption, assuming a linear relationship between the two. Using regression algorithms,
it has been demonstrated that all the proposed driving behavior data are relevant for monthly
CO2 emission estimation. By integrating the driving behavior data of various vehicle categories, ageneralizable CO2 estimation model is proposed. When utilizing all the proposed driving behavior
data collectively, our random forest regression model achieves the highest prediction accuracy, with
R2 , RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg, respectively, for monthly CO2 emission
estimation of individual vehicles. This research offers insights into CO2 emission reduction andenergy conservation in the road transportation sector.
Keywords: vehicular CO2 emission; eco-driving; dangerous driving behavior; machine learning;random forest

Energies 2024, 17, 1410. https://doi.org/10.3390/en17061410
Academic Editor: Pavel A. Strizhak
Received: 19 February 2024
Revised: 12 March 2024
Accepted: 13 March 2024
Published: 14 March 2024

Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

1. Introduction
In 2020, road transportation accounted for 24% of the European Union’s total carbon
dioxide (CO2 ) emissions, making it as the predominant greenhouse gas responsible for
global warming [1]. Data from 2021 reveal that China’s transportation sector consumed
energy equivalent to approximately 530 million tons of standard coal, accounting for 15.3%of the country’s overall energy consumption [2]. In the same vein, emissions from all
passenger vehicles in Japan in 2020 made up 8.9% of the country’s total CO2 emissions [3].
By 2022, the International Energy Agency (IEA) reported that personal vehicles, such ascars and vans, were responsible for more than a quarter of the global oil consumption andapproximately 10% of the worldwide CO2 emissions linked to energy use [4].
The adverse effects of CO2 emissions on both environmental sustainability and publichealth have been thoroughly documented in various studies, emphasizing the urgent
need to substantially reduce these emissions [5]. This urgency is driven primarily by two
reasons: First, the global commitment to mitigate climate change impacts, and second,the understanding that reducing CO2 emissions, through measures like decreasing fuel

Energies 2024, 17, 1410. https://doi.org/10.3390/en17061410

https://www.mdpi.com/journal/energies

Energies 2024, 17, 1410

2 of 16

consumption, contributes to conserving our limited fossil fuel reserves [6,7]. However,despite the adoption of various strategies encompassing technological innovations [8]and policy regulations to curb CO2 emissions, the increasing number of gasoline vehicles
worldwide still presents a significant challenge to CO2 emission reduction efforts [9–12],
emphasizing the importance of accurate vehicular CO2 emission estimation.
Accurate vehicular CO2 emission estimation is crucial, as it not only provides feedback
to drivers but also bolsters efforts in CO2 emission reduction and energy conservation.
In response to the mounting concerns over CO2 emissions, Japan introduced the J-Credit
Scheme in 2013 [13]. This government initiative promotes activities that diminish greenhouse gas emissions, aligning with the objectives of the Paris Agreement [14]. The J-Credit
Scheme issues “J-Credits” for a myriad of activities, like renewable energy projects, energy
efficiency enhancements, or activities that absorb CO2 , such as afforestation. Once these
credits are issued, they can be traded, enabling entities to count them towards their greenhouse gas reduction targets. Such a mechanism bolsters the financing and incentivizationof greenhouse gas reduction endeavors. There is potential in integrating CO2 emission
estimation results with the J-Credit Scheme, enhancing its effectiveness.
Prominent vehicular CO2 emission models like MOBILE6 [15], developed by the U.S.
Environmental Protection Agency (EPA), and EMFAC7F [16], proposed by the California
Air Resources Board (CARB), strive to integrate factors related to travel, weather, andvehicle characteristics in their CO2 emission estimations. In particular, these models
primarily utilize metrics such as average speed and total vehicle miles traveled for CO2
emission calculations. However, they often overlook the pivotal influences of roadway
conditions, traffic dynamics, and drivers’ behaviors on vehicular CO2 emissions. Several
studies have explored this area in depth. In [17], Oduro et al. introduced a dynamic
real-time fuel consumption estimation model based on multiple linear regression (MLR),
underscoring a linear relationship between CO2 emissions and both vehicle speed andacceleration. This suggests that these two factors play a significant role in determining CO2
emission levels. In [18], Ahn et al. presented microscopic fuel consumption and emission
models that leverage instantaneous speed and acceleration data by utilizing polynomialand hybrid regression models. This approach offers granular insights into the microscopic
interactions between driving behaviors and fuel usage. In our preliminary work [19], we
employed the random forest algorithm to estimate instantaneous fuel consumption using
the instantaneous speed and acceleration data of a specific vehicle type in Japan. Whileestimating instantaneous fuel consumption has its merits, such as providing immediate
feedback for efficient driving, promoting eco-driving, and aiding in traffic planning, it has
limitations in capturing the broader picture. The focus on instantaneous CO2 emissions cansometimes overshadow the significance of long-term accumulated CO2 emissions, which
are often of greater concern.
It has been revealed that the higher the frequency of speeding, sudden acceleration,and sudden braking, the lower the efficiency of the internal combustion engine [20,21].
In [22,23], Lois et al. and Jimenez et al. demonstrated that driving behaviors such as therate of deceleration, revolutions per minute (RPM), and speed significantly influence fuelconsumption. In [24], Mane et al. indicated that factors such as average speed, braking,and idling significantly influence fuel consumption. Congestion exacerbates this issue,
leading to more frequent braking and reduced fuel efficiency, as reported in [25,26]. In
contrast, Samaras et al. [27] demonstrated that free flow conditions result in a 4.9% decreasein fuel usage for a standard Euro 5 diesel vehicle with an engine capacity of less than
1.41 compared to congested scenarios. Moreover, Zhang et al. [28] reported a substantial
difference in fuel consumption, exceeding 20%, between ecological and aggressive driving
behaviors. Nevertheless, it is noteworthy that a gap still persists in existing studies that
directly integrates driving behaviors into CO2 emission estimation.
To tackle the urgent environmental concerns posed by increasing CO2 emissions fromvehicles, this paper introduces a novel methodology aimed at estimating monthly fuelconsumption (Vfuel ) for individual vehicles by leveraging detailed driving behavior data

Energies 2024, 17, 1410

3 of 16

provided by Aioi Nissay Dowa Insurance Co., Ltd. (Tokyo, Japan), which encompasses
counts of risky driving actions, such as speeding, sudden acceleration, and sudden braking,across various road types, grounded in machine learning regression techniques. The
monthly CO2 emissions (ECO2 ) are then calculated as a secondary parameter based on theestimated fuel consumption, assuming a linear relationship between the two.
The significance of this work lies in how it leverages data on dangerous driving behaviors to enhance the accuracy of fuel consumption estimates. Unlike previous studies,
which primarily focus on vehicle technical specifications and average driving patterns,
our research offers a more nuanced understanding of the relationship between driving
behaviors and fuel consumption. By providing a detailed analysis that connects individual
driving actions to fuel consumption, this study not only enriches the existing body ofknowledge on fuel consumption estimation models but also introduces a practical tool for
drivers to evaluate and improve their driving habits towards more fuel-efficient practices.
The CO2 emissions are then estimated based on the fuel consumption, assuming a linear
relationship between the two. It is important to note that the ECO2 values were not directly
verified by measurements, but rather estimated based on this assumed relationship. Moreover, the high prediction accuracy of the proposed fuel consumption estimation modeland the associated CO2 emission model holds promise for integration with initiatives
like the J-Credit Scheme, potentially serving as a catalyst for the widespread adoption of
fuel-efficient driving behaviors through targeted incentives. By aligning individual drivers’interests with broader environmental objectives, our approach offers a novel pathway tomitigate vehicular fuel consumption and the associated CO2 emissions. The remainder ofthis paper is organized as follows: Section 2 details the methodology of this study. Section 3
shows the overall structure of the estimation models for Vfuel and ECO2 , Section 4 discusses
the performance and implications of the prediction results. Finally, Section 5 concludes thepaper with a summary of our contributions and suggestions for future research.
2. Methodology
In this study, we investigate the potential of driving behavior data as features for
estimating ECO2 . Then, multiple feature sets are defined to evaluate the proposed driving
characteristics. Subsequently, dimensionality reduction algorithms and correlation analysis are employed on the driving characteristics for data visualization. Lastly, machinelearning regressions are conducted to evaluate the performance of each feature set inestimating ECO2 .
2.1. Novel Driving Behavior Data
We base our investigation on 228,281 monthly driving behavior data instances frommedium-sized TOYOTA vehicles (Toyota: Toyota City, Japan), provided by Aioi Nissay
Dowa Insurance Co., Ltd. Table 1 details the statistics of the categories (model, vehicle
name, and engine type) of the vehicles. For confidentiality reasons and for the purposesof both dimensionality reduction and machine learning analysis, the actual names of themodel, vehicle name, and engine type are encoded into numerical labels 1, 2, 3, . . . , asillustrated in Table 1. For the engine type, the numerical label 1 indicates a gasoline type,while 2 indicates a hybrid vehicle type. Notably, all hybrid vehicles considered in this study
are categorized as hybrid electric vehicles (HEVs), which do not support external charging.
Both the gasoline vehicles and HEVs utilize gasoline. Within these HEVs, batteries recharge
through regenerative braking and the internal combustion engine, with the electric motor
working in tandem with the gasoline engine. To conduct a reliable and generalizable
analysis, numerous vehicle categories (26 combinations of model, vehicle name, and enginetype) were taken into account, as shown in Table 1.

Energies 2024, 17, 1410

4 of 16

Table 1. Statistics of the vehicle categories (model, vehicle name, and engine type) and the corresponding numerical labels with the percentage share of each category. Engine Type 1 represents
gasoline vehicles, while Engine Type 2 represents hybrid vehicles.
Category

Model

VehicleName

Engine TypeCount

Percentage
(%)

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

1
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
23
24

8
9
5
2
4
16
3
14
18
4
18
19
18
19
19
7
16
6
13
11
17
12
13
10
15
1

1
1
1
2
2
2
2
2
1
1
1
1
2
2
2
2
1
2
1
2
2
2
2
2
2
2

16,101
3218
3938
3262
13,045
7279
5061
8562
6383
12,4188532
6825
25,106
15,6324525
9943
12,487
8918
3962
5388
21,1964374
11,266
1804
1590
7466

7.05
1.41
1.73
1.43
5.72
3.19
2.22
3.75
2.80
5.44
3.74
2.99
11.00
6.85
1.98
4.36
5.47
3.91
1.74
2.36
9.29
1.92
4.94
0.79
0.70
3.27

228,281100.00

Total

Table 2 illustrates the novel driving behavior data proposed in this study for Vfuel .
The dataset includes the counts of dangerous behaviors of speeding, sudden accelerating,sudden braking, alongside driving distance [m] on the expressways, national highways,
and local roads, respectively, on a monthly basis, in addition to the monthly total driving
time [s] and monthly total driving distance [m] on all types of roads, which were recordedby the telematics on the vehicles. Moreover, the monthly fuel consumption [L] was recorded
using the internal measurement devices of the vehicles. The monthly CO2 emission ECO2
was then calculated as a secondary parameter based on the assumption that engines emit
2.3 kg of CO2 for every 1 L of gasoline consumed [29,30], and that this relationship holds
true for all vehicles under consideration. The ECO2 can be calculated using Equation (1). It
is important to note that the ECO2 values were not directly verified by measurements, butrather estimated based on the assumed linear relationship between fuel consumption andCO2 emissions.
ECO2 [kg] = Vfuel [L] × 2.3[kg/L](1)

The speeding is determined based on the average speed over a certain distance, withthresholds set according to the type of road (expressways, national highways, local roads).
On general roads, the average speed is typically measured from one traffic signal to the next.
On expressways and national highways, where there are no traffic signals, it is measured
over distances of about 2000 m. The speeding thresholds are set at 120 km/h for expressways and 80 km/h for national highways and local roads, respectively. Brief increases
in speed for overtaking are not considered as speeding. Speeding is only determined if
the average speed over a specific section exceeds the set thresholds. Sudden acceleratingEnergies 2024, 17, 1410

5 of 16and braking are defined as increasing or decreasing speed by 10 km/h or more within
1 s. However, accelerating and braking that occur in normal driving scenarios are notconsidered as sudden accelerating or sudden braking.
Table 2. Novel driving behavior data utilized for ECO2 in this study.
Driving Behavior Data (Monthly)
Total driving time [s]Total driving distance [m]Counts of dangerous speeding on expressways [times]
Counts of dangerous sudden accelerating on expressways [times]
Counts of dangerous sudden braking on expressways [times]
Driving distance on expressways [m]Counts of dangerous speeding on national highways [times]
Counts of dangerous sudden accelerating on national highways [times]
Counts of dangerous sudden braking on national highways [times]
Driving distance on national highways [m]Counts of dangerous speeding on local roads [times]
Counts of dangerous sudden accelerating on local roads [times]
Counts of dangerous sudden braking on local roads [times]
Driving distance on local roads [m]2.2. Multiple Feature Sets
2.2.1. Estimation for All Vehicle Categories
To analyze and compare the efficacy of various features for ECO2 estimation, multiple
feature sets were defined as follows. First, the monthly total driving time and total driving
distance were combined to form the Base features. Second, the driving distance on theexpressways, national highways, and local roads, respectively, were combined to form
the D features. Third, the dangerous driving behaviors, including the counts of speeding,sudden accelerating, and sudden braking on the expressways, national highways, and local
roads, respectively, were combined to form the B features. Fourth, the specification of thevehicles, including the model, vehicle name, and engine type were combined to form theS features. Finally, 8 feature sets were defined in total by different combinations of the D,
B, and S feature sets to the Base feature set: Base, BaseD, BaseB, BaseS, BaseDB, BaseDS,
BaseBS, BaseDBS, as shown below.
•
•
•
•
•
•
•
•
•
•
•

Base features (Base feature set) consisted of the total driving time and total driving distance.
D features consisted of driving distance on expressways, national highways, andlocal roads.
B features consisted of dangerous driving behavior data on expressways, national
highways, and local roads.
S features consisted of specification of the vehicles (model, vehicle name, and engine type).
BaseD feature set consisted of Base and D features.
BaseB feature set consisted of Base and B features.
BaseS feature set consisted of Base and S features
BaseDB feature set consisted of Base, D, and B features
BaseDS feature set consisted of Base, D, and S features.
BaseBS feature set consisted of Base, B, and S features.
BaseDBS feature set consisted of Base, D, B, and S features.

2.2.2. Estimation for Each Vehicle Category
To verify the efficacy and generalizability of the ECO2 estimation model that mixes datafrom all vehicle categories together, individual ECO2 estimation models for each vehicle
category (model, vehicle name, and engine type) were defined separately using all thedriving behavior features (BaseDB) listed in Table 1.

Energies 2024, 17, 1410

6 of 162.3. Dimensionality Reduction for Data Visualization
In order to elucidate underlying patterns in the high-dimensional dataset and simultaneously gain insights into the significance of individual features, we employed dimensionality reduction techniques to the complete feature set (BaseDBS). This approach not only
allows for clear visualization of the intrinsic structure of the data in a lower-dimensional
space to highlight clusters, outliers, and trends, but also provides insights into feature
contributions. By analyzing the principal components, as in principal component analysis(PCA), or embedding vectors in methods like t-Distributed Stochastic Neighbor Embedding(t-SNE) [31] or Uniform Manifold Approximation and Projection (UMAP) [32], we candiscern which features are most influential in shaping the observed patterns, deepening ourcomprehension of data relationships. The PCA, a commonly used linear dimensionality
reduction technique, offers valuable insights for data visualization. However, its linear
nature limits its ability to capture non-linear data relationships. Conversely, t-SNE, anotherpopular choice for dimensionality reduction and visualization, excels in handling nonlinearities. Additionally, we employed UMAP, a versatile manifold learning method adept
at non-linear dimensionality reduction, which is also more computationally efficient thant-SNE. While both t-SNE and UMAP are non-linear, preserving local or high-dimensional
data structures, t-SNE’s computational complexity stands at O(n2 ), making it suboptimal
for larger datasets. UMAP, with its approximate complexity of O(n), is considerably more
efficient for extensive datasets. For our study, PCA, t-SNE, and UMAP were applied to the
BaseDBS feature set to condense the data dimensions from 19 to 2. Implementations of PCA
and t-SNE were sourced from the Python Scikit-learn package [33,34], while UMAP was
executed using its dedicated Python package [35]. We retained default hyper-parameters
for all the three dimensionality reduction algorithms (PCA: ‘n_components = 2’; t-SNE:‘learning_rate = auto’ and ‘perplexity = 30.0’; UMAP: ‘n_neighbors = 15’, ‘min_dist = 0.1’,
and ‘n_components = 2’).
3. Regression Models
An MLR model and a random forest regression (RFR) model were conducted toevaluate the validity of each feature set defined in Section 2.2 for estimating the ECO2 .
Prior to fitting the MLR and RFR models, the features were normalized to ensure thateach feature contributes proportionately to the result and used as explanatory variables.
Concurrently, the ECO2 served as the target variable. We used 80% of the total data points
as the training set and the remaining 20% as the testing set after shuffling. The MLR andRFR models were implemented using the Python Scikit-learn package. The MLR model
assumes a linear relationship between the target and explanatory variables while the RFR
model can account for non-linearities. A 5-fold cross-validation was conducted to the RFR
model by utilizing a grid search technique using the training set data to select the optimal
hyper-parameters of the RFR model, as illustrated in Figure 1. In the RFR model, thehyper-parameter candidates were selected empirically as follows: ’n_estimators’: [200, 600,
1000]; ’max_features’: [’auto’, ’sqrt’]; ’max_depth’: [10, 20, 30]. To gauge the performance ofthe model, the coefficient of determination (R2 ), root mean squared error (RMSE), and mean
absolute error (MAE) were used as the performance metrics. The calculation equations of
the R2 , RMSE, and MAE are shown in Equations (2)–(4). In Equations (2)–(4), y represents
the target variable of the regression, ŷ represents the predicted value of y, n stands for the
total number of data points, and ȳ is the average value of y spanning the n data points.
∑in=1 (yi − ŷi )2∑in=1 (yi − ȳ)2r∑in=1 (yi − ŷi )2
RMSE(y, ŷ) =n
R2 (y, ŷ) = 1 −MAE(y, ŷ) =∑in=1 |yi − ŷi |
n

(2)
(3)
(4)

Energies 2024, 17, 1410

7 of 16

Data
Train

Test

Validation

Train

Train

Train

Train

Test

Train

Validation

Train

Train

Train

Test

Train

Train

Validation

Train

Train

Test

Train

Train

Train

Validation

Train

Test

Train

Train

Train

Train

Validation

Test

Figure 1. Training, validation, and testing sets’ separation.

4. Results and Discussion
4.1. Data Visualization
Figure 2 depicts data visualizations derived from the PCA, t-SNE, and UMAP dimensionality reduction algorithms, utilizing the complete feature set (BaseDBS). The dimensionreduced data were color-coded using each feature separately for enhanced visualization,
as shown in Figure 2. As can be observed in Figure 2, it is evident that the clusters in the
PCA visualization are smaller than those in the t-SNE and UMAP visualizations, whichmakes it challenging to provide a detailed visualization. In Figure 2, UMAP gives a better
visualization than t-SNE in terms of clarity. While t-SNE primarily aims to preserve the local
structure of the data, sometimes at the expense of global structure, UMAP seeks to maintain a balance between local and global structures, often resulting in a more interpretableembedding. The distinction among different vehicle categories is markedly pronounced in
the UMAP visualization presented in Figure 2, particularly when the data are color-codedby the model, vehicle name, and engine type. These categories exhibit distinct and clear
borders in the embedding, indicating a significant influence of these features on the data
structure. This clarity in demarcation underscores the critical role of the model, vehicle
name, and engine type in differentiating driving characteristics across vehicle categories.
Notably, this observation aligns with the findings from our RFR model, where the model,
vehicle name, and engine type emerged as the top three features in terms of feature importance. This alignment reinforces the validity of our dimensionality reduction analysis andprovides empirical support for the significant impact these features have on the ability of
the RFR model to predict CO2 emissions accurately. The precise delineation of clusters bythese features in the UMAP visualization not only corroborates their importance but also
highlights the efficacy of the UMAP in capturing the nuanced distinctions among vehicle
categories, thereby offering valuable insights into the underlying structure of the data.
Figure 3 shows the boxplots of the driving behavior data and the ECO2 of all data points,
offering a visual representation of distribution and variability. Each boxplot delineates
the interquartile range (IQR), highlighting the middle 50% of the data, with the lower andupper bounds of the box corresponding to the first and third quartiles, respectively. Inside
the box, a horizontal line marks the median. The two bars above and below the box are theupper and lower whiskers, which extend from the first and third quartiles to the highest
and lowest data points within 1.5 times the IQR, indicating the spread of the bulk of thedata and highlighting outliers beyond this range. This visualization reveals a pattern ofgenerally low-frequency dangerous driving behaviors among most users, punctuated bya small number of significant outliers. This observation prompts a deeper analysis of theoutliers, suggesting potential areas for targeted interventions to mitigate high-risk driving
behaviors. Furthermore, the clear demarcation between typical and outlier behaviors aidsin understanding the relationship between driving habits and CO2 emissions, underscoring
the importance of addressing dangerous driving behaviors to enhance ECO2 estimation.

Energies 2024, 17, 1410

8 of 1650050

0.5 0.0 0.5

Vehicle Name

0.5
0.0
0.5

50
0
50

0.5 0.0 0.5

Engine Type

0.5
0.0
0.5

50
0
50

0.5 0.0 0.5

Monthly Driving Time [sec]

0.5
0.0
0.5

0.5 0.0 0.5

50
0
50

Monthly Driving Distance [m]

0.5
0.0
0.5

50
0
50

0.5 0.0 0.5

20
50

0

50

0

50
0
50

0.5 0.0 0.5

50
0
50

0.5 0.0 0.5

50

0

50

0

50
0
50

0.5 0.0 0.5

0
50

0

50

0

50
0
50

0.5 0.0 0.5

0
50

0

50

0

50
0
50

0.5 0.0 0.5

50

0

50

0

500

0
0

50

0

50
0
50

0.5 0.0 0.5

0

50

0

50
0
50

0.5 0.0 0.5

0

50

0

50
0
50

0.5 0.0 0.5

0

50

0

50
0
50

0.5 0.0 0.5

0

50

0

50
0
50

0.5 0.0 0.5

0

50

50
0
50

0.5 0.0 0.5

0

50

PCA 1

20

2

0
0

50

0

20

20
0

50

0

20

0

20

20
0

50

0
0

50

50

0

t-SNE 1

50

0
500

20
50

0

250

0
50

0

200

0
50

0

1e6

20
50

0
500

0

0

UMAP 2PCA 2t-SNE 2
0.5 0.0 0.5

50
0
50

20

0

Local Road: Driving Distance [m]

0.5
0.0
0.5

0

20
50

0

250

0

Local Road: Number of Dangerous Behaviors - Braking

0.5
0.0
0.5

20

20
50

0.0

1000

0

Local Road: Number of Dangerous Behaviors - Acceleration

0.5
0.0
0.5

20

20
50

0

2.5

0

Local Road: Number of Dangerous Behaviors - Speed

0.5
0.0
0.5

20
1e6

20
50

0

50

0

National Highway: Driving Distance [m]

0.5
0.0
0.5

20

20
50

0

50

0

National Highway: Number of Dangerous Behaviors - Braking

0.5
0.0
0.5

20

20
50

0

20

20
50

1

1.0
0.5

0

National Highway: Number of Dangerous Behaviors - Acceleration

0.5
0.0
0.5

20

2.0
1.5
1.0

1e7

20

National Highway: Number of Dangerous Behaviors - Speed

0.5
0.0
0.5

20
1e6

20

Expressway: Driving Distance [m]

0.5
0.0
0.5

20

20

Expressway: Number of Dangerous Behaviors - Braking

0.5
0.0
0.5

15
10
5

0

Expressway: Number of Dangerous Behaviors - Acceleration

0.5
0.0
0.5

20

20

Expressway: Number of Dangerous Behaviors - Speed

0.5
0.0
0.5

20
10

0

Colorbar Legend Colorbar Legend Colorbar Legend
Colorbar Legend Colorbar Legend Colorbar Legend
Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend Colorbar Legend
Colorbar Legend
Colorbar Legend

Model
0.5
0.0
0.5

20

0

1e6

20

1

0
0

UMAP 120

0

Figure 2. Data visualizations using PCA, t-SNE, and UMAP dimensionality reduction algorithms for
all data points.

Energies 2024, 17, 1410

9 of 16

1.2 1e6

1e7

ValuesValues

0.8
0.6
0.4

700
600

1.0

500

0.8

400

Values

1.0

1.2

0.6

300

0.4

200

0.2

0.2

100

0.0

0.0

0

Monthly Driving Time [sec]Monthly Driving Distance [m]Expressway: Number of Dangerous Behaviors - Speed

1e6
80

4

60

40

40
20

1

0

0

0
Expressway: Number of Dangerous Behaviors - Braking

National Highway: Number of Dangerous Behaviors - Speed

400
350
300
250
200
150
100
50
0

Expressway: Driving Distance [m]500400ValuesValues

2000
1750
1500
1250
1000
750
500
250
0

200

0

National Highway: Number of Dangerous Behaviors - Acceleration

National Highway: Number of Dangerous Behaviors - Braking
500

3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

250

400

200
150

ValuesValuesValues

300

100

1e6

100

0

0
Local Road: Number of Dangerous Behaviors - Speed

Local Road: Number of Dangerous Behaviors - Acceleration
1750

1.75

1500

1.50

300

1.00

100
0

1250
Values

1.25
Values

400

200

200
100

1e6
500

300

50

National Highway: Driving Distance [m]Values2

20

Expressway: Number of Dangerous Behaviors - Acceleration

Values3
Values60

ValuesValues

80

0.75

1000
750

0.50

500

0.25

250

0.00
Local Road: Number of Dangerous Behaviors - Braking

0
Local Road: Driving Distance [m]Monthly CO2 Emission [kg]Figure 3. Boxplots of the driving behavior data and the ECO2 for all data points.

4.2. Correlation Analysis
Figure 4 shows the heatmap of correlations among the driving behavior data and theECO2 . As is shown in Figure 4, the ECO2 highly correlates with the total driving time andtotal driving distance, and moderately correlates with the driving distance on the three
types of roads, while it weakly correlates with the dangerous driving behaviors on the three
types of roads, implying the validity of the proposed novel driving behavior data for ECO2 .

Energies 2024, 17, 1410

10 of 16

0RQWKO\'ULYLQJ7LPH>VHF@

0RQWKO\'ULYLQJ'LVWDQFH>P@([SUHVVZD\1XPEHURI'DQJHURXV%HKDYLRUV6SHHG
([SUHVVZD\1XPEHURI'DQJHURXV%HKDYLRUV$FFHOHUDWLRQ
([SUHVVZD\1XPEHURI'DQJHURXV%HKDYLRUV%UDNLQJ
([SUHVVZD\'ULYLQJ'LVWDQFH>P@
1DWLRQDO+LJKZD\1XPEHURI'DQJHURXV%HKDYLRUV6SHHG
1DWLRQDO+LJKZD\1XPEHURI'DQJHURXV%HKDYLRUV$FFHOHUDWLRQ
1DWLRQDO+LJKZD\1XPEHURI'DQJHURXV%HKDYLRUV%UDNLQJ
1DWLRQDO+LJKZD\'ULYLQJ'LVWDQFH>P@
/RFDO5RDG1XPEHURI'DQJHURXV%HKDYLRUV6SHHG/RFDO5RDG1XPEHURI'DQJHURXV%HKDYLRUV$FFHOHUDWLRQ
/RFDO5RDG1XPEHURI'DQJHURXV%HKDYLRUV%UDNLQJ
/RFDO5RDG'ULYLQJ'LVWDQFH>P@ 



  
   



    
     



      
       



        
         
          





           
            



             ([([ SUHV
SUHV VZD
0R
VZ
\
QWK
D\ 1X1P
0R O\'([ULYXP EHU
Q
W
SU
KO
LQ
R
EH
HV
UR I'D \'U J7L
VZ
PH
LYLI'
QJ
D\
QJ
>VHUDQ1
'
RX
HF
JH
XP
1D
L
V
V
U

W
RX
%H
DQ @EH
1D WLRQ
V%
FH
KD
UR
DO
WLR
>PYI'
H
+
QD
@
DQ KDY LRUVO+ LJKZ
LRUJH
LJK
D\V 6SH
U
R
(
ZD
1
XV
$
1D
[S
H
G
FF

\
X
UH
WLR
HOHVV %HK
QD 1X PEH
ZD
D
UD
P
U
O+
WLR
\ YLRU
LJK EHU RI'Q
V
'U
RIDQ
ZDLYL'J
\
QJ %UDN
1X DQJ HURX
'
L
Q
HU
PE
RX V%H LVWDQ J
HUV%
F
K
/RRI
HK DYLR H>P
F

'
D
/R
DY
UV
@
D
O
1

5
Q
FD
L
D

J
R
R

W
6
H
U
LRQ
O5
D
V
UR
SH
RD G1
DO
XV
$
H
G
FF
G+LXP
HOHJK %HK
/R 1XP EHU
ZD
D
UD

FD
WLR
\ YLRU
O5 EHU RI'Q
V
'U
RIDQ
RDLYL'J
G
QJ %UDN
1X DQJ HURX
'
L
Q
H
J
LV
V
PE
UR
XV %HK WDQF
HU
%
RI
HK DYLR H>P
'
D
U
@
DQ
JH YLRU V6
VSH
/R URXV
$
H
G
F
FD

FH
O5 %HK
OHUDRDDWLG YLRU
RQ
V
'U



LYL
0R
%UQ
J' DNL
QWKQJ
O\
L
&2 VWDQ( FH
>P
PL
@
VV
LRQ
>NJ@

0RQWKO\&2(PLVVLRQ>NJ@

Figure 4. Correlation heatmap of the driving behavior data and the ECO2 .

4.3. Machine Learning Regression Results
Table 3 presents the ECO2 prediction performance metrics for each vehicle category
separately. The performance metrics for estimating ECO2 , using the eight feature sets
defined in Section 2.2.1, are shown in Table 4. Figure 5 shows the observations-versuspredictions plots of ECO2 predictions using the BaseDBS feature set by MLR and RFR
models. As observed from Tables 3 and 4, the prediction accuracy using the BaseDBS
feature set for all vehicle categories combined is even higher than the average prediction
accuracy for each vehicle category when considered separately. This suggests the validityand feasibility of the method that combines all vehicle categories to create a generalizable
model. The decision to evaluate all the eight feature sets on a generalized estimation model,
rather than on individual vehicle models, is underpinned by the universally applicable
relationship between driving behaviors and engine efficiency [20,21] and CO2 emissions,as discussed in Section 1.
Observing Table 4, significant results can be summarized as follows:
1.
2.
3.
4.
5.

The BaseD, BaseB, BaseS feature sets outperformed the Base feature set.
The BaseDB feature set outperformed both the BaseD and BaseB feature sets.
The BaseDS feature set outperformed both the BaseD and BaseS feature sets.
The BaseBS feature set outperformed both the BaseB and BaseS feature sets.
The BaseDBS feature set outperformed all the other feature sets.

Therefore, it has been confirmed that the proposed driving behavior features are
relevant for ECO2 modeling. Notably, the BaseDBS feature set yielded the highest prediction accuracy for both MLR and RFR models, with R2 values reaching 0.842 and 0.975,

Energies 2024, 17, 1410

11 of 16

respectively. On the other hand, Figure 2 shows that, although no notable differences areobserved when a single driving behavior feature is used for color-coding the embedding,combining these features together yields high performance for ECO2 estimation, indicating
the non-linearities of the data. Moreover, the RFR model outperformed the MLR modelacross all feature sets, as the RFR model can account for non-linearities, as indicated inTable 4 and Figure 5. The error distributions, illustrated in Figure 6, show a prominent peak
centered around zero, indicating that most predictions closely match observed values. The
errors appear to follow a normal distribution, with fewer large deviations, suggesting thatthe models are well-calibrated and reliable.
Table 3. The ECO2 prediction performance of the MLR and RFR models using the BaseDB feature set
for each vehicle category separately.
Category

MLR

RFR

R2

RMSE

MAE

R2

RMSE

MAE

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26

0.967
0.961
0.976
0.973
0.946
0.970
0.946
0.972
0.967
0.967
0.948
0.947
0.924
0.943
0.946
0.943
0.971
0.978
0.980
0.915
0.940
0.944
0.954
0.935
0.909
0.962

23.39424.45817.357
11.23318.99612.23816.32311.80810.34218.24612.36416.26912.65812.37013.702
10.68915.6968.598
12.78917.88913.10013.121
12.76019.74720.356
11.47215.97715.87511.315
7.838
12.069
8.297
7.842
7.821
6.800
12.771
6.423
7.706
6.803
7.078
9.927
6.835
9.266
5.670
9.036
8.531
8.406
9.734
8.532
14.49914.581
8.166

0.975
0.957
0.973
0.972
0.963
0.963
0.935
0.969
0.967
0.969
0.967
0.954
0.962
0.961
0.941
0.951
0.979
0.977
0.979
0.964
0.965
0.947
0.958
0.912
0.906
0.954

20.37825.75118.448
11.46615.69613.52617.92212.38710.42617.6299.815
15.126
8.949
10.25214.3189.995
13.2788.967
13.067
11.64210.05512.82512.20222.97220.67812.65814.25517.176
11.826
7.665
10.162
8.725
8.191
7.485
6.671
11.880
6.259
7.800
5.443
6.799
9.660
6.560
8.673
5.638
8.911
8.001
6.582
8.308
8.235
15.18914.283
8.254

Average

0.953

14.922

9.531

0.958

14.247

9.178

Table 4. The ECO2 prediction performance of the MLR and RFR models using the proposed eightfeature sets for all vehicle categories combined.
Feature Sets
Base
BaseD
BaseB
BaseS
BaseDB
BaseDS
BaseBS
BaseDBS

MLR

RF

R2

RMSE

MAE

R2

RMSE

MAE

0.632
0.654
0.647
0.826
0.660
0.839
0.835
0.842

50.93349.41849.87934.99448.97233.74134.13933.38733.87533.09033.39623.49832.85122.71322.99222.456

0.642
0.676
0.686
0.965
0.703
0.972
0.972
0.975

50.23247.83647.03415.67145.771
14.11613.93413.29333.45231.95631.4399.963
30.486
8.965
8.820
8.329

Energies 2024, 17, 1410

12 of 16

Identity line

1200

Prediction
Predicted monthly CO2 emission [kg]Predicted monthly CO2 emission [kg]Prediction

1000
800
600
400
200
00

200

Identity line

1200
1000
800
600
400
200
00

400 600 800 1000 1200
Actual monthly CO2 emission [kg]D

200

400 600 800 1000 1200
Actual monthly CO2 emission [kg]E

Figure 5. Observations versus predictions plots for ECO2 prediction using the BaseDBS feature set by
the MLR model (a) and the RFR model (b).

Frequency

20000
10000
0

600

400

200

0
200
400
Error in monthly CO2 emission [kg] prediction

600

800

D

Frequency

20000
10000
0

200

100
0
100
Error in monthly CO2 emission [kg] prediction200E

Figure 6. Error distributions for ECO2 prediction using the BaseDBS feature set by the MLR model(a) and the RFR model (b).

Figure 7 displays the impurity-based feature importances of the explanatory variables
for ECO2 estimation using the BaseDBS feature set in the RFR regression model. As shownin Figure 7, the S features (model, vehicle name, and engine type), and Base features (total
driving time and total driving distance) are ranked as the top five features in terms offeature importance. This aligns with the dimensionality reduction results mentioned in
Section 4.1. Subsequently, the B features (driving behavior data on expressways, national
highways, and local roads) are ranked 6th to 8th, 10th to 12th, and 14th to 16th, respectively.
This ranking is considered reasonable, as according to general knowledge, vehicle speeds
on expressways are typically higher than those on national highways, and speeds onnational highways are generally higher than those on local roads. Higher speeds resultin higher CO2 emission rates, thereby having a greater impact on the feature importanceranking. Furthermore, the B features are ranked even higher than the D features (driving
distances on expressways, national highways, and local roads), highlighting the validityof the proposed novel dangerous driving behavior features for ECO2 estimation. Thisfinding aligns with the RFR regression result, which showed that the BaseB feature set
outperformed the BaseD feature set, as illustrated in Table 4. Therefore, the overall ranking
is S features > Base features > B features > D features.

Energies 2024, 17, 1410

13 of 16

0.6
0.4
0.2
Local Road: Driving Distance [m]Local Road: Number of Dangerous Behaviors - Braking

Local Road: Number of Dangerous Behaviors - Acceleration

Local Road: Number of Dangerous Behaviors - Speed

National Highway: Driving Distance [m]National Highway: Number of Dangerous Behaviors - Braking

National Highway: Number of Dangerous Behaviors - Acceleration

National Highway: Number of Dangerous Behaviors - Speed

Expressway: Driving Distance [m]Expressway: Number of Dangerous Behaviors - Braking

Expressway: Number of Dangerous Behaviors - Acceleration

Expressway: Number of Dangerous Behaviors - Speed

Monthly Driving Distance [m]Monthly Driving Time [sec]Engine Type

Vehicle Name

Model

0.0

Figure 7. Feature importances for ECO2 prediction using the BaseDBS feature set across all
vehicle categories.

5. Conclusions and Future Work
This study has developed a novel approach for estimating monthly CO2 emissions
from individual gasoline vehicles and HEVs (both utilize gasoline) using regression algorithms, incorporating unprecedentedly detailed driving behavior data across diverse
categories of TOYOTA cars. Our analysis revealed that the driving behavior data proposedin this study; specifically, the Base features (total driving time and driving distance), D
features (driving distances on expressways, national highways, and local roads), B features (dangerous driving behavior data, including counts of sudden acceleration, sudden
braking, and speeding on expressways, national highways, and local roads), and S features (specification of the vehicles, including model, vehicle name, and engine type) all
have a positive influence on monthly CO2 emission estimations. The overall ranking of
the proposed features is as follows: S features > Base features > B features > D features.
Utilizing a comprehensive feature set (BaseDBS), the RFR model achieved the highest
prediction accuracy, with R2 , RMSE, and MAE values of 0.975, 13.293 kg, and 8.329 kg,
respectively, for predicting monthly CO2 emissions. These results not only underscore thecritical relevance of all examined driving behaviors to CO2 emissions, but also highlightthe superior predictive accuracy and generalizability across vehicle categories.
In future work, instead of encoding specific models and vehicle names into integer
numerical labels (e.g., 1, 2, 3,. . . ) for inclusion in the regression models, it might bebeneficial to incorporate general physical quantities such as the frontal projection area
and the weight of the vehicles. This approach could enhance the generalizability of CO2
emission estimation models to unknown models and vehicle names. Additionally, byintegrating the CO2 emission estimation model with incentive-based programs like the
J-Credit Scheme or incorporating it into driver feedback systems, there is potential tosignificantly influence driving behaviors towards more energy-efficient practices, thereby
contributing to broader environmental sustainability goals. It is important to note that
the CO2 emissions in this study were not directly verified by measurements, but rather
estimated based on the assumed linear relationship with fuel consumption. Future work
could involve directly measuring CO2 emissions to validate and refine this relationship.
Furthermore, consideration will also be extended to diesel vehicles, addressing the distinct
characteristics and environmental impact of diesel fuel usage in addition to petrol vehicles,

Energies 2024, 17, 1410

14 of 16

to encompass a more comprehensive analysis of CO2 emissions across different fuel types.
In conclusion, the comprehensive approach to CO2 emission estimation presented inthis study offers a robust foundation for both advancing scientific understanding anddeveloping practical solutions to the pressing challenge of vehicular emissions.
Author Contributions: Conceptualization, S.N.; methodology, Z.W.; software, Z.W.; validation,
M.M.; formal analysis, Z.W.; investigation, Z.W. and M.M; resources, S.N.; data curation, M.M.;
writing—original draft preparation, Z.W.; writing—review and editing, Z.W. and M.M.; visualization,
Z.W.; supervision, R.M.; project administration, R.M.; funding acquisition, S.N. All authors have readand agreed to the published version of the manuscript.
Funding: This research was funded by Aioi Nissay Dowa Insurance Co., Ltd.
Data Availability Statement: Data are contained within the article.
Conflicts of Interest: Author Shoma Nishimura was employed by the company Aioi Nissay Dowa
Insurance Co., Ltd. The remaining authors declare that the research was conducted in the absence ofany commercial or financial relationships that could be construed as a potential conflict of interest.
This work was sponsored in part by Aioi Nissay Dowa Insurance Co., Ltd. The funding sponsor
provided the telematics data (driving behavior data) essential for our research and proposed ahypothesis on the correlation between individual driving behavior data and monthly individualvehicular CO2 emissions. While the sponsor facilitated the collection of data and offered a hypothesis
for investigation, they had no role in the design of the study beyond these contributions; in the analysis
or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations
IEA
EPA
CARB
RPM
HEV
Base
D
B
S
BaseD
BaseB
BaseS
BaseDB
BaseDS
BaseBS
BaseDBS
PCA
t-SNE
UMAP
MLR
RFR
R2
RMSE
MAE
IQR
Ddriving
Vfuel
ECO2
y
ŷ
n
ȳ
i

International Energy Agency
The U.S. Environmental Protection Agency
California Air Resources Board
Revolutions per minuteHybrid electric vehicle
Total driving time and driving distanceDriving distances on expressways, national highways, and local roads
Dangerous driving behavior data on expressways, national highways, and local roads
Specification of the vehicles (model, vehicle name, and engine type)
Base and D features
Base and B features
Base and S features
Base, D and B features
Base, D, and S features
Base, B, and S features
Base, D, B, and S features
Principal component analysis
t-distributed Stochastic Neighbor Embedding
Uniform Manifold Approximation and Projection
Multiple linear regression
Random forest regression
Coefficient of determination
Root mean squared error
Mean absolute error
Interquartile range
Monthly driving distance [m]Monthly fuel consumption [L]Monthly CO2 emission [kg]Target variable
Predicted target variable
Sequence length of the target variable
Average value of y spanning the n data points
i-th value in a variable sequence

Energies 2024, 17, 1410

15 of 16

References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.

18.
19.
20.

21.
22.
23.
24.
25.
26.
27.

28.
29.

European Commission. Road Transport: Reducing CO2 Emissions from Vehicles. 2020. Available online: https://climate.ec.
europa.eu/eu-action/transport-emissions/road-transport-reducing-co2-emissions-vehicles_en (accessed on 24 January 2024).
Wei, F.; Zhang, X.; Chu, J.; Yang, F.; Yuan, Z. Energy and environmental efficiency of China’s transportation sectors considering
CO2 emission uncertainty. Transp. Res. Part D Trans. Environ. 2021, 97, 102955. [CrossRef]
Eco-Mo Foundation. 2020 Transport and Environment in Japan. 2020. Available online: https://www.ecomo.or.jp/english/pdf/
tej2020.pdf (accessed on 21 January 2024).
International Energy Agency. Cars and Vans. Available online: https://www.iea.org/energy-system/transport/cars-and-vans(accessed on 29 January 2024).
Karczewski, M.; Chojnowski, J.; Szamrej, G. A Review of Low-CO2 Emission Fuels for a Dual-Fuel RCCI Engine. Energies 2021,
14, 5067. [CrossRef]
Dziubak, T.; Karczewski, M. Experimental Studies of the Effect of Air Filter Pressure Drop on the Composition and Emission
Changes of a Compression Ignition Internal Combustion Engine. Energies 2022, 15, 4815. [CrossRef]
Dziubak, T. Theoretical and Experimental Studies of Uneven Dust Suction from a Multi-Cyclone Settling Tank in a Two-Stage Air
Filter. Energies 2021, 14, 8396. [CrossRef]
Dziubak, T.; Karczewski, M. Experimental Study of the Effect of Air Filter Pressure Drop on Internal Combustion Engine
Performance. Energies 2022, 15, 3285. [CrossRef]
Tian, X.; Geng, Y.; Zhong, S.; Wilson, J.; Gao, C.; Chen, W.; Yu, Z.; Hao, H. A bibliometric analysis on trends and characters ofcarbon emissions from transport sector. Transp. Res. Part D Trans. Environ. 2018, 59, 1–10. [CrossRef]
Sperling, D.; Gordon, D. Two Billion Cars: Driving toward Sustainability; Oxford University Press: Oxford, UK, 2009.
Sterner, T. Fuel taxes: An important instrument for climate policy. Energy Policy 2007, 35, 3194–3202. [CrossRef]
Dargay, J.; Gately, D.; Sommer, M. Vehicle ownership and income growth, worldwide: 1960–2030. Energy J. 2007, 28, 143–170.
[CrossRef]
Ministry of Economy, Trade and Industry. J-Credit Scheme. 2022. Available online: https://japancredit.go.jp/english/(accessed on 1 June 2023).
United Nations Framework Convention on Climate Change. The Paris Agreement. Available online: https://unfccc.int/processand-meetings/the-paris-agreement (accessed on 16 December 2023).
Zhang, K.; Batterman, S. Near-road air pollutant concentrations of CO and PM2.5: A comparison of MOBILE6. 2/CALINE4 andgeneralized additive models. Atmos. Environ. 2010, 44 , 1740–1748. [CrossRef]
California. Air Resources Board. EMFAC7F; The Board: Sacramento, CA, USA, 1993.
Oduro, S.D.; Metia, S.; Duc, H.; Ha, Q.P. CO2 vehicular emission statistical analysis with instantaneous speed and acceleration
as predictor variables. In Proceedings of the 2013 International Conference on Control, Automation and Information Sciences(ICCAIS), Nha Trang, Vietnam, 25–28 November 2013; pp. 158–163.
Ahn, K.; Rakha, H.; Trani, A.; Van Aerde, M. Estimating vehicle fuel consumption and emissions based on instantaneous speed
and acceleration levels. Period. Polytech. Trans. Eng. 2002, 128 , 182–190. [CrossRef]
Maroju, R.; Nishimura, S.; Wang, Z.; Matsuhashi, R. Estimating Vehicular Fuel Consumption and CO2 Emissions by Machine
Learning Using Only Speed and Acceleration. J. Jpn. Soc. 2023, 44 , 30–38.
Wróblewski, P. An Innovative Approach to Data Analysis in The Field of Energy Consumption and Energy Conversion Efficiency
in Vehicle Drive Systems—The Impact of Operational and Utility Factors. In Proceedings of the 37th International BusinessInformation Management Association (IBIMA), Cordoba, Spain, 30–31 May 2021; pp. 1–2.
Saxena, S.; Phadke, A.; Gopal, A. Understanding the fuel savings potential from deploying hybrid cars in China. Appl. Energy
2014, 113, 1127–1133. [CrossRef]
Lois, D.; Wang, Y.; Boggio-Marzet, A.; Monzon, A. Multivariate analysis of fuel consumption related to eco-driving: Interactionof driving patterns and external factors. Transp. Res. Part D Trans. Environ. 2019, 72, 232–242. [CrossRef]
Jiménez, J.L.; Valido, J.; Molden, N. The drivers behind differences between official and actual vehicle efficiency and CO2
emissions. Transp. Res. Part D Trans. Environ. 2019, 67, 628–641. [CrossRef]
Mane, A.; Djordjevic, B.; Ghosh, B. A data-driven framework for incentivising fuel-efficient driving behaviour in heavy-duty
vehicles. Transp. Res. Part D Trans. Environ. 2021, 95, 102845. [CrossRef]
Grote, M.; Williams, I.; Preston, J.; Kemp, S. Including congestion effects in urban road traffic CO2 emissions modelling: Do Local
Government Authorities have the right options? Transp. Res. Part D Trans. Environ. 2016, 43, 95–106. [CrossRef]
Sharifi, F.; Birt, A.G.; Gu, C.; Shelton, J.; Farzaneh, R.; Zietsman, J.; Fraser, A.; Chester, M. Regional CO2 impact assessment ofroad infrastructure improvements. Transp. Res. Part D Trans. Environ. 2021, 90, 102638. [CrossRef]
Samaras, C.; Tsokolis, D.; Toffolo, S.; Magra, G.; Ntziachristos, L.; Samaras, Z. Improving fuel consumption and CO2 emissions
calculations in urban areas by coupling a dynamic micro traffic model with an instantaneous emissions model. Transp. Res. PartD Trans. Environ. 2018, 65, 772–783. [CrossRef]
Zhang, L.; Zhu, Z.; Zhang, Z.; Song, G.; Zhai, Z.; Yu, L. An improved method for evaluating eco-driving behavior based-on
speed-specific vehicle-specific power distributions. Transp. Res. Part D Trans. Environ. 2022, 113, 103476. [CrossRef]
Jahirul, M.I.; Masjuki, H.H.; Saidur, R.; Kalam, M.A.; Jayed, M.H.; Wazed, M.A. Comparative engine performance and emission
analysis of CNG and gasoline in a retrofitted car engine. Appl. Therm. Eng. 2010, 30, 2219–2226. [CrossRef]

Energies 2024, 17, 1410

30.
31.
32.
33.
34.
35.

16 of 16

Vitliemov, P.; Kolev, N.; Marinov, M. Economic evaluation of the implementation of policy actions in the field of energy efficiency.
Int. J. Energy Econ. Policy 2019, 9, 106–113. [CrossRef]
van der Maaten, L.; Hinton, G. Visualizing Data Using t-SNE. 2008. Available online: https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf?fbcl (accessed on 21 January 2024).
McInnes, L.; Healy, J.; Melville, J. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv 2018,arXiv:1802.03426. https://doi.org/10.48550/arXiv.1802.03426.
Scikit-Learn Developers. Sklearn.Decomposition.PCA. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html (accessed on 16 December 2023).
Scikit-Learn Developers. Sklearn.Manifold.TSNE. Available online: https://scikit-learn.org/stable/modules/generated/sklearn.
manifold.TSNE.html (accessed on 17 January 2024).
McInnes, L.; Healy. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction—Umap 0.5 Documentation. 2018. Available online: https://umap-learn.readthedocs.io/en/latest/index.html (accessed on 29 January 2024).

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual
author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury topeople or property resulting from any ideas, methods, instructions or products referred to in the content.