# What kind of cars do people drive? Including fuel type and emission standards in a car ownership model

Marjolaine Lannes, Nicolas Coulombel, Yelva Roustan

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Marjolaine Lannes, Nicolas Coulombel, Yelva Roustan. What kind of cars do people drive? Including fuel type and emission standards in a car ownership model. 2025. hal-05341257

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# What kind of cars do people drive? Including fuel type and emission standards in a car ownership model

Marjolaine Lannes, Nicolas Coulombel, Yelva Roustan

October 31, 2025

*This paper is the preprint version of the research article submitted to Travel Behaviour and Society. Submission date: October 3, 2025. It is currently under review.*

## Abstract

Car ownership models are essential for understanding travel behavior and informing transportation policy decisions. However, previous research on car ownership modeling has not addressed the determinants of car ownership related to pollutant emissions such as fuel type or emission standard. This study seeks to fill this gap by comparing the performance of several classification models in predicting the number of cars owned by households, their fuel type and their Euro norm (i.e. car age), while also investigating the significance of explanatory variables. These variables include socioeconomic characteristics, as well as mobility-related variables such as commuting distance, parking availability, and public transportation accessibility to the home and workplace. The methodology is applied to the Paris region. We find that logistic regression performs similarly to supervised learning models, even slightly outperforming them, except for car age estimation in which gradient boosting performs better. Our results show that income and commuting distance jointly have a preponderant explanatory effect on the type of car owned, particularly income for electric cars. This work paves the way for future research evaluating transportation policies related to household car ownership by allowing a deeper understanding of the determinants of the type of car owned by households and by providing the trained classifiers as open data.

**Keywords:** car ownership, fuel, Euro norm, supervised learning, electric vehicles
# 1 Introduction

As high levels of air pollutants are emitted in urban areas, causing deleterious effects on population health worldwide with 4.2 million deaths a year estimated to be linked to ambient air pollution (Fuller et al., 2022), many countries are introducing standards to regulate passenger car emissions. Within the European Union, Euro norms set maximum thresholds for pollutant emissions from cars, while an increasing number of cities concomitantly implement low emission zones in order to improve air quality, by limiting access to the city center to the most polluting cars (based on these Euro norms). In addition, transportation is the sector that emits the most greenhouse gases, accounting for almost a quarter of total emissions in the EU-27 in 2021, of which 75% were from road transport (European Environment Agency, 2024). As the EU is committed to reaching carbon neutrality by 2050, the Green New Deal aims to reach net zero CO₂ emissions for the upcoming cars in 2035 and thus promotes electric cars to achieve it. In this context, understanding the determinants of household car ownership decisions is crucial, particularly with regard to vehicle emissions of air pollutants and greenhouse gases. A better knowledge of which households own cars and the type of car they choose, conditional on their socioeconomic and mobility-related characteristics, would help decision makers better anticipate who might be affected by car regulation policies.

The development of the multinomial logit (MNL) model in the 1970s represented a major turning point in behavior choice modeling, including transportation models (Train, 1980). This discrete choice model (DCM) has then been broadly applied to the modeling of car ownership decisions (e.g. Purvis, 1994; Choo and Mokhtarian, 2004; Whelan, 2007). Discrete choice models have thus contributed notably to the development of disaggregated transportation choice models, such as the choice of travel mode (Ben-Akiva and Lerman, 1985) or the decision of car ownership (Jong et al., 2004), which can then be straightforwardly incorporated within agent-based transportation models. The first study applying discrete choice models to build a disaggregated car ownership model was written by Ben-Akiva and Lerman in 1975. Those models can estimate actual household car ownership decisions (e.g., Purvis, 1994; Potoglou and Kanaroglou, 2008b; Paredes et al., 2017), predict future car purchases from stated household preferences or the evolution of car ownership in future years (e.g. Whelan, 2007), or even estimate actual and predict future behaviors (e.g., Brownstone, Bunch, and Train, 2000; Dixon et al., 2021). For the last two decades, supervised learning and neural networks have been increasingly applied to modeling transportation choices. These methods provide data-driven alternatives to econometric and discrete choice models (DCM) to estimate transportation choices based on household characteristics, as attested in Cranenburgh et al. literature review (2021). Machine learning and discrete choice methods are both well established in the literature for their high estimation accuracy and provide insight into the effect of explanatory variables on the outcome. A flourishing literature comparing supervised learning and discrete choice methods for transportation choice modeling has consequently emerged for the prediction of future demand and the estimation of actual households' choices, concerning the modeling of car ownership, but also travel mode choice (e.g. Hagenauer and Helbich, 2017), vehicle size choice (e.g. Mohammadian and Miller, 2002), or even the adoption of autonomous cars (e.g., Lee et al., 2019). Household choices regarding vehicle ownership may concern the number of vehicles owned (e.g. Purvis, 1994; Paredes
et al., 2017) ; their size (e.g. Mohammadian and Miller, 2002) ; their type – such as cars and motorcycles (e.g., Zambang, Jiang, and Wahab, 2021), or cars, motorcycles, and taxis (e.g., Basu and Ferreira, 2020) - or their fuel type (e.g. Brownstone, Bunch, and Train, 2000) ; or even their purchase frequency (with a dynamic approach) or the age of the vehicle (with a static approach). Regarding car type choices, most studies focus on vehicle size. As we will see in the literature review that follows, we found no studies investigating the age and emission standards of cars and no recent study on fuel types, as the only one we found was based on 1993 data and did not investigate household characteristics determining this choice (Brownstone, Bunch, and Train, 2000). In addition, tools to model car type choices relatively to air pollutant emission standards are still lacking in agent-based transportation models, as the current state of the art is to model the car fleet using a single representative vehicle with an average emission factor based on the car fleet composition.

This study aims to investigate the factors that determine household car ownership decisions based on their socioeconomic and mobility-related characteristics with a broader concept of car choices which encompasses their type. It also set out to confirm the potential of supervised learning models to provide more accurate estimates of household car ownership choices. This includes three levels of decision: the number of cars owned by the household, their fuel types, and the cars' age (which determines their emission standard). These choices are here considered with a temporally static approach, which is appropriate to estimate car fleets for a given year. Our main contribution is to conduct this models comparison not only for the choice of car number but also for their fuel type and emission norms. The representation of car fuel and the Euro norm would thus allow for the computation of air pollutants' emissions. First, we compared fourteen different classifiers to investigate a wide range of supervised learning and discrete choice methods (multinomial logit, linear discriminant analysis, K-nearest neighbors, naive Bayes, ridge classifier, support vector machines, decision trees, random forest and gradient boosting) based on several performance indicators such as accuracy but also Matthews correlation coefficient (MCC), Cohen's kappa or F1-score, which are appropriate for multi-class problems with imbalanced training data. Then, we analyzed the importance of features for the best performing classifier of each of the three models. This work finally provides trained classifiers modeling three household car ownership choices (motorization, type of fuel, age of the car) for future research, as training algorithms and output classifiers are available in a public repository. These open access data are necessary inputs for fine-scale environmental assessment of road traffic impacts by characterizing cars and their owners.

The article is presented in line with the following plan. Section 2 reviews car ownership modeling literature applying discrete choice and supervised learning models. Section 3 outlines the materials and method, including the car ownership models, training dataset and evaluation metrics. We present the models’ performance comparison and model validation for each predicted variable in section 4. Finally, we discuss the challenges raised and perspectives for future work in section 5 and 6 and data accessibility is indicated in section 7.
## 2 Literature review

In recent years, many studies have compared discrete choice model (DCM) and machine learning (ML) for transportation choice modeling, notably for car ownership modeling. While most comparative studies of DCM and ML classifiers have focused on modeling transportation mode choice, fewer have examined car ownership choices, and none of them explored the fuel types and emission standards as S. Wang et al. highlighted in 2021. This review of the literature on comparative studies of discrete choice models and supervised learning classification in transportation modeling found two studies on car ownership out of 35 studies and 136 experiments. Among the existing literature on DCM-ML comparative studies to model transportation choices, some articles yet focus on car ownership models. For example, Paredes et al. (2017) compare the multinomial logit model with various supervised learning models, including decision tree family, support vector machine models, and gradient boosting classifier. Zambang, Jiang, and Wahab (2021) evaluate the performance of supervised learning models, namely logistic regression, stochastic gradient descent, support vector machine, decision tree, ensemble method, naive Bayes, K-nearest neighbors, or even Adaboost. Other authors compare supervised learning models to a neural network model, such as Kaewwichian, Tanwanichkul, and Pitaksringkarn (2019) and Dixon et al. (2021).

In terms of performance, Paredes et al. show that supervised learning models perform better than discrete choice models under the condition of engineering features. By comparing several supervised learning models, Zambang, Jiang, and Wahab obtained the best performance for the support vector model. Basu and Ferreira found a 10% higher accuracy score with neural networks than multinomial logit. Moreover, S. Wang et al. point out in a review of the literature on transportation choice models that machine learning models offer better performance than discrete choice models, which may not be adapted to wide learning samples, involving much longer computation times. They finally recommend implementing deep neural networks, model ensembles, and random forest classifiers, based on the accuracy score. However, the explainability of models remains an important challenge, particularly in terms of policy and practice (Cranenburgh et al., 2021). Transparency and interpretability are indeed essential for understanding individual behaviors and informing transport policies. In this context, discrete choice and supervised learning methods offer the advantage of greater interpretability and provide a clearer understanding of how specific features contribute to the prediction.

As for the choice of evaluation metrics, the vehicle ownership choice modeling studies considered above mainly adopt the accuracy score as an evaluation criterion for four of them (Paredes et al., 2017; Kaewwichian, Tanwanichkul, and Pitaksringkarn, 2019; Basu and Ferreira, 2020; Zambang, Jiang, and Wahab, 2021), and the last one refers to the mean absolute error (Dixon et al., 2021). The accuracy score appears to be relevant as a first measure as it evaluates the generalizability of the model, but it may still be worth considering if other evaluation metrics would be appropriate in our case, in particular when considering a multi-class classification problem. Some articles we previously quoted evaluate their models using the mean square error (MSE), the area under the ROC curve (AUC), or the Cohen-kappa statistic. Indeed, Kaewwichian, Tanwanichkul, and Pitaksringkarn (2019) complement the accuracy score by analyzing features'
significance, Basu and Ferreira (2020) by comparing it with the F-measure, mean absolute error and root-mean-square error; and Zambang, Jiang, and Wahab (2021) by comparing it with various metrics including the Cohen-kappa statistic and the area under the ROC curve. Besides, Cranenburgh et al. (2021) suggested in a discussion paper on machine learning for choice modeling that evaluation can be achieved by analyzing the significance of the parameters in relation to expected behavior. Metrics such as accuracy score, F measure, AUC or Cohen kappa, as well as feature significance, could thus be considered for the evaluation of car-type classifiers and the chosen metrics will be presented with the methodology (section 3.2).

Recent studies on the modeling of car ownership choices have identified explanatory vari-
ables not only related to household socioeconomic characteristics, but also the built environ-
ment in relation to local transport accessibility, regional accessibility of the city of residence,
as well as equipment variables. Some of them have suggested that variables associated with
built environments, which affect mobility capacity, influence vehicle ownership (Zegras, 2010;
Basu and Ferreira, 2020). Those variables describe the characteristics of the neighborhood and
their relative location, for example, the distance to the nearest metro station or to the central
business district. The works found in the literature review on car ownership modeling (Table
1) take into account the following explanatory variables: demographic variables (number of
residents, household composition) ; socioeconomic variables (disposable income, social classifi-
cation, tenure, employment status, number of chief executive officers, white collars, blue collars,
etc.) ; housing type ; urban environment variables representing local accessibility (density, bus
service, distance to the nearest bus station, distance to the nearest underground station, access
to services and jobs, or distance to the central business district) ; and regional accessibility vari-
ables (commuting time, commuting distance and means of travel to work). Multiple methods
can be applied to estimate the accessibility of residences at local or regional level and travel
behavior can be used to estimate public transport accessibility<fnref n="1" />. The literature review outlines
the importance of income effect, which is reflected not only in the household income range but
also in the housing type and property, the number of full-time workers, the number of seniors
or even the number of chief executive officers, and the distance to the nearest metro station
might indicate an accessibility effect. Paredes et al. (2017) thus find out that the most signif-
icant variables for car ownership modeling are the housing type, the ethnicity, the number of
full workers, the presence of a family member above 60 years old, the number of chief executive
officers, the household size, and the distance to nearest metro station ; whereas the distance
to central business district, the number of children, the number of students and the number of
white collars were not significant. The two other studies model vehicle ownership including mo-
torcycles and find out a strong significance respectively for average monthly income and age of
the respondents (Zambang, Jiang, and Wahab, 2021) and for the number of children, the number
of seniors, and noticed a positive influence of high income level simultaneously though income
and privately-owned housing (Basu and Ferreira, 2020). These studies analyze the influence of
the variables on the outputs of the model based on the coefficients of the logit model.

<footnote n="1">¹As an example, Kaewwichian, Tanwanichkul, and Pitaksringkarn study examined the free-flow travel time between zones within a tour and estimated the number of cars owned by households based on socioeconomic characteristics and travel behavior.</footnote>
<table><caption>Table 1: Decision types and approaches in car ownership modeling literature</caption><thead><tr><th rowspan="2">Reference</th><th colspan="5">Decision types</th><th colspan="2">Approach</th><th colspan="3">Model type</th></tr><tr><th>Vehicle ownership</th><th>Car ownership</th><th>Car size</th><th>Fuel type</th><th>Car age</th><th>Actual</th><th>Forecast</th><th>DCM</th><th>ML</th><th>NN</th></tr></thead><tbody><tr><td>Brownstone, Bunch, and Train (2000)</td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td>x</td><td></td><td></td><td></td></tr><tr><td>Mohammadian and Miller (2002)</td><td></td><td>x</td><td></td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td></tr><tr><td>Whelan (2007)</td><td></td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td>x</td><td></td><td>x</td></tr><tr><td>Potoglou and Kanaroglou (2008b)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td></td><td>x</td><td></td></tr><tr><td>Paredes et al. (2017)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>Kaewwichian, Tanwanichkul, and Pitaksringkarn (2019)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td></td></tr><tr><td>Basu and Ferreira (2020)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td>x</td></tr><tr><td>Dixon et al. (2021)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td>x</td><td>x</td><td>x</td><td>x</td></tr><tr><td>Zambang, Jiang, and Wahab (2021)</td><td></td><td>x</td><td></td><td></td><td></td><td>x</td><td></td><td>x</td><td>x</td><td>x</td></tr></tbody></table>

Note. DCM: discrete choice model, ML: machine learning, NN: neural network
Concerning the dependence between the choice of the number of cars and their type, Seyedabrishami et al. (2019) have shown that household decisions on whether to own vehicles, the number of vehicles owned, and fuel types are interdependent using a Franck Copula joint model for those three decisions, which are each represented by a multinomial logit model. In addition, multistage prediction methods are standard for vehicle type choice models and the household decision-making process can be described by a three-step decision tree, which is the number of vehicles, their class, and the vehicle model finally selected (Potoglou and Kanaroglou, 2008a). The state-of-the-art finally shows that those joint developments of vehicle types and motorization choice models are motivated by the agreement on the interdependence of those households choices (Potoglou and Kanaroglou, 2008a).

# 3 Material and methods

## 3.1 Car ownership modeling

A two-stage choice model was designed to represent three interdependent car ownership choices at the households' level: the number of cars owned, their fuel type and their age category which gives their emission standards (Figure 1). The first step is to determine whether a household owns no car, a single car, or several cars. From this information, the second step aims at estimating the type of these cars – i.e. their fuel type and their age category as a determinant of emission standard for the vehicle – knowing the number of cars held by the household. The estimation of car ownership choices is based on various household characteristics, including socioeconomic and mobility-related explanatory variables, as the literature review suggests. Given the article's focus on identifying and interpreting the determinants of car ownership, we used supervised learning models, which offer greater transparency and interpretability. We compared the performance of a multinomial logit model, generative models (linear discriminant analysis and naive Bayes), quadratic discriminant analysis, boosting methods (gradient boosting, Ada Boost, light gradient boosting machine), decision tree methods (decision tree, Extra Tree, Random Forest), support vector machine and a ridge classifier. A description of all classification models can be found in supplementary material.

<figure><img src="image_2.png" /><figcaption>Figure 1: Two-stage model of households car ownership decisions: (1) the number of cars owned and (2) their type</figcaption></figure>

## 3.2 Evaluation metrics

In this section, we present various evaluation metrics for supervised learning prediction which will be used in the following analysis, based on Grandini, Bagli, and Visani (2020). To evaluate the models, the sample is usually divided in two datasets: a training sample from
which we build the model and a test sample that allows us to evaluate it. If we consider a two-class model with 'positive' and 'negative' classes, let's denote TP the number of true positive predictions, FP the number of false positive, TN the number of true negative, FN the number of false negative. We will explain how to calculate evaluation metrics for a multi-class model from two-class models.

The accuracy score is the percentage of accurate predictions in the test sample. It is often used as a first evaluation metric as it is easy to interpret and is given by the statistics on the diagonal of the confusion matrix. For a two-class model, the accuracy is given by:

$$Acc = \frac{TP + TN}{TP + TN + FP + FN} \qquad (1)$$

The F1-score is the harmonic mean of the precision and recall, value in range [0,1], where the recall of a predictor is $\frac{TP}{TP+FN}$ and the precision is given by $\frac{TP}{TP+FP}$ in the 2-class case. In the multi-class case, the F1 score is the average of the F1 score of each class with a weighting parameter indicating if we use the micro or macro approach, which are both detailed in Grandini, Bagli, and Visani (2020). In the two-class case:

$$F1 = \frac{2}{\frac{1}{precision} + \frac{1}{recall}} = \frac{2TP}{2TP + FN + FP} \qquad (2)$$

Cohen's Kappa is a measure of agreement between observed and predicted or inferred classes for cases in a testing dataset, included in [-1,1]. To interpret its value, we consider that the evaluated model is better than a random classification if $\kappa$ is positive. Let's denote $c$ the number of elements correctly predicted, $s$ the total number of elements, $p_k$ the number of predictions of class k and $t_k$ the number of true predictions of class k.

$$\kappa = \frac{c \times s - \sum_{k=1}^{K} p_k t_k}{s^2 - \sum_{k=1}^{K} p_k \times t_k} \qquad (3)$$

Matthews correlation coefficient (MCC) is an evaluation metric that extends Pearson's correlation coefficient to multi-class classification problems with more than 2 classes. Delgado and Tibau (2019) have shown that MCC and Cohen kappa behave similarly when the confusion matrix is symmetrical, but they differ when the confusion matrix asymmetry increases. They recommend using MCC for classification assessment with unbalanced datasets. It is particularly used for multi-class models evaluation and values in range [-1,1]. To interpret it, we consider that values close to 1 indicate a strong positive correlation between predicted variables and the truth. In contrast, a null MCC means that our model performs no better than a random classification. A negative value reflects that the classifier tends to switch the labels, that is to say that there is a negative correlation between the prediction and the truth. The multi-class MCC can be defined, using the same notation as for Cohen kappa, for a K-class classification problem as follows.

$$MCC = \frac{c \times s - \sum_{k=1}^{K} p_k t_k}{\sqrt{(s^2 - \sum_{k=1}^{K} p_k^2)(s^2 - \sum_{k=1}^{K} t_k^2)}} \qquad (4)$$
## 3.3 Training data

### 3.3.1 Household travel survey

This study uses data from the regional household travel survey in Île-de-France, the Enquête Globale de Transport (EGT), which was carried out between 2018 and 2020 and stopped before the pandemic. The mobility agency surveyed 10,470 individuals belonging to 4,766 households about their mobility. The information about motorized equipment indicate that surveyed households own 4,862 cars, and only 6.8% of households owning a motorized vehicle did not own a car (e.g. a household owning only motorcycles). Respondents also provided information on their trips on a given day, weekday or weekend. The database is structured on four levels: households characteristics, individuals, their travel and their transport modes choices. This survey covers numerous transportation modes: public transport, two-wheel drive vehicles, car, taxi, plane, train, bike, walk and micromobility. We extracted our dataset from the "Households", "Individuals" and "Cars" tables and considered the household as the level of reference.

<figure><img src="image_3.png" /><figcaption>Figure 2: Real-world distribution of predicted variables: (a) households car ownership, (b) cars fuel type and (c) cars emission standard in Paris region (EGT 2018-2020).</figcaption></figure>
The advantages of this database are the large number of households it covers, the inclusion of the age of the car, and the detailed fuel type typology. However, some respondents did not want to answer questions about their income and housing type: data preparation is described next. Figure 2 provides summary statistics about households car ownership choices based on EGT. Two thirds of households are motorized, with an average of 0.9 cars per household. Besides, the ratio of petrol vs diesel is 45.0% vs 52.2% and a minority of households own a hybrid or electric car, as they respectively represent 2.0% and 0.6% of the car fleet. We thereby notice that the distribution of the number of cars owned by households and vehicle types, i.e. the input data on vehicle type, is imbalanced.

In light of the literature on the estimation of household car ownership choices with DCM and ML methods (Section 2), socioeconomic and mobility-related variables were selected. As expected, the survey provides numerous information on the socioeconomic situation of the households, notably on their income level or the number of employed actives in the household and also some information about the individuals such as their range of age and their city of work or city of study. It also includes the household type, but we do not have detailed information on the number of children, students, or seniors. The household is thus described as a single-parent family with a mother or father, a couple with or without children, or a single man or woman, and the age of household members is given. Type of dwelling – flat or house – is also given. Next, some variables are related to household mobility capacity, such as the type of car park at the household’s home, from which we get ownership or use of a private car park at home, or the presence of a car park at the workplace per individual – but also to the mobility constraints with the commuting distance. Lastly, the car table lists information about each car owned by the household, including their fuel type and year of initial entry into service, and the number of cars owned by the household, which we aim to estimate. Detailed information about the extracted variables and EGT is presented in Table 2).

<table><caption>Table 2: Structure of extracted data from "Enquête globale de Transport" mobility survey</caption><thead><tr><td>Variables</td><td>Description</td><td>Table</td></tr></thead><tbody><tr><td>REVENU</td><td>Household's income range</td><td>Household</td></tr><tr><td>MNPACT</td><td>Number of employed actives in the household</td><td>Household</td></tr><tr><td>NB_VD</td><td>Number of vehicles owned by the household</td><td>Household</td></tr><tr><td>TYPE_MEN</td><td>Type of household</td><td>Household</td></tr><tr><td>TYPELOG</td><td>Type of housing</td><td>Household</td></tr><tr><td>RESCOMM</td><td>City of residence</td><td>Household</td></tr><tr><td>TRAVINSEE</td><td>City of work</td><td>Individual</td></tr><tr><td>ETUDINSEE</td><td>City of studies</td><td>Individual</td></tr><tr><td>TRAGE</td><td>Age range</td><td>Individual</td></tr><tr><td>PKVPTRAV</td><td>Presence of parking at workplace</td><td>Individual</td></tr><tr><td>DDOMTRAV</td><td>Commuting distance</td><td>Individual</td></tr><tr><td>STAT</td><td>Type of car park at home</td><td>Car</td></tr><tr><td>ENERG</td><td>Fuel type of the vehicle</td><td>Car</td></tr><tr><td>APMC</td><td>Year of initial entry into service of the vehicle</td><td>Car</td></tr></tbody></table>
<table><caption>Table 3: Definition of variables</caption><thead><tr><th>Label</th><th>Description</th><th>Values</th><th>Type</th></tr></thead><tbody><tr><td colspan="4" style="text-align: center; font-style: italic;">Socioeconomic explanatory variables</td></tr><tr><td>Income</td><td>Household income</td><td>Logarithm of the mean of household's income range</td><td>Numeric</td></tr><tr><td>Age</td><td>Age range of the oldest individual of the household</td><td>15 to 24, 25 to 34, 35 to 54, 55 to 64, 65 to 74, 75+</td><td>Categorical</td></tr><tr><td>N_workers</td><td>Number of employed actives in the household</td><td>0, 1, 2+</td><td>Categorical</td></tr><tr><td>Household type</td><td>Type of household</td><td>Couple with child (CWC), couple without child (CWOC), single man (SM), single woman (SW), single-parent family mother (SPFM), single-parent family father (SPFF), others</td><td>Categorical</td></tr><tr><td>Housing type</td><td>Type of housing</td><td>Flat, house, others</td><td>Categorical</td></tr><tr><td colspan="4" style="text-align: center; font-style: italic;">Mobility-related explanatory variables</td></tr><tr><td>PT share home</td><td>Share of home city residents taking public transports to go to their workplace</td><td>-</td><td>Numeric</td></tr><tr><td>PT share work</td><td>Share of residents taking public transports to go to their workplace in the farthest working city of one of the household members</td><td>-</td><td>Numeric</td></tr><tr><td>Commuting distance</td><td>Maximum commuting distance within household</td><td>-</td><td>Numeric</td></tr><tr><td>Parking</td><td>Presence of a private parking at home</td><td>1 if there is a private parking, 0 otherwise</td><td>Categorical</td></tr><tr><td>Parking at workplace</td><td>At least one person in the household has parking at their workplace</td><td>1 if there is, 0 otherwise</td><td>Categorical</td></tr><tr><td colspan="4" style="text-align: center; font-style: italic;">Predicted variables</td></tr><tr><td>N_cars</td><td>Number of cars owned by the household</td><td>0, 1, 2+</td><td>Categorical</td></tr><tr><td>Fuel type</td><td>Fuel type</td><td>Diesel, petrol and others, plug-in hybrid, electric</td><td>Categorical</td></tr><tr><td>Euro norm</td><td>European emission standard</td><td>&lt; ECE, Euro 1, 2, 3, 4, 5, 6</td><td>Categorical</td></tr></tbody></table>

### 3.3.2 Definition and choice of explanatory variables

Explanatory and explained variables of the model are defined in Table 3 and their engineering description follows. The first step was to extract socioeconomic variables: the household type, income range, the number of employed actives and the housing type. The second step was to choose mobility-related variables that could be built from the database. These variables include the commuting distance of household members, the availability of parking at their workplace, and the type of parking at home. Furthermore, we do not have access to the precise location of the household in the survey, but only to its city. It is therefore not possible to include variables indicating local accessibility of the dwelling, such as the distance to the nearest metro or bus station or the distance to the central business district. The modal share of public transport
(PT) for commuting was therefore chosen as a variable indicating the regional accessibility of the household's city of residence and the workplace for the training dataset. By modal share of PT, we refer to the percentage of residents in a city who use public transportation primarily to commute to work. The modal share of PT was computed from the national census for professional mobility<fnref n="2" />. In the Île-de-France region, the modal share of public transport for commuting trips was 47.2% in 2018, higher than the modal share of car which was 35.7%. The geographical distribution of this indicator in Île-de-France shows a very high level of regional accessibility in Paris and the inner suburbs, which falls in the outer suburbs except along the RER (regional express network) lines (Figure 3). We are aware that some variables, especially the presence of a parking at home and, to a lesser extent, the housing type (house or flat) and the accessibility of the home city, present endogeneity issues with the household motorization status. Although residential and transportation equipment choices are joint choices, residential choices are considered as a prerequisite for transportation choices in agent-based mobility models today. We will therefore include them and interpret the explanatory effects concerning these variables as correlations in the results section.

<figure><img src="image_4.png" /><figcaption>Figure 3: Modal share of public transportation for professional mobility in the Paris region (2018)</figcaption></figure>

Concerning the predicted variables, we have redefined the classes that compose them. For the number of vehicles owned by the households, we defined the classes '0' for a non-motorized household, '1' for a household that owns exactly one car and '2+' for a multi-motorized household. In order to reduce the number of fuel type classes, especially those with a very small

<footnote n="2">²This professional mobility survey is a bi-located dataset that describes individuals habits with their municipality of residence and commune of work as references. Data source: INSEE 2018, https://www.insee.fr/fr/statistiques/5395749?sommaire=5395764#consulter</footnote>
sample, the classes were grouped as follows. 'Diesel' includes diesels and non-rechargeable hy-
brid diesels; 'petrol and others' includes petrol, non-rechargeable hybrids petrol, gases (LPG,
CNG) and others; 'electric' meaning 100% electric cars and finally 'plug-in hybrids'. Finally,
the year the vehicle was put on the road in the survey allowed us to calculate the car emission
standard (Table 4).

<table><caption>Table 4: Euro norm variable definition</caption>
  <thead>
    <tr>
      <th rowspan="2">Norm</th>
      <th colspan="2">EU law</th>
      <th colspan="2">EGT survey</th>
    </tr>
    <tr>
      <th>from</th>
      <th>to</th>
      <th>from</th>
      <th>to</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <td>&lt; ECE</td>
      <td>-</td>
      <td>01/01/1993</td>
      <td>-</td>
      <td>1993</td>
    </tr>
    <tr>
      <td>Euro-1</td>
      <td>01/01/1993</td>
      <td>01/07/1996</td>
      <td>1993</td>
      <td>1996</td>
    </tr>
    <tr>
      <td>Euro-2</td>
      <td>01/07/1996</td>
      <td>01/01/2001</td>
      <td>1997</td>
      <td>2000</td>
    </tr>
    <tr>
      <td>Euro-3</td>
      <td>01/01/2001</td>
      <td>01/01/2006</td>
      <td>2001</td>
      <td>2005</td>
    </tr>
    <tr>
      <td>Euro-4</td>
      <td>01/01/2006</td>
      <td>01/01/2011</td>
      <td>2006</td>
      <td>2010</td>
    </tr>
    <tr>
      <td>Euro-5</td>
      <td>01/01/2011</td>
      <td>01/09/2015</td>
      <td>2011</td>
      <td>2015</td>
    </tr>
    <tr>
      <td>Euro-6</td>
      <td>01/09/2015</td>
      <td>-</td>
      <td>2016</td>
      <td>-</td>
    </tr>
  </tbody>
</table>

Note: the EU law applies to the construction date, while the EGT survey indicates the year of first entry into service.

3.3.3 Data preprocessing

First, we filter the input dataset, removing the two households that did not complete the information about their cars types. They represented 0.026% of the households in the population. The database is then cleaned with Pycaret library, setting the option to attribute the mean answer to households that did not respond to a question: this concerns income and housing type variables. Regarding income, 775 respondents did not answer, accounting for 14.80% of the weighted population, whereas 2 respondents did not answer for housing type, accounting for 0.10% of the weighted population. Then, training and test samples are chosen based on a *stratified k-fold* cross-validation strategy, which returns stratified folds preserving the percentage of samples for each class.

Finally, the explanatory variables are normalized. The number of workers, the type of household, the type of housing, the presence of parking at home, and the presence of parking at workplace are all categorical variables, while the number of cars is an ordinal variable. *One-HotEncoding*, which transforms a variable with k classes into k boolean variables, is performed on categorical variables. Numerical variables are normalized with *minmax* normalization based on the training dataset: the income, the modal shares of public transport at home and at work, and the commuting distance.

4 Results

First, we compare the 14 classifiers performances for the prediction of the three car own-
ership models. We then present the validation results – including an analysis of the variables’
importance – for the estimation of actual household choice concerning car ownership for the first
stage, and the fuel type and the emission standard of the cars for the second stage. The mod-
els are simulated using the *pycaret* library (version 3.2.0) for python (Ali, 2020), which builds
upon machine learning libraries such as scikit-learn (Pedregosa et al., 2011). Detailed results on models performance for all presented metrics are presented in supplementary material.

## 4.1 AI vs discrete choice models performance

A comparison of classification performance for the 14 models was performed on the training datasets, representing 70% of the EGT sample. Table 3 provides a comparison of models performance based on the accuracy score, Cohen's kappa and Matthews correlation coefficient (MCC). The ranking of models performance based on MCC shows similar performances between the multinomial logistic regression and the best performing supervised machine learning models, with a slightly better performance of MNL, except for Euro norm prediction for which the gradient boosting classifier gives the best estimate. Let's notice that the rankings of the classification methods are almost identical for all indicators (MCC, F1 score, AUC, Cohen kappa) for the three choice models. In this imbalanced multi-class problem, accuracy, as well as the F1-score and Cohen's kappa value, give a first indication of the model's performance and the MCC is used to rank the models.

Regarding household motorization, logistic regression slightly outperforms supervised learning models, with similar scores for linear discriminant analysis and gradient boosting classifiers ($MCC_{lr} = 0.611$ vs $MCC_{lda} = 0.609$ and $MCC_{gbc} = 0.606$). The accuracy of the logistic regression classification is 75%, F1-score is largely closer to 1 than to 0 ($F1_{lr} = 0.75$), indicating a satisfying prediction, and Cohen's kappa confirms a substantial agreement for logistic regression, with $\kappa_{lr} = 0,61 \in [0,60; 0,80]$. Finally, Matthews Correlation Coefficient (MCC) reaches +0,611 for logistic regression, indicating a strong positive relationship.

For car fuel types classification, logistic regression slightly outperforms the best supervised learning model, ridge classifier, with $MCC_{lr} = 0.144$ and $MCC_{ridge} = 0.143$, as linear and boosting classifiers yield similar MCC. The accuracy reaches 56% for the two classifiers and F1-score is closer to 1 than to 0 for most classifiers ($F1_{lr} = 0.55$), indicating a quite satisfying prediction, whereas Cohen's kappa indicates a slight, nearly fair for gradient boosting classifier, agreement : $\kappa_{lr} = 0.14 \in [0; 0,20]$. Matthews Correlation Coefficient (MCC) reaches +0,144 > 0 for the multinomial logit, also suggesting a slight agreement.

Gradient boosting classifiers outperform logistic regression for predicting the Euro norm of a car, while the multinomial logit stands fifth with $MCC_{lr} = 0.037$ whereas $MCC_{gbc} = 0.048$. Also, the F1-score, which is closer to 0 ($F1_{gbc} = 0.26$), seems to indicate an unsatisfying prediction, Cohen's kappa indicates a slight agreement : $\kappa_{gbc} = 0.05 \in [0; 0,20]$ and Matthews correlation coefficient confirms a negligible relationship, slightly better than random. The accuracy of gradient boosting classification is still 27% for prediction of a 7-class variable. The prediction performance therefore requires a more detailed analysis of Euro norm misclassification to validate the model, to check if misclassifications lead to neighbor classes, e.g. Euro 5 instead of Euro 6. In the following sections, we will analyze the results for the model giving the highest MCC score with the most extensively adjusted parameters.
<table><caption>Table 5: Performance comparison based on accuracy (Acc.) and Matthew correlation coefficient (MCC) of 14 machine learning models ranked by MCC for predicting households' number of cars owned, their fuel types, and Euro norms.</caption><thead><tr><th rowspan="2">Models</th><th colspan="3">Number of cars</th><th colspan="3">Fuel type</th><th colspan="3">Euro norm</th></tr><tr><th>Acc.</th><th>κ</th><th>MCC</th><th>Models</th><th>Acc.</th><th>κ</th><th>MCC</th><th>Models</th><th>Acc.</th><th>κ</th><th>MCC</th></tr></thead><tbody><tr><td>Logistic Regression</td><td>0,750</td><td>0.609</td><td>0.611</td><td>Logistic Regression</td><td>0,560</td><td>0.144</td><td>0.144</td><td>Gradient Boosting Classifier</td><td>0,274</td><td>0.047</td><td>0.048</td></tr><tr><td>Linear Discriminant Analysis</td><td>0,740</td><td>0.601</td><td>0.609</td><td>Ridge Classifier</td><td>0,559</td><td>0.142</td><td>0.143</td><td>Linear Discriminant Analysis</td><td>0,275</td><td>0.040</td><td>0.041</td></tr><tr><td>Gradient Boosting Classifier</td><td>0,747</td><td>0.604</td><td>0.606</td><td>Linear Discriminant Analysis</td><td>0,556</td><td>0.140</td><td>0.140</td><td>Random Forest Classifier</td><td>0,259</td><td>0.039</td><td>0.039</td></tr><tr><td>Light Gradient Boosting Machine</td><td>0,736</td><td>0.586</td><td>0.588</td><td>Gradient Boosting Classifier</td><td>0,554</td><td>0.137</td><td>0.137</td><td>Ridge Classifier</td><td>0,278</td><td>0.037</td><td>0.038</td></tr><tr><td>SVM - Linear Kernel</td><td>0,722</td><td>0.574</td><td>0.585</td><td>SVM - Linear Kernel</td><td>0,543</td><td>0.109</td><td>0.128</td><td>Logistic Regression</td><td>0,275</td><td>0.036</td><td>0.037</td></tr><tr><td>Ridge Classifier</td><td>0,726</td><td>0.575</td><td>0.584</td><td>Light Gradient Boosting Machine</td><td>0,541</td><td>0.116</td><td>0.116</td><td>Ada Boost Classifier</td><td>0,268</td><td>0.035</td><td>0.035</td></tr><tr><td>Random Forest Classifier</td><td>0,733</td><td>0.582</td><td>0.583</td><td>Random Forest Classifier</td><td>0,534</td><td>0.102</td><td>0.103</td><td>Extra Trees Classifier</td><td>0,246</td><td>0.031</td><td>0.031</td></tr><tr><td>Ada Boost Classifier</td><td>0,705</td><td>0.548</td><td>0.562</td><td>Extra Trees Classifier</td><td>0,530</td><td>0.097</td><td>0.098</td><td>Light Gradient Boosting Machine</td><td>0,252</td><td>0.030</td><td>0.030</td></tr><tr><td>Naive Bayes</td><td>0,650</td><td>0.499</td><td>0.554</td><td>Ada Boost Classifier</td><td>0,526</td><td>0.091</td><td>0.092</td><td>SVM - Linear Kernel</td><td>0,264</td><td>0.021</td><td>0.025</td></tr><tr><td>K Neighbors Classifier</td><td>0,712</td><td>0.548</td><td>0.551</td><td>K Neighbors Classifier</td><td>0,517</td><td>0.062</td><td>0.062</td><td>Decision Tree Classifier</td><td>0,226</td><td>0.020</td><td>0.020</td></tr><tr><td>Extra Trees Classifier</td><td>0,712</td><td>0.548</td><td>0.549</td><td>Decision Tree Classifier</td><td>0,503</td><td>0.060</td><td>0.060</td><td>Quadratic Discriminant Analysis</td><td>0,080</td><td>0.008</td><td>0.012</td></tr><tr><td>Decision Tree Classifier</td><td>0,668</td><td>0.477</td><td>0.478</td><td>Quadratic Discriminant Analysis</td><td>0,193</td><td>0.012</td><td>0.013</td><td>K Neighbors Classifier</td><td>0,226</td><td>0.009</td><td>0.009</td></tr><tr><td>Quadratic Discriminant Analysis</td><td>0,341</td><td>0.057</td><td>0.077</td><td>Naive Bayes</td><td>0,037</td><td>0.005</td><td>0.009</td><td>Dummy Classifier</td><td>0,263</td><td>0.000</td><td>0.000</td></tr><tr><td>Dummy Classifier</td><td>0,481</td><td>0.000</td><td>0.000</td><td>Dummy Classifier</td><td>0,491</td><td>0.000</td><td>0.000</td><td>Naive Bayes</td><td>0,051</td><td>-0.003</td><td>-0.004</td></tr></tbody></table>
## 4.2 Households motorization model validation

In order to evaluate the multinomial logistic regression model, we plot the confusion matrix (Figure 4a) and the class prediction error (Figure 4b) based on the test dataset for the household motorization model. First, all classes have largely better than uniform random draw performance, with accurate predictions for 62% to 92% of the class elements in a 3-class model. Second, the non-motorized class is almost perfectly estimated, and motorized households are rarely predicted to be non-motorized: 14% for households owning one car and 3% for those owning several cars. Among motorized households, mono-motorization is better predicted with 71% accuracy and equally misclassified between non- and multi-motorization; whereas multi-motorization is mostly misclassified with mono-motorization.

The feature importance plot indicates that the most influential variables for the motorization

<caption>Figure 4: Assessment of the household's car ownership model with a MNL classifier.</caption>

Legend: Non-motorized: 0, Mono-motorized: 1, Multi-motorized: 2

<figure><img src="image_5.png" /></figure>
of the household are the presence of parking at home, the accessibility of the home city, household income and household type – especially couples with children. Logistic regression coefficients give a more precise insight into the effects of variables values. Figure 4c clearly shows that a high income has a significant positive effect on the probability of owning several cars and a negative effect on no or one car ownership. On the contrary, a high public transport modal share in home city has a strong negative impact on household multi-motorization and a strong positive impact on non-motorization. Concerning household types, couples with children are strongly likely to own several cars and unlikely to own one or no car, so as couples without children to a fewer extent, whereas single adults are very unlikely to be multi-motorized and likely to own zero or one car, especially women. There is also a salient effect of housing: living in a house has a positive impact on multi-motorization, whereas living in a flat has a positive impact on non-motorization.

## 4.3 Car type models validation

### 4.3.1 Fuel type

Concerning the choice of car fuel type, the multinomial logistic regression mode has also been evaluated as the better performing, so Figures 5a and 5b present its confusion matrix and its class prediction error based on the test dataset. This variable has a strong imbalance between classes, as electric and hybrid cars represent 0.6% and 2.0% of the car fleet, respectively. As a result, they are not predicted in the test sample. Among the majority classes, diesel cars have a 60% accuracy score whereas petrol cars have 54% accurate predictions, showing a slight agreement in the prediction of car fuel type choice.

Furthermore, the outstanding variables explaining fuel type choice are income, followed by commuting distance. Other significant features are housing that is not a flat or a house, an age between 15 and 24 years old, the regional accessibility of the residence city, and being a single-parent or other household types. More precisely, the logistic regression coefficients presented in Figure 5c indicate that higher income has a strong positive impact on the ownership of hybrid and electric cars and a strong negative impact on diesel cars. In contrast, the longer the commuting distance is, the more likely the household is to own a diesel car. Both high income and commuting distance have a small negative impact on petrol car ownership. Concerning age, the household being composed of young adults (where the oldest person is 15 to 24 years old) has a positive impact firstly on electric and secondly on diesel car ownership, and we observe that being a single man has a strong positive effect on electric car choice – whereas it is negative for single women. Finally, regional accessibility – approximated by the modal share of public transport – of the home city has a positive impact on electric or petrol car ownership and a negative impact on diesel car ownership. Regarding electric car ownership in particular, income is the major explanatory variable with a positive relationship. Being a young, one-worker or single-man household also have a strong positive impact on owning an electric car. Besides, the number of cars owned has a positive effect on electric car ownership.
<caption>Figure 5: Assessment of the fuel type model with a MNL classifier.</caption>

Legend: Diesel: 0, Electric: 1, Hybrid: 2, Petrol: 3

<figure><img src="image_6.png" /></figure>

4.3.2 Emission standard

In the third model, the emission standard of a household is predicted using a boosting approach, since the gradient boosting classifier has the highest MCC. We first notice that the classes are strongly imbalanced as the oldest ones (before Euro 2) have a very small sample size, which has an impact on the misclassification of these classes (Figure 6a). The assessment of the model prediction then shows that even if the classifier has slightly better than random performance, firstly the share of recent predicted classes increases as the actual age of the cars decreases, secondly the classifier tends to return neighbor classes even if the prediction is false (Figure 6b). The fact that the classifier predicts mainly neighbor classes when it fails indicates the model explanatory capacity.
<caption>Figure 6: Assessment of the emission standard model with a Gradient Boosting classifier.</caption>

Legend: < ECE: 0, Euro-1: 1, Euro-2: 2, Euro-3: 3, Euro-4: 4, Euro-5: 5, Euro-6: 6

<figure><img src="image_7.png" /></figure>

The features importance plot shows that the variables that most influence the age of cars owned by households are commuting distance and income, followed closely by regional accessibility of places of residence and work. Among other influential variables are the presence of parking at home, the age and the multi-motorization of the household. Income has a linear relationship with the car age. The higher the household income, the more likely it is to own a recent-class car (Euro 6) and the more unlikely it is to own an old car (< ECE), as shown by logistic regression coefficients (Figure 6c). A high commuting distance favors Euro 3 cars (built between 2001 and 2006), then the oldest cars (< ECE) and the most recent ones (Euro 6). Here, we can suppose that the choice of the oldest or the latest car depends on the income level of the household, and the effects of commuting distance and income combine together as commuting costs optimization faces the household financial capacity to invest in a recent car. Furthermore,
the regional accessibility of the home city and the farthest work city within the household, as
reflected by their public transport modal shares (PT share), both have a negative impact on
the ownership of a recent car and have a positive impact on older car's ownership. In addition,
the presence of a parking lot favors the ownership of recent cars, whereas multi-motorization
favors the ownership of old cars (at most Euro 1), but discourages the ownership of recent cars.
Concerning age, young adults are likely to own recent cars (Euro 5 or 6) whereas 25 to 55 years
old tend more to own Euro 4 or < ECE, and retirees to own older cars (Euro 1 or 2).

# 5 Discussion

This study investigates households' car ownership choices in the Paris metropolitan region, with a focus on choices that have an incidence on car emissions, that is to say fuel type and the emission standard given by the car age. Our first major finding is that multinomial logistic regression and supervised learning classifiers have similar performances for modeling household car choices. To model these choices, we had designed a two-level model of household car ownership – the first level being the number of vehicles owned while the second being the probability to own each car type depending on whether the household is mono- or multi-motorized. This choice of a two-level structure was confirmed by the model assessment, the number of vehicles owned having a strong positive impact on electric car ownership while also being a significant variable in the Euro norm model.

As a measure of classifier performance, we analyzed the Matthews correlation coefficient of car choice models. As expected, the car ownership model performs very well, with the multinomial logistic regression slightly outperforming supervised learning. The multinomial logistic model even has the highest MCC for motorization and fuel type prediction, whereas for Euro norm the gradient boosting classifier outperforms the other models. The performances of car ownership prediction are similar to those found in the literature, as Paredes et al. found an accuracy of 0.743 for the prediction of household car ownership – specified as 0, 1 or 2+ cars owned – in Singapore for the MNL classifier, with 0.1% of accuracy difference for the extreme gradient boosting ; while we obtained $Acc_{MNL} = 0.750$ for this model with 0.3% of difference in accuracy with gradient boosting. Basu and Ferreira reported lower performances in the same region for discrete choice models than machine learning with more classes since they also included motorcycles and taxis in their motorization model – their accuracy is 0.633 for multinomial logistic regression, 0.707 for random forest and 0.731 for neural network – which confirms the importance of the number of classes in the choice of the classifier. Besides, the classification models for car types had lower but satisfactory performances for unbalanced 4-classes and 7-classes problems. The highest performance for the boosting model, based on its MCC, was expected, as machine learning performs better on multi-class problems than discrete choice models. The analysis of prediction errors highlighted that the selected classifiers tend to predict the true class more often than any others and neighbor classes otherwise, notably for the Euro norm. In sum, the MNL and the most effective supervised learning classifiers (linear discriminant analysis or boosting methods) perform similarly for household car ownership choices modeling.
<table><caption>Table 6: Assessment of models representing households car ownership choices</caption><thead><tr><th>Decision type</th><th>Best classifier</th><th>Metrics</th><th>Most important variables</th></tr></thead><tbody><tr><td>Number of cars</td><td>Logistic regression</td><td>Acc. = 0.750<br>MCC = 0.611</td><td>Parking, regional accessibility of home city, income</td></tr><tr><td>Fuel type</td><td>Logistic regression</td><td>Acc. = 0.560<br>MCC = 0.144</td><td>Income and commuting distance</td></tr><tr><td>Euro norm</td><td>Gradient boosting classifier</td><td>Acc. = 0.274<br>MCC = 0.048</td><td>Commuting distance and income</td></tr></tbody></table>

Our second major finding is that income has a prevailing explanatory effect on the number and on the type of cars owned, as household income stands among the most important variables for all three submodels. Income is related to car ownership in many ways as high income allows one to have access to the most recent and most efficient cars, but also to afford parking, car maintenance, etc. With the current diesel tax breaks in France, choosing the fuel type has a significant impact on the cost of driving, which explains why high income has a strong negative impact on diesel car ownership. Besides, income has a strong positive impact on electric or hybrid car ownership, as their acquisition remains expensive. This is consistent with Alberini and Santoboni (2025) findings on electric and hybrid car ownership in Washington DC in 2018, showing that their owners are wealthier and own more vehicles than those of internal combustion engine cars. Finally, the results of the logistic regression confirm that higher income favors ownership of a recent car. This income effect has been shown in numerous studies on the choice of the number of cars (Potoglou and Kanaroglou, 2008b; Paredes et al., 2017; Basu and Ferreira, 2020; Zambang, Jiang, and Wahab, 2021, e.g.).

Furthermore, the commuting distance and regional accessibility of the city of residence served as indicators of household mobility capacity and constraints, which were rarely considered in previous studies. Although the presence of parking variables could cause endogeneity issues, it is linked to the income effect and reflects the mobility equipment capacity of the household. Its importance is consistent with Christiansen et al. study (2017), which brings out the influence of parking access on car ownership, as they found that access to private or reserved parking triples the likelihood of car ownership. Next, regional accessibility of the residence city was one of the most significant variables for motorization classification. We can assume that the income effect is modulated by the regional accessibility of the home city for motorization choice, since it translates into a higher mobility capacity even without owning a car: this phenomenon is for example observed in Paris city where the high non-motorization rate is correlated to a dense public transport network. Finally, commuting distance is the most significant variable for car type choice with household income. We observed that commuting distance has a negative impact on petrol car ownership and a positive impact on Euro 3, < ECE and Euro 6 cars. It can be interpreted as an optimization of car costs by households, with a compromise between the income effect and commuting distance for the type of car chosen by motorized households. The costs optimization logic calls for a recent diesel or electric car choice, especially when the commuting distance is long and therefore the driving costs are high. Nevertheless, it might be limited by the household's inability to invest in a more recent car. That would explain why
income has a linear positive impact on car age, but commuting distance does not. Households
with longer commuting distances and low incomes are indeed the most constrained in their travel
and car ownership choices and can be identified as in a position of transport poverty as defined
by (Lucas et al., 2016). This might denote the car-dependency of households living far from
urban centers with low income (Newman and Kenworthy, 1998).

The chosen methodology to model household car ownership choices is consistent with guide-
lines from Cranenburgh et al. (2021) literature review on machine learning in choice models, as it
relies on metrics that are appropriate for our problem with an assessment based on out-of-sample
test data. This study uses both supervised learning and discrete choice modeling indicators in a
complementary way, for example accuracy and Matthews correlation coefficient or logistic regres-
sion coefficients and features importance, to assess the classifiers and interpret the explanatory
variables. Even if the fuel type model has a lower accuracy than the household motorization,
important variables (mainly income and commuting distance) give valuable insight on car type
attribution. Our results are for example in agreement with French national statistics on electric
cars’ acquisition in 2022 (Maamari, 2024). In this study, a logistic regression has been performed
on car purchasing data matched to the municipality sociodemographic data, taking into account
the available income, age, household type, housing type, the size of catchment basin, type of
commune and number of cars owned by the household. Maamari also found that households
with higher income, those owning several cars and young adults are more likely to purchase an
electric car. We only notice a slight difference regarding the household type: while our results
concur on the gender effect and on the fact that couple with children are more likely to own an
electric car, they found that couple with children were more likely than single men to buy an
electric car. Furthermore, the main limitation of the models we present here is that socioeco-
nomic and mobility-related variables can only partially explain car type choice. Prior studies
on fuel type choice have indeed pointed out some hurdles: car type choices are influenced by
psychological factors such as the perceived benefit in fun (Kim, Tanaka, and Schmöcker, 2020)
or the car pride (Moody and Zhao, 2020), as car ownership is usually associated with freedom
and progress in social representations. For example, Moody and Zhao have shown that there
exists a feedback loop between car ownership, car pride and car use. Besides, the dataset used
in this study is a survey which has been conducted between 2018 and 2020, with therefore still
few households owning electric or hybrid cars. The fuel type prediction is therefore expected to
be more reliable when more recent surveys will be published, as the market share of electric and
hybrid vehicles keeps growing, with electric cars representing 18.3% of household purchases of
new cars in France in 2022 (Maamari, 2024). Furthermore, it should be noted that the model
share of public transport is only a proxy for regional accessibility, since it implicitly estimates
the availability and regional reach of public transport in a city through their actual use by in-
habitants. Local accessibility indicators could have been used if the distance to the nearest bus
stop or metro station was indicated in the survey.

This analysis on household choices relative to car ownership opens the door for further
research on household (non-)motorization choices. In the context of spatial mismatch between
jobs and housing (J. Kain, 1968; J. F. Kain, 1992; Alamá-Sabater et al., 2024), the disjoint
modeling of residential and mobility equipment choices can be questioned. Indeed, our model
shows that the commuting distance and the regional accessibility of the city of residence are
significant explanatory variables of the number and type of cars owned. As of now, the joint
modeling of residential and car ownership decisions has been carried out in some empirical
studies (e.g. Yao, D. Wang, and Yang, 2020 or Picard, De Palma, and Dantan, 2013) for joint
residential and mode choices with a representation of negotiation within couples. However, it
is very rarely implemented in agent-based mobility models due to the complexity of moving
toward a so-called integrated transport land-use model (ITLUM). Future discrete choice model
studies could still seek to include car type choices in joint residential and car ownership choices,
even with a larger acceptance of mobility equipment that could be interpreted in a larger sense:
for example Püschel et al. jointly predicted car ownership, public transportation pass and car
sharing membership (2023) to take into account the substitution effect between them (Scott
and Axhausen, 2006). Concerning the motorization model, a more detailed classification of non-
motorized households would be interesting to provide a better understanding of the explanatory
variables differences between classes depending on the stability of their non-motorized status:
long term, temporary non motorization and demotorization can be distinguished, and it has been
shown that they do not concern the same household profiles in France (Korsu, Aguilera-Belanger,
and Proulhac, 2021). This would require the model to be trained on a dataset containing the
time from which the household is not motorized, and if it intends to be one day. The induced
classification performance improvements by such a classification would yet be marginal since
the non-motorized class prediction is very accurate, but the explanatory variables analysis could
enrich our knowledge on household profiles diversity in this three non-motorized households
categories.

6 Conclusion

This study mainly contributes to the existing literature by investigating socioeconomic and mobility-related determinants of household car ownership decisions. The results confirm the importance of the income effect on car ownership choices, which had already been demonstrated for the number of cars but neither for the fuel type nor the age. Our analysis confirms, for instance, that electric cars are mainly owned by high-income, young and single-men households. Another contribution of this study is that we included variables related to household mobility that were rarely considered in previous studies, namely regional accessibility and commuting distance. The most significant explanatory variables for car type choices in motorized households are income and commuting distance. Higher commuting distance calls for a choice of car that optimizes driving expenses, as long as the household has the financial capacity to invest in a recent electric or diesel car. Concerning the motorization choice, the income effect is weighted by the regional accessibility of the home city as it translates into a higher mobility capacity, whereas low accessibility induces household car dependency.

Secondly, we assessed 14 classifiers for each model: car number, fuel type and age. Multi-
nomial logistic regression and boosting or linear machine learning classifiers performed similarly
for all models. This work thus emphasizes that discrete choice and supervised learning mod-
els should rather be brought together than opposed to improve the performance of vehicle fleet
synthesis in agent-based modeling. Classifiers evaluation methodology was informed by machine
learning literature, considering the Matthews correlation coefficient (MCC) as an appropriate
indicator for this imbalanced multi-class problem, with data preparation including imputation
and normalization of features and an assessment based on unseen test data. Their performance
has been assessed to be largely satisfactory for the choice of the number of cars owned (3 classes),
as *MCC* = 0.611, and quite satisfactory for fuel type and car age choices as their MCC reached
respectively 0.144 and 0.048. Despite car type models having lower performance, the Euro norm
model tends to predict neighboring classes, and both models reproduce the effect of income and
commuting distance on household car choices. The training steps are fully reproducible in other
cities as the code is available in an open-source repository and the models rely on usual mobility
survey variables.

We finally provide these open-data trained classifiers for household car ownership choices
prediction. The classifiers are expected to supply a synthetic car generation model associated
with a synthetic population in future works. This disaggregated car fleet model would allocate
specific car types to each household in place of state-of-the-art average emitting cars. It would
still take further work to correct a metropolis car fleet based on the fuel type and Euro norm
classifiers, given their limitations highlighted by accuracy and MCC. This study thereby provides
valuable inputs for agent-based mobility models, as well as pollutant emission models, that
could be used in this context to assess car fleet regulatory measures such as Low emission
zones. The model would enable assigning car types to households, and therefore their emissions
profile, allowing the assessment of the impacts of mobility policies on the environment and on
individuals' mobility vulnerability.

7 Code and data availability

Classifiers trained on the entire EGT mobility survey, along with the detailed steps of the
modeling pipeline, including preparation of explanatory variables, imputation and normalization,
are publicly available in Zenodo (for double-blinding reasons, we put all the data and code on an
Open Science Framework repository https://osf.io/3urjg/?view_only=c4300170f084447da99181fb085bb5c1).
The associated python code to reproduce the data preparation, classifiers training and selection
and features importance analysis is also provided. The survey has been obtained through a
restricted data use agreement with Île-de-France mobility agency.
List of acronyms

DCM discrete choice model. 2, 4, 10

EGT Enquête Globale de Transport. 9, 10, 24

INSEE National Institute of Statistics and Economic Studies. 12

MCC Matthews correlation coefficient. 3, 8, 14, 18

ML machine learning. 4, 10

MNL multinomial logit. 2, 14

PT public transport. 11
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