Car Ownership Research Articles

Navigating Urban Mobility: The Relationship between Car Consumption and Public Transport Usage in Malaysia

Increasing private-owned cars in Malaysia has, thus far, contributed to growing congestion, environmental pollution, and decreasing public transport utilization. Car ownership has become possible through economic growth, and convenience and status attached to cars triggered tendencies from shifting to public transit. This development contradicts investments in public transport and aggravates environmental problems. This paper examines the relationship between private car ownership and public transport usage in Malaysia from 2000 to 2022, concentrating on intercity bus services, KTM commuters, and light rail transit. The results indicate that with increased riders for intercity buses and KTM commuter services, car usage drops, while that of light rail may raise car ownership. The study emphasizes the need to upgrade intercity and KTM services to mitigate the pressures of car ownership and calls for further research into the role of light rail.

Sustainable Horizons: Exploring Attitudes and Beliefs on Electric Aircraft Usage in General Aviation

This research was conducted to better understand the perceptions surrounding electrically powered aircraft in the general aviation (GA) sector. The study employed survey methodologies to collect responses from ( N = 780) participants, with an emphasis on data from collegiate aviation students as well as a broad subset of the GA community. The study focused on factors such as generational differences, gender, the impact of flight training, and prior exposure to electric or hybrid cars, and how these factors influence the aviation industry’s sustainable transformation. The results indicate a significant and positive correlation between attitudes toward electric aircraft and prior exposure to or ownership of electric or hybrid cars. Generational differences, gender-based variations, and the impact of flight training on willingness to pay for electric aircraft utilization also had significant relationships with attitudes toward electric aircraft. The findings provide valuable insights for industry stakeholders and policymakers in promoting sustainable aviation practices, and complements prior research into the feasibility of electric flight.

Effects of COVID-19 on flex route transit utilization: An interrupted time series analysis

The COVID-19 outbreak has exerted serious effects on the transportation system, especially public transport. Flex route transit (FRT), one of public transport, has shown significant advantages in transporting passengers during COVID-19 due to its high flexibility and relatively large cabin spaces. This study aims to estimate the effects of the lockdown and first lockdown ease on FRT utilization through interrupted time series analysis. A survey was conducted in Beijing to collect data regarding the daily FRT utilization of individuals, spanning from December 2019 to May 2020. Results indicated that FRT utilization was immediately reduced during the lockdown, and a decreased trend was observed. After the first lockdown ease was rolled out, FRT utilization showed a rising level and trend. Private car owners showed a lower dependence on FRT than non-car owners. The level of income exerted varying effects in a wide range of periods. FRT utilization among the higher income group was more likely to take effect than the lower income group. The study yields various policy implications for FRT operations during COVID-19 or other pandemics.

Key influences on residential photovoltaic solar panel adoption in the United States

ABSTRACT The prevalence of low-density residential development to host solar PV, the ubiquity of solar irradiation, and incentivizing policies have created substantial opportunities for homeowners in the United States. Federal, state, and municipal governments implement regulatory policies and financial incentives to promote photovoltaic solar systems in residential buildings with limited resources. Thus, understanding the main factors and their spatial variations is vital for expanding the accessibility of renewable energy benefits to specific socio-economic groups and submarkets. Our research includes linear regression, principal component regression, and spatial error models to provide empirical evidence for the relationship between the adoption rates and socio-economic, geographical, and technical factors while identifying characteristics of adopter groups. The results suggest that the relative advantage factors – electricity prices and solar irradiation – play the most significant role across all regions and market segmentations. Statewide policy indicators are the second most significant factor, followed by socio-economic variables on employment status, remote working, car ownership, and property value. Our results indicate that homeowners do not only differ in their circumstances but also in their motivations. This highlights a need for policy-makers and the solar industry to place greater emphasis on distinct characteristics of consumers motivated by ideology and consumers motivated by economic benefits.

A prospect theoretical choice model incorporating profitable and punctual uncertainties: an investigation in the participation of private car sharing

This study aims to examine people’s propensity to share private cars between peers accounting for the potential impacts of uncertainties underlying the sharing profit and vehicle returns. A mixed logit prospect theoretical model is developed to estimate the effects of uncertain variables and examine the unobserved heterogeneity using the data collected through a stated choice experiment in Hiroshima City, Japan. Results show that car owners are generally sensitive to the gains and losses for the revenue and the time of delayed returns, and people respond to uncertain situations differently. Individual participation in private car sharing is significantly influenced by the choices made by different social group members like family/relatives and friends/colleagues. These insights on individuals participation mechanism are helpful for service operators to promote the market share of private car sharing.

“I always say, it’s the icing on the cake”: the discursive production of fare-free public transport in Luxembourg

ABSTRACT Although the policy of fare-free public transport (FFPT) continues to gain popularity across diverse geographical contexts, the case of Luxembourg is unique in this global landscape. It is the first nation-state to have universally abolished fares, in a society characterized by high incomes and GDP, and a particularly high car ownership rate. In this paper, we analyze the evolution of political discourses on FFPT in Luxembourg surrounding the introduction of the policy. Using qualitative methods, we reconstruct the decision-making process and examine the competing ideologies that shaped it. We find that the idea germinated on the margins of the political spectrum, but was adopted by those in power because of the unique opportunity to combine territorial marketing with a form of socially-inclusive and sustainable policy that would not harm the widely valued car-based transport system. Hence, this process demonstrates both the post-political aspects of FFPT, illustrated by how its introduction helped to marginalize critical voices around fare abolition, and its political dimension related to the public debate and the contestation that FFPT engendered.

Exploring disparities and similarities in daily travel mode choices between electric vehicle owners and internal combustion engine vehicle owners

ABSTRACT This study proposes a novel instrument for measuring private car owners’ travel-related lifestyle (PCTRL) based on a survey (N = 2045) conducted in China’s four first-tier cities and ten quasi first-tier cities. By employing principal component analysis (PCA) and cluster analysis, the study classifies electric vhehicle (EV) owners and internal combustion engine vehicle (ICEV) owners into five distinct PCTRL segments, respectively. The findings reveal that only 66% of EV owners have similar PCTRLs to 67% of ICEV owners, with the most notable disparity being the higher likelihood of EV owners choosing private cars for their daily commutes. Furthermore, a random forest model was developed to predict the daily activity-based travel mode choices behavior of both EV and ICEV owners. The findings offer valuable insights into understanding the behavior of EV and ICEV owners in their daily travel mode choices and help formulating more effective traffic management strategies.

Assessing Trends of Vehicle Ownership and Use Among Tertiary Students in Kwame Nkrumah University of Science and Technology Campus, Ghana

The study aims to assess trends of car ownership and usage among tertiary students on campus, with an emphasis on Kwame Nkrumah University of Science and Technology (KNUST). The research design employed in this study was descriptive survey, aiming to gather information from a sample population through their responses to specific questions. Data analysis was conducted using descriptive statistics, summarizing the responses quantitatively through bar charts, pie charts, frequencies, percentages as well as means and tables, created using Microsoft Excel. The results revealed that, a majority (70 percent) of the cars being used by students on the KNUST campus are owned by Ghanaian students with foreign students making up the minority of this number. The major challenges faced by students who own or use vehicles on campus are a lack of filling stations on and around campus, lack of student parking lots, and excess traffic. From the findings, it can be concluded that, students' psychological make-up is a major determinant of vehicle ownership among students. Moreover, the findings further revealed that most of the students who have vehicles on the campus exhibit or develop mostly negative habits after vehicle ownership and this in the long run may have negative effects on academic performance which is the sole purpose of being on campus. The study provides insights into the environmental and health impacts of increased vehicle use on university campuses, offering valuable insights for policy decisions, future research, and campus management, thereby enhancing student mobility.

Take a hike: Spatializing allemannsretten and transportation accessibility for outdoor recreation in the Greater Stavanger Region, Norway

ABSTRACT A special connection between people and their environment is legally recognized in Norway as Allemannsretten, the right to enjoy a large part of Norwegian nature. Scholarship on leisure mobility, spatial planning, and transport geography recognizes the intrinsic value of this spatial connection. As people travel for outdoor recreation, equitable access to recreation is a goal for just transport systems and must be achieved as these systems are digitalized and electrified for low-carbon transitions. The aim of the article is to identify the impact of transportation accessibility on outdoor recreation habits in the Greater Stavanger Region, Norway’s third-largest metropolitan region. With car-centric development since the 1970s and ambitious automobility-reduction targets, transportation accessibility for outdoor recreation is a key indicator of challenges to overcome in mobility transitions, yet features marginally in public debate. In focusing on popular outdoor day trips and based on multisited interviews with both car owners and non-owners, the authors identify oversights in accessibility, spatializing the issue of local destinations in relation to urban transport transition. They conclude that policymakers must address specific gaps to make transport systems desirable and inclusive, and that a spatial lens can be used to problematize and advance just low-carbon transitions.

A machine learning comparison of transportation mode changes from high-speed railway promotion in Thailand

Thailand's collaboration with China to develop High-Speed Rail (HSR) represents a crucial step in enhancing transportation infrastructure and promoting regional economic growth. While most research has focused on existing travel modes such as cars, buses, and planes, there is a notable lack of analysis on future transport choices, particularly in developing countries like Thailand. This study addresses this gap by analyzing the factors influencing High-Speed Railways adoption to inform future traveler decisions. The comparison of models for predicting High-Speed Railways usage revealed that CatBoost consistently outperformed the other models, with cross-validation confirming its superior performance across all key metrics. The Binary Logit Model (BL) demonstrated moderate effectiveness, achieving an accuracy of 0.7404 ± 0.006, sensitivity of 0.7655 ± 0.006, and a relatively lower AUC of 0.8161 ± 0.0067. Its specificity (0.7140 ± 0.0129), precision (0.7376 ± 0.0085), and F1 score (0.7513 ± 0.0055) were moderate, but it struggled with handling imbalanced data. In contrast, XGBoost delivered significantly stronger results, with an accuracy of 0.8846 ± 0.0068, sensitivity of 0.9210 ± 0.0094, and an AUC of 0.9583 ± 0.0036. XGBoost also achieved high specificity (0.8464 ± 0.0062), precision (0.8630 ± 0.0055), and an F1 score (0.8910 ± 0.0067). LightGBM also performed well, achieving an accuracy of 0.8763 ± 0.0077 and a relatively high sensitivity of 0.9358 ± 0.0071. However, it exhibited lower specificity (0.8139 ± 0.0107) and precision (0.8408 ± 0.0083), resulting in a slightly reduced F1 score of 0.8857 ± 0.0069 and an AUC of 0.9506 ± 0.0034. CatBoost demonstrated the highest overall performance, achieving an accuracy of 0.8853 ± 0.0061, sensitivity of 0.9245 ± 0.0072, specificity of 0.8441 ± 0.0068, precision of 0.8616 ± 0.0058, an F1 score of 0.8920 ± 0.0058, and an AUC of 0.9584 ± 0.0034. The decision to use High-Speed Railways in Thailand is influenced by several key factors that reflect the country's unique transportation context. These factors include travel time, access time, service frequency, cost, waiting time, household income, automobile ownership, gender, and the purpose of travel. This information is valuable for shaping policies to support High-Speed Railways adoption in Thailand.

Traffic Flow Prediction: A Method Using Bagging-Based Ensemble Learning Model

For the development of the national economy, transportation is strategically significant. As the increasing ownership of automobiles, traffic jams are a common occurrence. Accurate prediction of traffic flow contributes to diverting traffic effectively and improving the quality of urban traffic, in turn improving the operation of the overall transportation system. The rapid development of artificial intelligence technologies, especially machine learning and deep learning, has provided effective methods for accurate prediction of traffic flow. Based on the above, in order to improve the accuracy of the prediction and to extend the application of machine learning and deep learning in the prediction of traffic flow, this study proposed a bagging-based ensemble learning model. Firstly, normalization method is used to preprocess the data. Subsequently, base prediction models including decision tree, random forest, logistic regression, convolution neural network, long short-term memory and multilayer perceptron are selected for training the prediction model, respectively. Finally, bagging-based ensemble learning method is used to integrate these base prediction models to further predict traffic flow. The results of comparison between the single base prediction models and the bagging-based ensemble learning model on the five evaluation indicators show that, for predicting the traffic flow, the bagging-based ensemble learning model outperforms the base prediction models. Meanwhile, this study explores the potential in the application of machine learning, deep learning, and especially bagging-based ensemble learning to predict traffic flow.

Travel mode choice behavior analysis using multinomial logit models towards creating sustainable college campus: a case study of the University of Toledo, Ohio

By employing descriptive statistics and multinomial logistic regression models, the paper investigates factors that affect the travel mode choice behavior of a college community for both commuting to the campus from their residences and commuting on campus from one facility to another. Results show that car ownership, travel distance, age, gender, academic status, income, perception of safety, number of weekly trips, and weather conditions affect people’s mode choice decisions significantly. The study finds that increasing travel distance, age, and being a staff member positively impact car mode choice, faculty members are more likely to bike and walk compared to undergraduate students, and unlike the authors’ predictions, the higher-income people are more likely to walk on campus. Results also reveal that car users change travel modes more often than active transport mode users based on semesters and that although women are less likely to bike to the campus, they are more likely to walk and bike on campus. If the number of trips increases, people prefer cars to walk, bike, and ride a bus to commute to the campus while they prefer walking to driving a car on campus. The study contributes to help create pollution-free and healthy urban college campuses that, on larger scales, will further contribute to developing sustainable cities to achieve the Sustainable Development Goals as described by the United Nations, particularly goal number 11, “to make cities and human settlements inclusive, safe, resilient and sustainable,” and thereby contribute to improving urban living conditions.

But can it drive to Lapland? A comparison of electric vehicle owners with the general population for identification of attitudes, concerns and barriers related to electric vehicle adoption in Finland

Transportation is a major source of greenhouse gas emissions with private car use accounting for a large proportion of those emissions. Battery powered electric vehicles (EV) are a currently available technology that has the potential to significantly reduce emissions from car use. Therefore, promoting a shift from internal combustion engine vehicles to EVs should be supported by policy makers aiming to curtail emissions from car use. To design policies and interventions to promote EV adoption, information is needed on how people perceive electric vehicles and what are the real and perceived barriers preventing their adoption. With this objective in mind, it was investigated what the current EV owners in Finland are like and how they compare with the general population. We also endeavored to identify real as well as potentially misperceived barriers to EV adoption and factors affecting EV purchasing intentions. The study utilizes a sample of 3857 participants, including 141 electric car owners, who answered a questionnaire investigating lifestyle, car use, environmental and political attitudes as well as household carbon footprint and attitudes towards EVs. EV owners in Finland have more financial security, own more cars, drive more, and have a larger average household carbon footprint than the general population. There are two distinct subgroups among electric car owners, one group being interested in environmental impact of their car use and the other focused on the performance of their cars. People without experience of EVs generally have negative attitudes towards electric cars and also maintain some misconceptions about EVs. Personal experience with electric vehicles is associated with a positive outlook on these types of vehicles and is the strongest predictor of EV purchasing intentions.

Forecasting CO2 Emissions from Libya’s Transport Sector

This paper presents an innovative approach to forecast carbon dioxide (CO2) emissions from the transport sector in Libya. The method combines machine learning algorithms with historical data and future estimates. The research built a model that took into account factors such as population growth, rates of car ownership, patterns of fuel consumption and government regulations in order to provide an accurate forecast of carbon dioxide (CO2) emissions over the next decade based on the Global Change Assessment Model (GCAM). The authors used a variety of statistical time series models to forecast future CO2 emissions from Libya's transportation sector. These models included the exponential smoothing model (ESM) and the autoregressive integrated moving average (ARIMA). The ARIMA model outperformed the ESM model, achieving an R2 of 0.931 and a root mean square error (RMSE) of 1.040 Mt CO2. The results of the study found that CO2 emissions from Libya's transport sector could increase by 27.98% and 57.99% in 2030 and 2050, respectively. The study proposed six transportation theories to reduce CO2 emissions from Africa's and Libya's transport sectors. The identified factors encompass price systems, land use planning, eco-driving, electric automobiles, bicycle infrastructure, and telecommuting. The authors also examined the needs to reduce CO2 emissions from Libya’s transport sector in order to meet the International Energy Agency’s ambitious targets for reducing CO2 emissions from the global transport sector. These needs arise due to increasing urbanization, population growth, underinvestment in public transportation infrastructure, and the increasing incidence and severity of heat waves. Additionally, hypothetical scenarios are presented to demonstrate the importance of further reducing CO2 emissions from these sectors to match the projections of global change assessment models.

Decoding Jakarta Women’s Non-Working Travel-Mode Choice: Insights from Interpretable Machine-Learning Models

Using survey data from three dwelling types in Jakarta, we examine how dwelling type, socioeconomic identity, and commuting distance affect women’s travel-mode choices and motivations behind women’s choices for nearby and distant non-working trips. We compared the performance of the multinomial logit (MNL) model with two machine-learning classifiers, random forest (RF) and XGBoost, using Shapley additive explanations (SHAP) for interpretation. The models’ efficacy varies across different datasets, with XGBoost mostly outperforming other models. The women’s preferred commuting modes varied by dwelling type and trip purpose, but their motives for choosing the nearest activity were similar. Over half of the women rely on private motorized vehicles, with women living in the gated community heavily relying on private cars. For nearby shopping trips, low income and young age discourage women in urban villages (kampungs) and apartment complexes from walking. Women living in gated communities often choose private cars to fulfill household responsibilities, enabling them to access distant options. For nearby leisure, longer commutes discourage walking except for residents of apartment complexes. Car ownership and household responsibilities increase private car use for distant options. SHAP analysis offers practitioners insights into identifying key variables affecting travel-mode choice to design effective targeted interventions that address women’s mobility needs.

Who is ready to live a car-independent lifestyle? A latent class cluster analysis of attitudes towards car ownership and usage

An excessive use of private cars for transportation has multiple negative effects on our society. It is therefore paramount to identify the underlying factors driving car usage among different segments of travellers. In this study, we aim to identify and characterise traveller segments in terms of their car-related attitudes and how different sociodemographic attributes, travel behaviour characteristics (such as using cars as the primary mode of transportation), and the accessibility of their place of residence vary amongst different population segments. Through Confirmatory Factor Analysis and Latent Profile Analysis we identify five different classes, namely car-detractors, −hesitants, −positives, −friends, and −lovers. More favourable car attitudes are associated with higher car ownership and access but, interestingly, there are no significant differences in terms of travel frequency and trip length distribution. This suggests that attitudinal differences relate mostly to modal preference rather than to the underlying travel demand patterns and the associated activities. We also see that car-less individuals are more likely to be car detractors, suggesting that the experiencing barriers for owning a car may contribute to developing more negative attitudes towards cars. Our results shed light on the different influencing factors and characteristics of individuals that consider, or not, a car-centred mobility.

Exploring the factors hindering the intention to adopt sustainable transportation options in Addis Ababa, Ethiopia: using structural equation modeling

With poor transportation infrastructure and increased reliance on motorization, fast-growing cities in the Global South that are experiencing rapid urbanization have received less attention in terms of sustainable transportation research. In this regard, it is imperative to support active mobility and public transit that help to reduce vehicle emissions, traffic congestion, road fatalities, and energy depletion while supporting public health, robust economies, and social equity. However, in the emerging cities context, the adoption of sustainable transportation options are constrained by various factors. This study aims to explore the key factors hindering the adoption of sustainable transportation options in Addis Ababa, Ethiopia. To do so, travel survey data were gathered from 720 households using stratified sampling and analyzed using factor analysis. The results from factor analysis indicated five groups of factors hindering residents’ intention to adopt sustainable transportation options, including (1) infrastructure condition, (2) modal characteristics, (3) land use pattern, (4) policy framework, and (5) technology-related factors. The confirmatory factor analysis also depicts the five latent factors and the contributions of observed variables that are statistically significant (p < 0.01). More specifically, observed variables such as (i) inadequate pedestrian facilities, (ii) lack of bike facilities, (iii) poor public transit facilities, (iv) limited availability of reliable public transport, (v) long wait times, (vi) high public transport fare, (vii) discomfort, (viii) safety and security issues are the most significant factors. Another set of factors includes (ix) walking distance, (x) dispersed land use pattern, (xi) poor land use mix, (xii) inadequate policy support, and (xiii) lack of real-time information. Furthermore, the intention to adopt sustainable modes of transportation is influenced by socio-demographic characteristics such as gender, age, educational status, income, family size, and car ownership. The result contributes to the existing literature by providing insight into adopting sustainable transportation modes in emerging cities like Addis Ababa, which are characterized by several urban transportation challenges.

Modeling of Applying Road Pricing to Airport Highway Using VISUM Software in Jordan

Road congestion in Amman City has been increasing yearly, due to the increase in private car ownership and traffic volumes. This study aims to (a) evaluate the toll road’s effects on society and the economy in Amman, Jordan, through a survey questionnaire using statistical software (SPSS), (b) assess the impact of the toll road on reducing congestion and delays using micro-simulation (VISUM), (c) identify the optimal toll price for a selected road using VISUM and (d) validate the simulated models with the optimal revenue. Traffic, geometric, and cost data about the toll technique of two sections on the Airport Highway (from the Ministry of Foreign Affairs to the Madaba Interchange; and from the Madaba Interchange to the Queen Alia International Airport (QAIA) Interchange) were used for simulation purposes. The toll road (across seven different scenarios at different prices) was evaluated for optimal revenue. The survey questionnaire was made based on all scenarios, including the AM peak hour. The operation cost for the toll road was determined based on the Greater Amman Municipality (GAM). The best scenario was determined based on the value of revenue (JOD). The results indicate that higher acceptance is achieved when applying road pricing during the AM peak hour and that users prefer the charging method based on travelled distance (54.02%). Additionally, the total cost of the manual toll collection (MTC) method is 126,935 JOD. Road pricing can reduce traffic delay (or speed up traffic flow) by 4.61 min in the southbound direction and by 9.52 min in the northbound direction. The optimal toll value is 0.25 JOD (34.08%), with revenues of 1089.6 JOD for 2024 and 1122.6 JOD for 2025. Eventually, applying road pricing on the airport road is shown to be effective and economically feasible only when using the manual method.

Smart Transportation Systems: IOT-enabled Traffic Management & Vehicle-to-infrastructure Communication

Recently, there has been a substantial increase in the number of cars on the road. Escalating traffic congestion is a widespread problem that folks face on a daily basis. The manual control of traffic-by-traffic police has shown to be ineffective. “Furthermore, the signal's set duration has failed to effectively address this issue, regardless of the level of traffic congestion. This research presents a communication system called Vehicle-to-Blockchain (V2B), which utilizes blockchain technology to enable transparent and decentralized communications. The aim is to optimize the incorporation of blockchain technology into V2X (Vehicle-to-Everything) and IoT (Internet of Things) systems, therefore enhancing the efficiency and effectiveness of future transportation systems. We present a range of advanced applications for blockchain technology, such as a car ownership system that utilizes the multi-token standard, a vehicle grading system, the integration of blockchain with the Internet of Things (IoT), and a decentralized ticket administration system for transportation services. The design places a high emphasis on essential elements such as the seamless integration of data, precise precision, and the establishment of secure connections. Furthermore, it includes a decentralized payment system and marketplace for transportation inside smart cities. Our main focus is on the technical execution of smart contracts in these particular scenarios, highlighting their importance in guaranteeing strong and dependable interactions. Our decentralized approach creates a transportation ecosystem that has the ability to adjust and fulfill the evolving requirements of intelligent urban areas. It is groundbreaking, durable, and capable

Accessibility, (dis)advantage and everyday mobility practices and experiences: the cases of Maputo and Freetown

ABSTRACT This paper deconstructs everyday mobility practices from a perspective of accessibility and social and transport (dis)advantage in two capital port cities in sub-Saharan Africa (SSA) – Freetown, Sierra Leone and Maputo, Mozambique. The paper proposes a novel framework that interprets social and transport (dis)advantage conditions as critical drivers of mobility behaviours and the resulting (in)accessibility that circumscribes individuals’ experience. Building on its framework, the paper raises unique empirical qualitative evidence from eight neighbourhoods about mobility practices across populations facing different degrees of social and transport (dis)advantage. The study’s design is grounded in the in-depth understanding of accessibility using qualitative methods as a counterpoint to predominant quantitative and spatial approaches, filling critical data gaps in cities in SSA. The findings reveal a deep-seated aspiration for car ownership, tempered by the urgency of other immediate material needs, as well as the dynamic nature of travel behaviour due to changing conditions during the day and throughout the year. The findings also indicate informality plays a significant role in enabling access to critical opportunities and the need to strengthen trust in popular transport systems.