Drivers of Driving: A Review

Abstract

As car ownership and usage expand globally, understanding the factors that influence the propensity to drive is crucial for promoting sustainable transportation. This literature review examined the factors influencing driving decisions through a systematic search of databases, rigorous screening of over 1000 articles, and analysis of 142 studies. The findings reveal that attributes of the built environment (e.g., density, diversity, accessibility), economic factors (e.g., income, costs of car ownership, policies), and psychological aspects (e.g., attitudes, social norms, perceptions) have significant impacts on driving behaviors. By employing an integrative methodology involving targeted searches, keyword analysis, and detailed evaluation, this review offers insights into the multifactorial nature of driving decisions. The synthesis of studies across multiple domains emphasized the need for a holistic approach to understanding and addressing the factors influencing the propensity to drive, laying a foundation for informed transportation policy and practice.

1. Introduction

1.1. Global Trends in Car Ownership and Usage

The global number of registered cars has experienced a significant increase over recent decades, progressing from 246 million in 1970 to 1.446 billion in 2022 [1,2]. In 2020, approximately 78 million new vehicles were produced, adding to the existing global car fleet; this number rose to about 80 million in 2021 [3]. The surge in car ownership is not limited to developed countries alone. Consistent economic growth in developing countries and the Global South has led to a notable increase in car ownership and usage [1,4,5,6]. A study by Li et al. [7] showed that private car ownership in China grew significantly from 2000 to 2018, increasing from 6.25 million to 207 million, with an annual growth rate of 21.4%. Furthermore, the study projected that by 2030, private car ownership in China is expected to reach 475 million, marking a 76-fold increase from the levels in 2000. Dargay et al. [8] also predicted that by 2030, non-OECD nations will own 56% of the world’s cars, up from 24% in 2002.

The rise in car ownership is significant, as it enables various applications, such as long-distance travel, the transportation of large items, and new leisure activities dependent on personal vehicles [9,10,11]. Consequently, the increase in car ownership is likely accompanied by a rise in car usage [12,13]. Empirical studies on car usage patterns support this, revealing that in German, Swiss, and Austrian cities, around half of all trips are made by private cars, and in American cities, this figure is 86% [14]. In the United Kingdom, private cars account for two-thirds of all weekly trips, covering three-quarters of the distance traveled by an average citizen [15].

1.2. Negative Impacts of Excessive Car Use

The trend toward increased car usage partly stems from the user’s perception of cars as the most convenient and preferred transportation mode, which is associated with enhanced mobility, quality of life, and flexibility [16]. However, this excessive reliance on cars can lead to numerous societal externalities, as extensively discussed in the literature [17]. The primary externalities linked to car usage include climate change and air pollution, traffic congestion, noise pollution, road safety concerns, and health risks (it is noteworthy that other less-studied externalities, like emergency services, natural resource depletion, water pollution, and urbanization effects, also play a significant role in the overall costs of road transport externalities and are estimated to be around 10% [18]).

Climate change and air pollution: Wee [19] identified three main environmental problems stemming from the transport sector: (1) climate change caused by CO₂ emissions; (2) acidification of the environment, including agricultural land; and (3) large-scale local air pollution as a result of emissions from the transport sector and chemical reactions in the atmosphere. Private cars, which are a significant source of greenhouse gas emissions, emit pollutants, such as carbon monoxide (CO), nitrogen oxide (NO_x), and hydrocarbons (HCs) [20]. These emissions are central to the discussion on sustainable transportation, as Bickel et al. [21] estimated the annual environmental costs of road transportation in Europe to exceed EUR 86 billion, which encompasses climate change and air pollution.

Traffic congestion: Cravioto et al. [22] defined congestion as a waste of time resulting from the excessive use of limited road infrastructure. Urban road congestion in the USA causes substantial travel delays, where Schrank et al. [23] recorded 6.9 billion hours of delay in 2014, leading to an economic loss of USD 160 billion. This is exacerbated by the growth of private vehicles disproportionate to the available space and infrastructure, which is a common issue in metropolitan areas [18].

Noise pollution: The relationship between noise and health is well-established, where Suter [24] found that noise can lead to stress-related illnesses, high blood pressure, speech interference, hearing loss, sleep disruption, and reduced productivity. Road traffic is a major source of noise, accounting for 90% of the health impact caused by noise [25]. Evans et al. [26] estimated the average cost of urban traffic noise per 1000 passenger-kilometers for cars, buses, and trains, indicating the significant role of transportation in noise pollution, which, in turn, affects public health and quality of life.

Road safety: Road traffic accidents are a major global concern, with approximately 1.3 million fatalities annually and 20 to 50 million non-fatal injuries, with many resulting in disability [27]. The economic impact of these accidents varies by country income level, with a notable effect on national economies. Traffic-related trauma, as Paniker et al. [28] highlighted, is a leading cause of death and disability globally, emphasizing the importance of addressing safety in transportation policies and driving behavior analysis.

Health risk: Sugiyama et al. [29] conducted a systematic review on the relationship between car use and health risk; they found that prolonged periods in cars are associated with increased cardiovascular disease risk. Moreover, driving in congested areas or other stressful conditions can affect mental health issues, such as anxiety and depression.

1.3. Research Aims and Scope

The existing research on car dependence initially emerged from concerns about the depletion of fossil fuel resources and subsequent energy crises. The motivation behind this study, however, was rooted in the broader externalities of car dependence. While the current literature provides insights into the consequences of car usage, it often overlooks a comprehensive analysis of the underlying factors that encourage driving. This gap is significant, given that understanding these drivers is essential for mitigating the negative impacts of car usage and for promoting sustainable transportation options. Our study aimed to fill this gap by providing a detailed examination of the reasons behind individuals’ driving behaviors. We investigated factors that influence not only the decision to drive rather than use alternative modes of transportation but also those that impact the overall driving distance. Although the choice of mode and the distance driven are separate elements of driving behavior, they are interrelated and both contribute to the societal impacts and challenges mentioned previously. This study sought to enhance urban mobility and accessibility by offering a clearer insight into the complex motivations for driving.

We acknowledge certain inherent limitations. The scope, while broad, may not encompass every relevant study, partly due to the constraints in the search terms and databases used. Furthermore, the majority of the studies reviewed focused on urban settings in developed countries, which might limit the applicability of our findings in different socioeconomic and infrastructural contexts, such as rural areas or developing nations. The rapid evolution of transportation technologies, like electric vehicles and intelligent mobility solutions, may also date some of the referenced literature, necessitating continuous updates to this review. Additionally, our emphasis on quantitative research might overlook qualitative insights that could offer a deeper understanding of personal motivations and context-specific factors influencing driving behavior. These limitations notwithstanding, our review aimed to provide a comprehensive and current perspective on the factors influencing driving decisions.

The subsequent sections of the paper are structured as follows: Section 2 outlines the review methodology. Section 3 presents an analysis of the various factors influencing driving behavior. Finally, Section 4 provides a summary and conclusion of the paper.

2. Review Methodology

This section details the comprehensive and methodical approach employed for conducting the review, i.e., following the PRISMA guidelines [30] to ensure a transparent, systematic, and replicable process.

2.1. Identification of Relevant Sources

At the initiation of our research process, we embarked on an extensive search to identify the most suitable sources, aiming to ensure access to a broad range of high-quality scholarly materials relevant to our study. This critical phase involved a careful evaluation of various databases, each of which are recognized for their rich collection of scholarly literature in the domains of transportation studies and decision-making related to driving behaviors.

The databases searched included Google Scholar, which was selected for its extensive coverage across multiple academic disciplines; PubMed, which was chosen for its specific focus on health-related aspects; Scopus and Web of Science, which were utilized for their thorough indexing of peer-reviewed articles in transportation and environmental research; and JSTOR, IEEE Xplore, and the Transportation Research Board (TRB) database, which were included to capture historical perspectives, technological advancements, and policy-related studies on transportation.

To add depth to our research, we also searched a range of academic journals focused on transportation, including Transport Reviews; Transportation Research Parts A, B, C, and D; Transportation; Transportation Science; Journal of Transport and Land Use; Journal of Urban Planning and Development; Cities; Journal of Transport Geography; Transport Policy; and Accident Analysis and Prevention. These journals were selected for their contributions to urban planning, geographical aspects of transportation, policy discourse, and safety research.

Furthermore, we delved into specialized databases and digital repositories, like the National Transportation Library and the EU Transport Research and Innovation Portal (TRIP) to gain access to technical reports, government publications, and policy documents, incorporating practical insights and regional studies into our comprehensive review.

2.2. Development of Search Strategies

To comprehensively capture the breadth and depth of research on driving propensity and car dependency, our literature search strategy employed a multidimensional approach. We meticulously curated a set of keywords and subject headings, as detailed in

Table 1. Keywords and subject headings for literature search on car ownership and usage patterns.

Category	Keywords and Subject Headings
General terms	Car ownership, vehicle ownership, automobile dependence, driving propensity, car usage, vehicle-use patterns, motor vehicle trends, auto mobility
Economic factors	Cost of car ownership, vehicle maintenance costs, fuel prices, car affordability, transportation economics, ownership cost analysis, vehicle insurance costs, vehicle financing, economic impact of car ownership
Environmental factors	Environmental impact of cars, CO2 emissions from vehicles, urban pollution and cars, sustainable transportation, eco-friendly vehicles, vehicle emissions, climate change and automobiles, green vehicles, electric car environmental impact
Demographic factors	Age and car usage, gender differences in driving, socioeconomic status and vehicle ownership, family structure and car needs, demographic trends in car ownership, youth and vehicles, elderly driving patterns, income level and car use
Built environment factors	Urban design and car use, walkability and driving patterns, neighborhood design and vehicle usage, transit-oriented development, land-use mix, street connectivity, built environment and sustainable mobility, urban density and car use, accessibility and car dependence
Psychological aspects	Psychological drivers of car use, car as a status symbol, emotional attachment to cars, convenience and independence, perception of car ownership, motivation for car use, car and identity, psychological effects of driving, vehicle choice psychology
Social and behavioral	Attitudes toward car ownership, behavioral economics of driving, sociocultural influences on car usage, transportation behavior, travel behavior, social norms in driving, lifestyle and car usage, personal transportation choices, mobility behavior
Urban planning	Urban sprawl and car use, public transport accessibility, city planning and vehicle use, traffic congestion, parking availability, urban transport planning, land use and transportation, public transit development, urbanization and car use
Policy and legislation	Transportation policy, emission regulations, car taxation, urban mobility policies, public transportation funding, transport regulations, environmental policy for vehicles, infrastructure policy, urban transport governance
Technological advances	Electric vehicles, hybrid cars, autonomous vehicles, car sharing, ride-hailing services, vehicle technology innovation, smart cars, connectivity and automobiles, future of automotive technology
Health and safety	Road safety, health risks of car usage, traffic accidents, active transportation, pedestrian-friendly planning, driver safety, vehicle-related injuries, health impacts of driving, traffic safety measures

, to reflect the intricate nuances of car ownership and usage. Boolean operators (AND, OR, NOT) were instrumental in effectively combining search terms to create a cohesive and robust search string.

In addition to database searches, we adopted the technique of reference list checking, examining the reference lists of key papers to uncover additional relevant studies that may have been overlooked in the initial search. Our strategy also extended to include grey literature, such as conference proceedings and technical reports, to ensure a holistic view of the topic.

2.3. Inclusion and Exclusion Criteria

To determine the eligibility of studies for inclusion in our review, we established the following inclusion criteria:

• Peer-reviewed journal articles, conference papers, technical reports, or government publications.
• Published in the English language.
• Focused on factors influencing car ownership, car usage, or driving propensity.
• Contained empirical data or rigorous conceptual analysis.

Studies were excluded if they did not focus on factors influencing driving behaviors, were not in English, did not contain original research (e.g., commentaries, editorials), or had severe methodological limitations.

2.4. Screening and Study Selection

The process of selecting studies, as depicted in the PRISMA flow diagram (Figure 1), commenced with initial searches in databases, which produced 1243 records. These records were screened by title and abstract, leading to the elimination of 640 records that failed to meet the inclusion criteria. The full texts of the remaining 603 articles were evaluated for eligibility, which resulted in the exclusion of 352 articles for various reasons, such as a lack of focus on factors affecting driving behaviors (n = 209), absence of original research (n = 98), or significant methodological flaws (n = 45).

Given the potential for redundancy in the literature, we prioritized including the most relevant and recent studies when multiple articles offered overlapping content. This approach allowed us to strike a balance between being thorough and succinct. The remaining 251 articles were then thoroughly evaluated based on their study design, sample size, methodological rigor, and the strength of the statistical analysis to ensure the highest quality and relevance for our review. Ultimately, 109 articles were excluded due to insufficient quality and relevance, while 142 articles were selected for the final qualitative synthesis. These articles collectively provided a comprehensive understanding of the drivers of driving behavior, representing a wide array of perspectives and methodologies.

This curated collection of literature formed the backbone of our review, offering valuable insights into the various factors influencing driving decisions. By adhering to the PRISMA guidelines and employing a rigorous, transparent methodology, we ensured that our review was systematic, comprehensive, and replicable.

3. Analysis of Driving Determinants

This section offers a detailed review of the various factors that influence driving decisions and their implications for transportation policy and practice. Through our extensive search, we identified three primary categories of driving determinants: built environment attributes, economic factors, and psychological factors. Each of these categories was examined in depth to assess how they impact an individual’s choice to drive (Appendix A provides a summary table of the reviewed articles, including key details, such as author(s), publication year, study location, temporality, objectives, methods, and main findings for quick reference).

3.1. Built Environment Attributes

The built environment, encompassing the physical characteristics and features of a place, such as the layout, design, and infrastructure of a city or region, has been extensively researched [31,32,33,34]. Factors such as the availability of public transportation, the density of housing and businesses, and the connectivity of streets and sidewalks are critical built environment attributes that significantly influence travel choices. These attributes of the built environment, commonly known as the “six Ds” [35,36,37,38], are density, diversity, design, destination accessibility, distance to transit, and demand management.

Density: This term refers to the concentration of individuals and buildings within a specific area. It not only directly influences driving propensity but also acts as an indicator of other land-use factors related to density, such as parking availability [39]. The effect of density on driving propensity and car dependency was thoroughly analyzed in previous studies using various density measures, including residential density [40,41,42] and job density [40,43]. These studies consistently show that higher densities are associated with lower driving propensity rates. The logic behind this is straightforward: as the mix of employment and residential areas increases in a location, so does its accessibility, leading to a reduced reliance on individual cars [40]. This relationship between density and driving propensity is supported by various empirical studies. For example, the 2001 Household Travel Survey for California [44] found that areas with densities below 1000 housing units per square mile experienced a 4.8% increase in vehicle kilometers traveled. A meta-analysis of 23 planning studies from 18 metropolitan areas in the USA [45] estimated that increasing the density could reduce vehicle kilometers traveled by 17% by 2050 compared with a “business as usual” scenario. Leck [46] also identified residential density as the most significant factor in mode choice within the built environment, followed by mixed land use and the street configuration. Ding and Cao [39] found that residential and employment densities were the most influential factors in the relationship between the built environment and car usage after evaluating several studies on this topic. Furthermore, research in Flanders, Belgium [47], demonstrated that higher densities promoted the use of alternative modes of transportation, such as walking, cycling, and public transit.

Diversity: This term describes the variety of different land-use forms. Extensive literature discusses the link between land-use diversity and driving propensity, with numerous studies showing a positive relationship between increased land-use diversity and reduced driving propensity [48,49]. This effect is attributed to the fact that land-use diversity enhances access to proximal destinations, thereby reducing the need for individual car usage [38]. Empirical evidence from a travel survey in Jinan, China [50], indicated that improvements in the balance between employment and housing were associated with a decrease in car ownership and usage. Beyond the direct relationship between land-use diversity and driving propensity, it is vital to consider mediating factors that may influence this relationship. For example, the availability and quality of public transportation, as well as the accessibility of non-motorized modes of transportation, such as walking and cycling, may affect the relationship between land-use diversity and driving propensity. The presence of these alternative modes of transportation can enable individuals to access nearby destinations without relying on cars, leading to a decrease in driving propensity [51].

Design: This term refers to the characteristics of the street network in a particular area, including factors like intersection design and average block size. Research examining the relationship between street design and driving propensity has produced mixed results. Some studies identified a relationship between pedestrian-friendly design elements, such as walkways and overhead street lighting, and lower driving propensity rates [49,50]. However, other studies have found no significant link between pedestrian-friendly design and driving propensity [52]. These inconsistent findings suggest that the impact of design on driving behavior may depend on contextual factors, such as land-use density and diversity. Additionally, the availability and reliability of alternative transportation modes, as well as individual attitudes and preferences toward transportation, can influence this relationship. Despite mixed evidence, it is widely recognized that design plays a crucial role in shaping transportation behavior. For example, street and intersection designs that prioritize the safety and convenience of active transportation modes, like walking and cycling, can promote their use and potentially decrease car ownership or usage. Similarly, designs that facilitate public transit use can make it a more attractive and convenient option, leading to reduced car ownership or usage [53].

Destination accessibility: This term describes the ease and availability of transportation options that enable individuals to reach desired locations efficiently and conveniently. It is a critical component of community livability and sustainability, potentially affecting mobility, economic opportunities, and social interactions. The likelihood of using alternative transportation modes, such as walking, biking, or public transit, instead of driving increases when destinations are easily accessible, potentially reducing driving propensity [54]. However, the influence of destination accessibility on driving propensity can vary based on the location and cultural context. For instance, studies in the USA [55,56] have shown that households closer to metropolitan centers typically have fewer cars. In contrast, research in Beijing and Chengdu, China [57], indicates the opposite trend, with households further from metropolitan centers having fewer cars. This variation could be attributed to differences in social structures and transportation systems between developed and developing countries, or it may be specific to developing countries based on historical growth trends and the extent to which affluent residents centralize or relocate to urban outskirts. A study in Jinan, China [50], for example, found that proximity to the city’s major and secondary centers had no significant effect on car dependency, underscoring the complex nature of destination accessibility and the need for a thorough understanding of its determinants.

Distance to transit: This term pertains to how close a location is to public transportation stations and services. Research has consistently shown that there is an inverse relationship between the proximity to transit infrastructure and car ownership and usage rates. For instance, Li and Zhao [38] discovered that the closeness to public transportation stations significantly affects car ownership and usage. Similarly, Potoglou and Kanaroglou [48] observed that residents living within 500 m of bus stops tended to have lower car ownership rates. Zegras [49] found that households in areas with limited bus access compared with those with better access to cars had more vehicles. Moreover, Chatman [58] indicated that improved bus services in neighborhoods could deter the acquisition and use of personal vehicles. Correspondingly, living near rail transit systems, such as subways or light rails, has been associated with reduced car ownership and usage. Studies by Kim and Kim [59] and Chatman [60] noted a negative impact of rail proximity on car ownership and usage. Gossen [61] revealed that only 19.9% of total trips by residents living within 0.25 miles of a metro station were made by car, in contrast with 45% by those living a mile away from transit stations. However, the relationship between transit proximity and driving propensity can be complex. For instance, Cao and Cao [62] found that in Minneapolis, light rail proximity did not affect car ownership when controlling for other factors, and Cervero and Murakami [63] reported a weak connection between metro station proximity and vehicle miles traveled. Furthermore, Combs and Rodríguez [64] analyzed Bogota’s TransMilenio BRT and found no significant impact on car ownership related to access to the TransMilenio route, except in transit and pedestrian-friendly areas. A study in the Indian cities of Hubli and Dharwad by Doddamani and Manoj [65] highlighted the nuanced nature of this relationship, showing significant effects of the road network density and proximity to key amenities, like hospitals and bus stops, on motorcycle ownership, even after considering travel attitudes.

Demand management: This concept involves strategies designed to control the demand for parking and other transportation services, including the regulation of parking capacity, and the implementation of parking fees and congestion pricing. The availability of parking has a significant impact on car ownership and usage. For example, Weinberger et al. [66] and Weinberger et al. [67] found that homes with ample off-street parking options were more likely to be occupied by households with higher rates of car ownership and usage compared with similar neighborhoods with restricted parking availability. This suggests that parking availability can influence both the decision to own a car and how frequently it is used. Guo [68,69] also noted a strong impact of home parking availability on car ownership, implying that home parking availability is a crucial factor in the decision to own a car, especially in urban areas where on-street parking is limited. The relationship between parking availability and driving propensity has been explored in both developed and developing countries. In a study conducted in Norway, Christiansen et al. [70] found that access to private or reserved home parking significantly increased the likelihood of car ownership. Conversely, Sobhani et al. [71] investigated the influence of residential parking space on car ownership in developing countries and suggested that the relationship might be less pronounced in these settings. This implies that within a city, variations in car ownership are more likely attributable to differences in parking demand and regulations, rather than merely the availability of residential parking.

3.2. Economic Factors

As discussed by Van Eenoo et al. [72], simply altering the built environment may not be enough to significantly reduce car dependency. Various studies [48,73,74,75] have demonstrated that a range of economic factors critically influence the decision to own and use a car. These factors include an individual’s income; education; occupation; and the costs associated with car ownership, such as fuel and maintenance expenses. Additionally, government policies, such as subsidies and tax incentives for alternative modes of transportation, play a significant role. Therefore, it is essential to consider the impact of economic factors on driving behavior. We categorized these factors into three main groups: socioeconomic characteristics, the costs of owning and maintaining a car, and government policies, all of which significantly affect an individual’s propensity to drive.

3.2.1. Socioeconomic Characteristics

Extensive research has established the significant influence of socioeconomic characteristics on travel behavior and driving propensity. Numerous studies have consistently shown that factors such as income, household structure, education, and age are closely associated with car ownership and travel habits [76,77,78,79,80,81,82,83,84,85]. These sociodemographic characteristics play a crucial role in shaping an individual’s travel behavior and reliance on a car.

Household income, in particular, is a key factor affecting driving propensity. Households with higher incomes, regardless of their location, generally own more cars and use them more frequently for transportation compared with those with middle or lower incomes [86,87,88]. This trend is attributed to the ability of higher-income households to afford multiple vehicles, as well as the associated costs of fuel, insurance, and maintenance. On the other hand, households with lower incomes may find it challenging to bear the expenses related to car ownership, and thus, may rely more on alternative transportation modes, like public transit, biking, or walking. Research from various developing countries, such as India [89], South Africa [90], China [57], Kenya [91], Iran [88], and Nigeria [92], supports the view that higher income is a significant determinant of household car ownership and usage. It is crucial to recognize that household income influences driving propensity not only through financial capability but also by affecting the availability of alternative transportation options. For example, households in low-income neighborhoods often have limited access to public transit, making car ownership essential for commuting and other transportation needs. Similarly, in rural areas where public transit access is scarce, households are more likely to depend on cars for transportation due to the absence of viable alternatives [93,94].

Additionally, the structure of a household significantly influences driving propensity due to its effect on the mobility needs of the household members. Research indicates that having more children often leads to the need for a car to meet increased mobility demands, such as transporting children to school and other activities [95,96]. This is especially relevant in single-parent households, where a car becomes essential for managing children’s schedules. Contrarily, other studies suggest that having more children may reduce the likelihood of car ownership due to the allocation of household resources to other expenses [97]. The number of adults in a household, along with their work and transportation habits, can also influence car use. Households with multiple adults who work from home or have flexible schedules might be less dependent on a car and more inclined to use alternative transportation, such as public transit, biking, or walking. Conversely, households with several children might require multiple vehicles to cater to the transportation needs of the entire family. Households without children, or with adult children who have moved out, are typically less reliant on cars and may prefer other modes of transport [98].

Expanding on the theme of household structure, other factors like the characteristics of the household head and the number of employed individuals within the household also play a role in driving propensity. Studies have shown that male-headed households tend to own and use cars more than female-headed households, possibly reflecting societal gender roles and expectations [99]. Furthermore, households with more working members are likely to own and use more vehicles to meet their commuting and transportation needs, as they generally have higher mobility requirements [43,48,87]. However, this trend may vary according to regional and cultural contexts. For instance, a study in Chennai, India, found that households with female employees and school-aged children were more likely to own cars [100], highlighting the influence of different societal norms. Conversely, households with fewer employed individuals or those working remotely might be less dependent on cars and more inclined to use alternative transportation modes, like public transit, biking, or walking.

Other socioeconomic factors also impact an individual’s reliance on a car for transportation. These factors include the level of education [84,101,102] and age [103,104]. People with higher education levels may have more awareness of the environmental and economic implications of car use and the availability of alternative transportation modes. For instance, more-educated individuals might be more knowledgeable about the effects of car emissions on air quality and climate change, hence more inclined to use alternatives, like public transit, biking, or carpooling. They are also more likely to live in urban areas with efficient public transportation systems, offering alternatives to car use [105]. Their occupations might allow for flexible work schedules or remote work options, reducing the need for car commuting [106]. On the other hand, older individuals may depend more on cars due to reduced mobility or challenges using alternative transportation modes [107]. Conversely, younger individuals are often more open to using alternative modes of transport, like public transit, biking, or carpooling, which is driven by their willingness to try different options and a desire to lessen their environmental impact. They are also more likely to live in urban areas with robust public transportation systems, reducing their need for cars [108].

3.2.2. Cost of Cars

Decisions about car ownership are often influenced by the cost of owning a car, encompassing both the initial purchase price and ongoing expenses, like fuel, maintenance, insurance, and taxes [109]. Research, however, has shown that car owners frequently underestimate the full costs of car ownership, which include both private costs (such as fuel and maintenance) and social costs (like environmental and health impacts) [110,111]. In an empirical study involving 6233 German car users, Andor et al. [110] found that while 88% of the participants claimed to understand their monthly car ownership and usage costs, about 50% substantially underestimated these costs, with an average underestimation of EUR 221 per month, or 52% of the actual cost. Based on these findings, Andor et al. [110] suggested that a more accurate understanding of the true cost of driving could lead to a significant reduction, approximately 37%, in car ownership rates.

Moreover, the costs associated with parking significantly affect car ownership rates [112]. Parking subsidies, such as employer-provided parking, can encourage individuals to drive alone since they are more likely to use their cars when parking is free or offered at a low cost [113,114,115,116]. For instance, in the USA, around 95% of commuters benefit from free parking [116]. While this arrangement is beneficial for employers, as it allows them to offer lower wages and save on payroll taxes while retaining employees, it also contributes to the expansion of urban areas by making car commuting more cost-effective [117].

3.2.3. Government Policies

From an economic perspective, government policies play a significant role in influencing car dependence by affecting the costs and benefits of various transportation options. These policies are instrumental in shaping the decision-making of individuals and households, thereby impacting the driving propensity within communities [118,119].

Policies that modify the costs associated with car ownership or usage, such as fuel taxes or incentives for electric or hybrid vehicles, can lead to substantial changes in the relative costs of different modes of transportation. Such measures often promote the use of more fuel-efficient vehicles or public transit, which can reduce the overall dependence on cars [120]. In addition, investment in public transit systems has significant economic implications for car reliance. Adequately funded and reliable public transit can present a more affordable transportation alternative, especially for individuals in lower-income brackets who may struggle with the costs related to car ownership [121]. Land-use policies are also crucial; those that encourage compact, pedestrian-friendly urban designs can diminish the need for cars, leading to reductions in transportation expenses [122]. Conversely, policies that promote urban sprawl tend to increase the reliance on personal vehicles, resulting in higher long-term transportation costs [123,124]. Moreover, regulations that target vehicle emissions can increase the costs of manufacturing and owning traditional gasoline-powered vehicles, encouraging a shift toward electric or hybrid vehicles, which are generally more economical to operate [125].

Additionally, government responses to recent global events have had a profound impact on driving behaviors. Specifically, the COVID-19 pandemic introduced unique challenges and policy measures that significantly altered transportation dynamics. A study that focused on the pandemic’s impact in European urban areas by Vega-Gonzalo et al. [126] noted an increased dependence on cars. This increase was particularly evident among demographics that traditionally relied less on private vehicles. The surge in car usage emerged as a direct response to government-imposed restrictions, changes in public transportation services, and heightened public concerns about health and safety. These changes took place against a backdrop of evolving socioeconomic conditions, highlighting the dynamic relationship between government policies and the factors that influence car usage and ownership.

3.3. Psychological Factors

In the existing literature on driving decisions, the focus has predominantly been on physical and economic factors, with less attention given to the roles of attitudes, perceptions, and preferences. As Handy [127] highlighted, the exploration of attitudinal theories in understanding travel behavior has not been as extensive as the examination of economic factors. Nonetheless, Anable [128] emphasized the importance of including a wider range of explanatory variables to comprehensively understand what influences an individual’s choice of transportation mode. This section begins with an overview of the primary theories in this area, followed by a detailed analysis of empirical studies that explore the psychological determinants shaping individual behaviors in the realm of transportation.

3.3.1. The Prevalent Theories

Understanding choice behavior, particularly in the context of how individuals select their mode of transportation, is crucial for analyzing driving propensity. Travel behavior analysis often employs behavioral theories derived from microeconomics, with rational choice theory being a notable approach. This theory asserts that individuals are capable of evaluating their options thoroughly and making optimal decisions based on comprehensive information [129,130]. As outlined by Lucas and Jones [131], rational choice theory fundamentally suggests that decision-making is driven by cost–benefit analyses of available alternatives, focusing on self-interest and informed deliberation. It presumes a stable context with fixed preferences, allowing individuals to process all relevant information to make the best decisions.

Rational choice theory’s widespread use in travel behavior research is largely due to its ability to be mathematically operationalized through discrete choice theory [132]. This framework supports policies that provide individuals with sufficient information to make well-informed transportation choices. Lucas and Jones [131] proposed that driving can be seen as a rational consumer choice, where vehicles contribute to well-being by facilitating access to various goods and services. However, the application of rational choice theory in analyzing driving behaviors has been criticized for overlooking human preference inconsistencies and not fully considering the utility concept, the maximization of decision-makers’ utility, or the processes leading to observed choices [133]. Therefore, to thoroughly understand driving behaviors, it is essential to consider not only sociodemographic and physical factors but also attitudes and behaviors [134]. Wu et al. [86] advocated for a broader definition of rationality that includes intangible factors related to car ownership and usage.

The emergence of alternative theories, mainly from social psychology—including self-perception theory [135], social learning theory [136], the theory of planned behavior [137], social cognitive theory [138], and social exchange theory [139]—has renewed interest in the complexities of travel behavior, including driving. A significant development in this area is the concept of automotive psychosocial satisfaction, addressing both functional and psychosocial needs, such as belongingness, self-esteem, and autonomy [131]. Jackson [132] noted two key insights from this research: first, cars are valued not only for their practical utility but also for their symbolic significance in people’s lives, and second, individuals often remain entrenched in their consumption patterns due to various factors, including habits, routines, social norms, expectations, cultural values, access inequalities, and limited choices.

3.3.2. Attitudes toward Car Ownership/Usage

Considerable research has been conducted on attitudes toward private car ownership and usage [140,141]. These studies, which utilized a variety of methodologies, focused on different demographic segments, including students [142,143,144,145], younger individuals [146,147], and various generational cohorts [104]. Additionally, some research compared attitudes toward car ownership and usage across different countries or cities [148,149], and others examined these attitudes in relation to alternative modes of transportation [108,148,150].

In the field of automotive psychology, Wu et al. [86] introduced the concept of “symbolic utility” to represent the psychological satisfaction derived from car ownership and usage. This influential study underscored the impact of attitudinal factors on car ownership and usage preferences, indicating that integrating symbolic utility into models could enhance their predictive accuracy. Building on this concept, Wright and Egan [151] applied Maslow’s hierarchy of needs [152] to argue that cars fulfill various psychological needs, such as shelter, security, warmth, and self-expression. Sheller [153] further expanded this discussion, suggesting that “automotive emotions” often surpass rational considerations for public welfare, moving beyond a simple economic cost–benefit analysis. More recently, Li et al. [154] investigated the effects of car ownership and usage on travel and life satisfaction. They found that owning multiple vehicles is associated with increased life satisfaction, which is a trend that is not observed in single-car ownership. The study also revealed that neither acquiring a more expensive vehicle nor increasing usage rates enhanced life satisfaction. Conversely, infrequent car usage was associated with higher levels of both travel and life satisfaction. Additionally, the study identified a positive relationship between attitudes toward the instrumental and affective roles of cars and the levels of satisfaction in travel and life among car owners.

Material possession theory [155] formed the basis of the research by Steg [108,140,156] on car ownership and usage. In their study, Steg et al. [156] aimed to identify the primary motivational dimensions that make car usage appealing and discovered that the instrumental and symbolic–affective functions of cars significantly contribute to their attractiveness. Building upon this, Steg [140] sought to empirically categorize various car usage motives and evaluate the extent to which Dittmar’s model is supported by empirical evidence. Their survey, which involved participants from the Dutch cities of Groningen and Rotterdam, showed that the respondents distinguished between the instrumental, symbolic, and affective motives fulfilled by cars. The study further found that even functional commuting is more influenced by symbolic and affective motives than instrumental ones. It also revealed a tendency among male participants, younger individuals, and those in lower-income groups to value the symbolic and affective functions of cars more highly.

In addition to the instrumental, affective, and symbolic aspects already recognized as crucial in understanding car ownership and usage, further motivators have been identified in various studies. For instance, Steg [140] highlighted “independence” as a separate motivator in her qualitative analysis. Likewise, Gatersleben [157] identified a relationship between feelings of independence and positive experiences associated with car usage. Contrasting with traditional car ownership, innovative models, like mobility as a service (MaaS), introduce new perspectives. Some research also integrated different psychological motivators into unified concepts. For example, Bergstad et al. [158] conducted a comprehensive analysis of statements related to car usage motivators and identified two interrelated motivational categories: “affective–symbolic” and “instrumental–independence”.

Additionally, the relationship between car ownership or usage and sociodemographic factors, such as age, income, and education level, was examined, with these factors being influenced by psychological motivators, including affective, symbolic, instrumental, and independence motivators. The study by Bergstad et al. [158] suggested that these psychological motivators could partially account for variations in car usage across different sociodemographic groups. For example, the affective–symbolic motivator was found to play a role in mediating the relationship between the frequency of car trips and gender, while the instrumental–independence motivator influenced the relationship between weekly car use and the overall extent of driving. These findings indicate that psychological factors significantly influence car usage preferences, though the direct impact of sociodemographic variables on car usage remains notable.

Van and Fujii [148] investigated attitudes toward cars and public transport in six Asian countries—Japan, Thailand, Vietnam, Indonesia, China, and the Philippines—with each one characterized by distinct cultural, developmental, and social norms. This research concentrated on three factors—symbolic, affective, and instrumental—which were previously identified as key in shaping attitudes toward cars and public transport. The study also introduced a new factor called “social orderliness”, which includes considerations of environmental friendliness, safety, quietness, and altruism associated with cars or public transport. The results show diverse perceptions of the symbolic and affective aspects of cars and public transport in these countries. For instance, individuals in lower-income societies tend to view cars as a more significant status symbol. Notably, the concept of social orderliness was particularly salient in the responses from Japan, indicating a possible perception of cars as less valuable due to societal concerns, like air pollution and congestion.

Complementing the existing research on attitudes toward car ownership and usage, several studies have underscored the significant role of social influence in decision-making processes. As described by Cialdini et al. [159] in the theory of normative action, car ownership decisions are influenced by two main types of social norms: descriptive norms, which reflect the prevalent behaviors among others, and injunctive norms, based on perceived expectations from others. The impact of social peers and neighbors on individual decisions regarding car ownership is further evidenced by the studies of Weinberger and Goetzke [160] and Weinberger and Goetzke [161] in the USA.

In contemporary research, it is increasingly recognized that cultural and psychological values associated with cars frequently overshadow the practical advantages of alternative modes of transportation, such as public transit [108,150,162,163]. For example, a survey by Linda [108] among Dutch respondents shows a strong preference for cars over public transportation, influenced by both their instrumental utility and symbolic importance. In a similar vein, research by Cullinane and Cullinane [164] in Hong Kong highlighted that practical aspects, such as convenience and comfort, are primary motivators for car ownership and usage, often taking precedence over considerations for public transport. These findings emphasize the predominance of car-centric values in shaping transportation preferences.

Beyond these discussed factors, several less-explored psychological elements play a crucial role in shaping driving decisions. For example, cars significantly contribute to the organization of daily life, providing solutions for individuals balancing demands from work, family, or childcare [165,166,167]. Stress and anxiety can also amplify reliance on personal transport, offering a sense of familiarity and control [168]. In certain situations, psychological conditions, such as anxiety disorders or phobias, may limit the feasibility of alternative modes of transportation, leading to increased dependency on cars [169]. Car usage often becomes habitual, with individuals preferring to drive for convenience, ease, or due to a lack of familiarity with other options [170]. Additionally, concerns about using public transit or cycling can further reinforce this dependency [171,172]. On the other hand, negative driving experiences could heighten future apprehensions about driving, potentially reducing reliance on cars [173].

4. Summary and Conclusions

Over recent decades, the global landscape of transportation has significantly evolved, marked by a substantial increase in car ownership and usage. This trend, which is predominantly attributed to the perceived convenience and preference for cars, is linked to their role in enhancing mobility, quality of life, and flexibility. However, this growing reliance on personal vehicles brings with it a spectrum of societal challenges, including environmental concerns, like climate change and air pollution; urban issues, such as traffic congestion and noise pollution; and health risks encompassing road safety and lifestyle-related diseases.

The imperative to understand the driving forces behind this shift toward increased car usage lies at the heart of both mitigating these negative externalities and fostering sustainable transportation systems. This comprehensive study delved into the diverse factors influencing individuals’ decisions to drive, categorizing them into three primary domains: built environment attributes, economic factors, and psychological influences. By conducting a thorough review of the literature, this research unravelled the intricate web of elements that shape driving behaviors, offering a nuanced understanding that is crucial for shaping transportation policies aimed at enhancing urban mobility and accessibility.

The built environment attributes, which are crucial in shaping driving propensity, encompass six key categories: density, diversity, design, destination accessibility, distance to transit, and demand management. Despite some discrepancies in the research findings, the consensus indicates that higher levels of these attributes generally correspond with reduced car ownership and usage. Specifically, denser urban environments, diverse land use, well-designed infrastructure, accessible destinations, and proximity to transit are associated with lower reliance on personal vehicles. Conversely, demand management strategies, particularly those addressing parking availability and cost, often lead to increased car ownership and usage, highlighting the significant influence of these built environment factors on transportation choices.

Regarding economic factors, three primary categories emerge as influential in driving propensity: socioeconomic characteristics, car ownership costs, and governmental policies. Among these, household income stands out as a pivotal determinant, with higher-income households typically exhibiting greater levels of car ownership and usage. Household composition also plays a critical role, with the presence of children, male-headed households, and the number of employed individuals in a household often linked to increased car ownership. Education level, which is inversely related to driving propensity, and age, with older individuals more inclined toward car usage, further influence the economic factors shaping driving behavior. Direct costs of car ownership, including acquisition and maintenance expenses, and indirect costs, like parking fees, significantly impact the decision to own and use a car. Government policies, such as fuel taxation, incentives for eco-friendly vehicles, public transit funding, and urban planning regulations, profoundly influence the degree of car dependence, demonstrating the interplay between economic considerations and transportation choices.

Psychological factors, although less explored compared with built environment attributes and economic factors, significantly influence car ownership and usage. These include life satisfaction; instrumental, affective, and symbolic values associated with vehicles; the sense of independence; social orderliness; and the impact of daily life structure, stress, and anxiety related to public transportation usage. Additionally, the pursuit of convenience and ease through car usage is a notable psychological driver. In contrast, negative driving experiences leading to increased fear of driving can reduce driving propensity. These psychological dimensions, which encompass both positive and negative aspects, underline the complex and multifaceted nature of factors influencing driving behavior, extending beyond mere physical and economic determinants.

In Figure 2, the hierarchical tree diagram graphically illustrates the findings from our literature review. Factors positively associated with driving propensity are marked in red, while those negatively associated are in blue (this color coding is indicative rather than absolute, as factors’ impacts can vary based on cultural and locational contexts). This review highlights the multifaceted nature of driving propensity, emphasizing the need for a holistic approach to understand and address its drivers. A comprehensive understanding of these factors’ interplay is crucial for developing effective interventions to mitigate car use’s negative externalities, promote sustainable transportation, and enhance urban mobility and accessibility.

This study provides a structured framework for policymakers, outlining various factors influencing driving propensity and illustrating potential trade-offs. To this end, it emphasizes the necessity of balancing sustainable transportation advancement with community and individual quality of life enhancements. A multifaceted approach is essential to mitigate excessive driving’s harmful effects. This approach could encompass designing compact, well-connected communities with efficient public transportation systems, promoting electric and hybrid vehicles through regulations and incentives, and investing in public transit infrastructure. Additionally, increasing public awareness of driving’s negative impacts and promoting alternative transportation methods, such as carpooling, cycling, and public transit, can reduce personal vehicle dependency. Addressing psychological drivers of behavior is crucial, necessitating an examination of psychosocial constructs and negative driving experiences. Enhancing life satisfaction, alleviating stress and anxiety linked to public transportation, and improving alternative transportation modes’ accessibility and convenience are vital.

While this review aimed to provide a comprehensive analysis, its scope was inevitably limited by potential omissions due to search term and database constraints, which possibly affected the included perspectives’ diversity. The primary focus on urban settings in developed countries might limit the applicability to rural or developing areas with distinct socioeconomic and infrastructural dynamics. Moreover, rapid advancements in transportation technologies, like electric vehicles, may quickly date some findings. The review’s quantitative emphasis might overlook qualitative insights, revealing deeper individual motivations and context-specific driving behaviors. Future research should bridge these gaps by investigating diverse geographical contexts and assessing emerging technologies’ impacts on driving patterns. Delving into psychological and sociocultural dimensions of driving, examining transportation’s intersection with critical issues like climate change and public health, and conducting longitudinal studies are imperative for understanding driving behaviors’ evolution in response to policy changes and urban development.