Car Choice Research Articles

Company cars and household car choices

Tax systems that favour company cars for personal use could cause households to have more cars. It could also affect the choice of fuel type. We investigate the relationship between household car choices and access to a company car through a difference-in-differences design using Norwegian microdata. We find that access to a company car is associated with an increase in the total number of cars and the number of combustion-engine cars. For electric cars, the results are inconclusive. However, wage growth and access to company cars are also positively correlated. Therefore, we cannot interpret the difference in the number of cars between the treatment and control groups as a causal effect of the company car scheme, but as a correlation. Still, existing evidence on the income elasticity of car demand suggests that the increase in the number of cars is unlikely to be driven by wage growth alone.

Behavioural modelling of metro car choice

Overcrowding in metro systems can exert a negative impact on the travel experience of the passengers and on the system’s capacity. This can affect passengers in different ways, worsening their perception of safety, altering their behaviour, decreasing their satisfaction, and leading to higher travel-time sensitivities. One of the causes of overcrowding can be attributed to some metro cars being selected more often than others, and thus, the distribution of passengers inside the train not being homogeneous. This study adopts a disaggregated approach based on the observed behaviour to understand why some specific metro cars are preferred. The characteristics of passengers and metro cars that generate heterogeneity in the load profiles are identified and the motives behind passenger choices are understood by formulating a hybrid latent-class choice model. The behavioural models consider sociodemographic characteristics, level-of-service information, and attitudinal latent variables. Once a metro car choice model was obtained, an operational micro-simulator of the metro was programmed to evaluate how passengers' choices would be affected by altering variables related to the design of the stations, assessing it based on the current situation. Subsequently, interventions to station layouts are made based on the forecast of the behavioural models, to affect passenger choices and produce more homogeneous passenger distributions in the train. The intervention presented in this study reduced 46.49 h of total travel time during the morning peak hours; the impact was greater when considering individual perceptions. Incentives can be developed for optimising the distribution of passengers in metro cars by understanding passenger choice using a behavioural approach, and therefore, they can improve the passenger experience.

Perceptions towards autonomous vehicle acceptance: Information mining from Self-Organizing Maps and Random Forests

The present research investigates a range of factors affecting autonomous vehicle (AV) acceptance of Greek citizens through a questionnaire distributed to 563 respondents. Following the extraction of descriptive statistics, self-organizing maps (SOMs) were employed to meaningfully categorize and aggregate questions pertaining to four main pillars of the questionnaire, which are conceptually relevant namely: (i) how several factors affect general car choices of respondents, (ii) what the respondents perceived that AVs would offer, (iii) how much they agreed with stated expected technology and efficiency-oriented AV traits and (iv) how they believe several factors affect driving behavior overall. A Random Forest (RF) algorithm was applied to classify the AV acceptance decisions of a training subset of the respondents, and was subsequently assessed on a test subset. SOM results indicate that participants can be meaningfully separated into two SOM cluster groups for pillars (i), (ii) and (iv), while pillar (iii) yielded separations into three SOM cluster groups. RF feature importance calculation indicated a number of affecting variables; the five most contributing ones are: distance covering capabilities of AVs was a major factor affecting acceptance decisions, followed (by a wide margin) by responder opinions on whether the principles and conscience of drivers can be replaced by an AI navigator without reducing safety levels, while the algorithm itself conducted successful classification to about 80% of test cases. Present results can be used to anticipate AV penetration levels based on sample characteristics and to improve AV traits in cases where higher AV penetration is sought.

A simultaneous model of residential location, mobility tool ownership and mode choice using latent variables

This paper investigates the interplay between three key choice dimensions in transport modeling: (i) Residential location, (ii) mobility tool (e.g., car and/or public transport season ticket) ownership and (iii) mode choice. We use a unique panel dataset of recently moved Swiss respondents to better assess the impact of changes in the respective dimensions. An expected trend is that the more accessible the residential location, the more likely the respondent owns a public transport season ticket, and the more likely public transport rather than a car is chosen. However, our model allows to disentangle the decision process and provides a more comprehensive picture of respondents’ behavior. For example, our simple mode choice model shows a strong and positive effect of income on the choice of car. In the simultaneous approach, given respondents’ increase in car ownership for increasing income, this effect becomes even more substantial if the residential location is assumed to be constant. With the latter assumption lifted, however, the effect decreases again, since respondents with higher income tend to live in areas with higher accessibility, lowering their car usage. An important general finding is that if the longer-term decisions (i.e., the choice of residential location and mobility tools) are considered, the short-term effects of level-of-service attributes (such as travel time) on mode choice is substantially weakened by more than 20%.

Car choice determinants in Italy and Norway: A comparison based on revealed and stated choices

Norway is the leading country in electric car adoption in the world, while in Italy electric cars are only recently gaining acceptance. We compared car choices in the two countries highlighting commonalities and differences in the choice determinants, distinguishing between the small and the large car segment. We analyzed actual choices made in the real-world conditions and stated choices under hypothetical scenarios. The comparison between the preference structures of the two countries shows important differences when the revealed preference dataset is analyzed, while the differences are much reduced with the stated preference dataset. All in all, we feel that the two countries present only differences associated with longer car driving habits of the Norwegian drivers, the higher percentage of large cars in Norway, and the more developed public charging infrastructure. Since the supply of cars is quite similar, such a consideration leads us to believe that the huge discrepancy in electric car uptake is mainly due to the different car policies adopted in the two countries. The evolution of the policy setting and of the technology will determine whether Italy will follow the Norwegian model of gradual BEV uptake.

Sistem Informasi Rental Mobil Pada Regulator Rent Car

Car rental is an activity of renting a vehicle in the form of a car with the terms and conditions determined by the rental party. Every time a rental transaction is made, the Car Rent Regulator always records data on each customer and transactions made in writing. In addition, the rental party also cannot determine the choice of the best available car. This study aims to build an information system that can be applied to Car Rent Regulators, so that the transaction process can be carried out more quickly and reduce the risk of data damage. This system will apply the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to determine the choice of the best car available at the rental. System information is created using the Java programming language and MySQL as a database. In designing the system, the Unified Modeling Language (UML) model is used to determine the structure and processes that occur in the system. The results of this study can facilitate and improve admin performance in conducting rental transactions, and calculations using the TOPSIS information system method can determine the choice of the best available car by entering values in the specified criteria. The results of calculations using the TOPSIS method show that the Avanza car is the best car choice available for rental.

The Impacts of Low-Carbon Incentives and Carbon-Reduction Awareness on Airport Ground Access Mode Choice under Travel Time Uncertainty: A Hybrid CPT-MNL Model

Developing strategies to incentivize travelers towards adopting sustainable mobility options is one of the effective approaches to mitigate carbon emissions. Using Xi’an Xianyang International Airport as a case study, this study aims to explore the effects of low-carbon incentives and carbon-reduction awareness on airport ground access mode choices. In addition, to account for the complex road environment, an innovative stated preference choice experiment was designed, integrating the factor of travel time uncertainty. Then, a hybrid cumulative prospect theory–Multinomial Logit (CPT-MNL) model was also developed. The estimated results revealed that travelers increasingly prioritize emissions reduction and consciously prefer sustainable mobility options to reach the airport. Furthermore, the potential of low-carbon incentives to encourage public transport usage over private vehicles has been highlighted. Notably, travel time uncertainty had a significant impact on the choice of private cars. When the travel time to the airport is uncertain, travelers exhibit a greater inclination towards selecting public transport. The findings of this study offer nuanced insights for transportation authorities, aiding them in fostering the adoption of sustainable mobility options and achieving carbon reduction objectives.

A METHOD FOR ASSESSING ENERGY EFFICIENCY OF LIGHT-DUTY VEHICLES TAKING INTO ACCOUNT CHANGES IN THEIR DESIGN PARAMETERS

The present stage of development of the world market of vehicles, in particular, light-duty vehicles (LDVs) is marked by an increasing variety of types and designs. Therefore, while validating or renewing fleet vehicle of an enterprise, carriers are faced with the choice of cars setting conditions they can assess using existing fragmentary and cost-effective approaches. However, the latter ones do not meet the concepts of saving energy and resources. Therefore, the task of choosing an efficient car is complex requiring a science-based formation of demands and their satisfaction. Existing approaches to the selection and validation of LDVs are simplified and imperfect in terms of transport technology. They do not take into account a) resource and technical properties of LDVs as a means of transport; b) main design and technical characteristics of LDVs; c) parameters of its operation (running surface, traffic density, length of the driving cycle, etc.). This article considers a method for assessing energy efficiency of light-duty vehicles, which is based on the scheme of the vehicle operation as a dynamic tool. To develop this method, the provisions of the energy and resource efficiency theory of a car have been used, namely the calculation schemes of transport operations based on models of reference prototypes and test operations. These models are able to ensure the reasonable use of the vehicle fleet with design novelty under the given operating conditions. In order to analyse changes in the parameters of new vehicles and improve the parameters of transport technology, a universal design of a light-duty vehicle of a generic type has been developed and considered. This structural pattern covers all options of the vehicle’s design basis. New options for the vehicle design are created by changing the number of design modules and their parameters. The method proposed for consideration ensures the technical and technological properties of light-duty vehicles in accordance with the concept of energy and resource conservation in the transport system. The paper develops methods for comprehensive improvement of transport energy efficiency and formation of requirements for transportation projects of LDVs taking into account technical and technological factors within their life cycle.

Fiscal policies and car choices in Italy and Norway: A scenario analysis based on a stated-preference survey

Norwegian and Italian car drivers make very different car choices. This paper investigates the influence of fiscal policies on car buyers’ choices, using data collected from a stated preference survey conducted in 2021. After estimating a joint random parameter logit model, we simulated the market shares of five car powertrains under three scenarios: “Italian car buyers face the same net purchase car prices and fuel\\electricity costs as the Norwegian car drivers and vice versa”, “Italy adopts the Norwegian registration tax”, and “Both Italy and Norway adopt a social cost internalizing registration tax”. The results indicate that Italian car users are reluctant to switch to battery electric cars (BEVs). They would choose BEVs more frequently in the three scenarios envisaged but without reaching the corresponding Norwegian levels. If Italy would adopt the Norwegian registration tax system, BEVs’ market share would gain 5.4 percentage points relative to the baseline scenario, while under the social cost internalizing scenario, BEVs’ market share would improve by 3.4 and PHEVs’ one by 0.2 percentage points. On the contrary, Norwegians are BEV-oriented and would comparatively preserve a high BEV share. In the social cost internalization scenario, the BEV share relative to the baseline scenario would decrease by 7.2 percentage points, petrol cars would gain 1.2, HEVs 2.9, PHEVs 3.4, and diesel cars would lose 0.3 percentage points. In general, there seems to be a lock-in or path dependence effect that limits BEV penetration in Italy and prevents the decline of the BEV share in Norway.

A Multicriteria-Based Comparison of Electric Vehicles Using q-Rung Orthopair Fuzzy Numbers.

The subject of this research is the evaluation of electric cars and the choice of car that best meets the set research criteria. To this end, the criteria weights were determined using the entropy method with two-step normalization and a full consistency check. In addition, the entropy method was extended further with q-rung orthopair fuzzy (qROF) information and Einstein aggregation for carrying out decision making under uncertainty with imprecise information. Sustainable transportation was selected as the area of application. The current work compared a set of 20 leading EVs in India using the proposed decision-making model. The comparison was designed to cover two aspects: technical attributes and user opinions. For the ranking of the EVs, a recently developed multicriteria decision-making (MCDM) model, the alternative ranking order method with two-step normalization (AROMAN), was used. The present work is a novel hybridization of the entropy method, full consistency method (FUCOM), and AROMAN in an uncertain environment. The results show that the electricity consumption criterion (w = 0.0944) received the greatest weight, while the best ranked alternative was A7. The results also show robustness and stability, as revealed through a comparison with the other MCDM models and a sensitivity analysis. The present work is different from the past studies, as it provides a robust hybrid decision-making model that uses both objective and subjective information.

Sustainable behaviour at work: How message framing encourages employees to choose electric vehicles

AbstractThis paper explores the application of message framing as a management practice to promote change in employee behaviour for corporate sustainability. We conduct a field experiment in a German automotive company to test the effects of three different frames (emotional, normative and gain) on pro‐environmental actions in relation to electric vehicle choices of 170 employees. The frames are applied via two communication channels: first, via emails to remind employees about ordering a new car and second, via pop‐up notifications appearing in the online system where employees complete their orders. We find that the interventions applied in emails, but not in pop‐up notifications, have significant positive effects on electric vehicle adoption. Yet, the durability of the effects is limited. Overall, gain framing in the form of cost saving information has the longest and most powerful impact on electric car choices. Our findings have implications for workplaces where employees might not yet possess strong pro‐environmental beliefs, showing that employee sustainable behaviour can be enhanced by emphasising complementary gain motives.

Sistem Pendukung Keputusan Pemilihan Mobil Bekas Dengan Metode Analythical Hierarchy Process Pada CV. Icha Mobilindo

Icha Mobilindo is a company engaged in the sale of used cars and has several branches in the city of Jambi. And now there is a problem, namely the process of selecting the best used car, there is no computerized system and not yet using a decision support system method so that the selection of the best used car cannot be measured, and due to the large number of available car choices, consumers are confused about choosing based on the criteria they want. Therefore, this study aims to provide a solution to the problems that occur by offering a decision support system for the selection of used cars using the analytical hierarchy process method. The author develops the system with the prototype method and uses a unified model language system model approach using use case diagrams, activity diagrams and class diagrams. The new system produces output that can display existing used car data, admin data, criteria data, alternative data, used car assessment data and used car ranking results with the analytical hierarchy process method that contributes to the company to increase sales.

CO2 Emission Labels in India: Whistling in the Wind?

We studied the effect of an environmentally conscious nudge on the choice of car that our respondents make. Our primary data driven study was conducted after adequately reviewing the literature pertaining to the field of behavioral environmental economics. Our experiment shows that the nudge worked in most cases, except those which involve the question of prestige and status. We also found that in case if the preference for a car in particular segment is high with a small difference in carbon dioxide (CO2) emissions, then the respondents tend to choose the car of their liking. It is concluded that tools like nudges are in indeed helpful in reducing the emission levels from what we currently have.

Exploring the Role of Shared Mobility in Alleviating Private Car Dependence and On-Road Carbon Emissions in the Context of COVID-19

Shared mobility is becoming increasingly popular worldwide, and travelers show more complex choice preferences during the post-pandemic era. This study explored the role of shared mobility in the context of coronavirus disease (COVID-19) by comparing the travel mode choice behavior with and without shared mobility. Considering the shared mobility services of ride-hailing, ride-sharing, car-sharing, and bike-sharing, the stated preference survey was designed, and the mixed logit model with panel data was applied. The results show that if shared mobility is absent, approximately 50% of motorized mobility users and 84.62% of bike-sharing adopters will switch to using private car and public transport, respectively. The perceived pandemic severity positively affects the usage of car-sharing and bike-sharing, while it negatively affects the ride-sharing usage. Under different pandemic severity levels, the average probabilities of private car choice with and without shared mobility are 38.70 and 57.77%, respectively; thus, shared mobility would alleviate the dependence on private car in post-pandemic future. It also helps to decrease the on-road carbon emissions when the pandemic severity is lower than 53. These findings suggest policymakers to maintain the shared mobility ridership and simultaneously contain the pandemic. Additionally, pricing discount and safety enhancement are more effective than reducing detour time to protect ride-sharing against COVID-19.

Fuel consumption elasticities, rebound effect and feebate effectiveness in the Indian and Chinese new car markets

China and India, the world's two most populous developing economies, are also among the world's largest automotive markets and carbon emitters. To reduce carbon emissions from the passenger car sector, both countries have considered various policy levers affecting fuel prices, car prices and fuel economy. This study estimates the responsiveness of new car buyers in China and India to such policy levers and drivers including income. Furthermore, we estimate the potential for rebound effect and the effectiveness of a feebate policy. To accomplish this, we developed a joint discrete-continuous model of car choice and usage based on revealed preference survey data from approximately 8000 new car buyers from India and China who purchased cars in 2016–17. Conditional on buying a new car, the fuel consumption in both markets is found to be relatively unresponsive to fuel price and income, with magnitudes of elasticity estimates ranging from 0.12 to 0.15. For both markets, the mean segment-level direct elasticities of fuel consumption relative to car price and fuel economy range from 0.57 to 0.65. The rebound effect on fuel savings due to cost-free fuel economy improvement is found to be 17.1% for India and 18.8% for China. A revenue-neutral feebate policy, with average rebates and fees of up to around 15% of the retail price, resulted in fuel savings of around 0.7% for both markets. While the feebate policy's rebound effect is low – 7.3% for India and 1.6% for China– it does not appear to be an effective fuel conservation policy.

Commuting to work in cities: Bus, car, or train?

AbstractIn this paper, we study the commuting behavior of citizens living in or near a city who must decide how to get to work. Such citizens can use their own car and drive to work or they can take public transport. The two public transport options we consider are the bus and train. In this context, we perform two tasks. First, we analyze the choice of car versus train. We calculate the deadweight loss from the negative externality (traffic congestion) generated by car travel and then discuss how a toll can achieve the efficient allocation of commuters between the car and train modes of transport. Second, we analyze the choice of car versus bus. Again, we calculate the deadweight loss from the traffic congestion resulting from car travel and discuss how a toll can achieve the efficient allocation of commuters between the car and bus modes of transport, which would benefit all commuters.

Travel mode choice as a representation of travel utility: A multilevel approach reflecting the hierarchical structure of trip, individual, and neighborhood characteristics

AbstractTrip characteristics, travel utility, socio‐economic characteristics, and built environment influence travel mode choice. Since the elements have a hierarchical structure, this study conducted multilevel multinomial logistic regression to evaluate the relationship between these elements and travel modes. Particularly, this study focused on how twelve types of expressed travel utilities affect travel mode choice for five travel purposes. The results show that people consider various travel utilities for each travel purpose; these elements influence the travel mode choice of cars, public transit, or non‐motorized modes, for example, public transit is preferred for punctuality and multitasking in commute, and for health benefits in shopping.

Analysis of Lifestyle and Consumer Attitude Towards Intention to Purchase a Personal Car During Pandemic

The COVID-19 pandemic is a new virus that allegedly originated in Wuhan, China, in 2019. This COVID-19 has spread rapidly, disrupting all sectors of life in various countries. The Covid-19 pandemic also impacted Indonesia's economic decline in 2020 and triggered the acceleration of the world recession. The policy of Large-Scale Social Restrictions (PSBB) and regional quarantine significantly impacted the delay in the distribution of goods, which resulted in employee housing and even layoffs. The fear of contracting COVID-19 triggers the intensity of the use of private vehicles as well as the intention to own a car. Lifestyle changes in traveling, especially the choice of the private car, are also influenced by the primary destination and distance traveled during the pandemic. But the economic pressure of the impact of the pandemic has become a substantial perception control that holds back the realization of intention to make a personal vehicle purchase decision. Although there has been an increase in the frequency of private vehicle use during the pandemic, in reality, the increase has not impacted private car sales. This is caused by the purchasing power of consumers, who are still hampered because of the pandemic. The government itself is aware that the automotive industry is an industry that has a significant contribution to 20 percent of the national Gross Domestic Product (GDP) and absorbs 1.5 million workers. This prompted the government to issue a stimulus in the form of policy relaxation to boost the productivity of the auto industry.

Consumer Knowledge about Electric Buses in Underserved Communities in Huntsville, Alabama

The major offshoot of innovation of electric vehicles (EVs) is sustainability. Given the prevailing challenges of carbon emissions from automobiles and the consequent effects on climate change, the introduction of EVs is timely to serve as an effective mechanism for reversing global warming. However, knowledge of EVs is not well distributed among the general population. This study is therefore aimed to analyze the familiarity of Americans with the basic features of EVs. One hundred respondents were selected from two underserved communities in Huntsville, Alabama, using a simple sampling technique. Nonetheless, only 71 residents returned the filled questionnaires. Data were analyzed using frequency counts and descriptive statistics. Findings revealed that the respondents were generally aware of EVs, but EVs are not yet common on their roads. The respondents also signaled low preferences for EVs. The purchase price, charging stations, and limited supply are the candid explanations for why EVs are not a priority in the respondents’ choice of cars. To bolster peoples’ taste toward EVs, the study, therefore, concludes that government authorities and city planners should popularize incentives among the people in underserved communities.

On parents' choice of the school travel mode during the COVID-19 pandemic.

Based on the theory of planned behavior (TPB) and the protection motivation theory (PMT), this study examines the factors that influence parental choice of school travel mode during COVID-19. Structural equation modeling (SEM) and a hybrid choice model (HCM) are used to analyze this decision-making process. The results show that trust, perceived severity, perceived vulnerability, perceived built environment, attitude, subjective norm, and perceived behavioral control are significant factors. Perceived severity, perceived vulnerability, and the evaluation of pandemic risk, significantly impact the choice of public transit and private car, but not walking. Perceived built environment is the most critical factor influencing the choice of walking. The results provide a theoretical basis and reference for relevant government departments to formulate policies and measures during COVID-19.