Household Vehicle Research Articles

THE SYSTEM OF PROVIDING HOUSEHOLD TRANSPORT OF THE KAZAKH PEOPLE (HISTORICAL ANALYSIS)

One of the traditional transports of the Kazakh people – the cart – is one of the most interesting and difficult topics. It can be used for lectures on ethnology, special courses, seminars, as well as for establishing interdisciplinary links in the field of ethnoarchaeology. The history of the formation of Kazakh household and household vehicles, their types and areas of distribution, methods of construction, features of their functions are considered. This issue has not been studied as a special topic in domestic and foreign ethnology. Archaeological excavations in the ancient settlements and mounds of the Kazakh land, which make up the base of objects, found things related to economic and household relations, which were considered from a scientific point of view to determine the genesis of transport. A written database on the ancient type of transports has been collected, analyzed and systematized, their level of study and historiography has been analyzed. An internal typology has been developed depending on the functional features of the carts. In addition to the specifics of the type and scope of application of the cart, regional, seasonal use, documents related to physical statistics were differentiated, their use in the economy, types of activities in the cart and databases of the cart in the historical era were revealed. An overview of the history of one of the household cars – a wagon was made. Analyzing physical and written databases, the carts were divided by typology as, early carts and medieval carts (houses on a cart, luxury carts).

Expansion of a small-scale car-sharing service: A multi-method framework for demand characterization and derivation of policy insights

When a car-sharing system is small, at its early stages, the modal split for the service is very low (e.g. <50 trips per day for the entire system). This results in a situation, where it is not possible to account for the demand for the service through traditional mode choice models. In addition, the business model of such a car-sharing service can be designed for serving special trips (e.g., trips to furniture stores). In several cities, this special group of trips are not adequately reflected in the existing demand models. Therefore, the objective of this research is to develop a data-driven methodological framework for finding the demand for such a system and characterise the users. To characterise the users, a multinomial logit model is employed. A linear regression model is used to estimate the average daily demand for the whole system, and a dirichlet regression to distribute the system level daily demand to individual stations. The estimation results of the multinomial logit model indicate the influence of socio-demographic characteristics (age, employment, income, education, household vehicle ownership), use of conventional modes (bicycle, PT and private car) and supply parameter (fleet size). Moreover, the results reveal that the service can improve transport equity. The linear regression model shows that the average daily demand increases with an increase in the number of stations. Furthermore, the demand varies according to days of the week and months (i.e., seasonal variation). Similarly, the shares of demand for individual stations is influenced by days of week. In addition, the shares are affected by the system level daily demand, i.e., as the demand increases, certain stations attract more users. Besides the aforementioned findings, operational and policy measures are suggested under the following categories: (i) Mobility as a Service (MaaS), (ii) Pricing strategy, (iii) Hybrid fleet, and (iv) Campaigns. The contributions from this study enable several cities to characterise the car-sharing service at early stages for expansion and also support the sustainable growth and integration of the service towards MaaS, leading to a better and efficient transportation system.

Who’s in the driver’s seat? Gender and the division of car use in auto-deficit households

• Practical necessity—the amount of time spouses that spend on household-serving or work-related activities—is the primary determinant of automobile use for both men and women. • Women are more likely to be the primary houseworker than men. • Women have substantially more exclusive access to the household vehicle than their male partners. • The findings reflect the persistence of the gender division of household labor and the role of the car in its maintenance. • The use of alternative modes of transportation is high among spouses who travel when the vehicle is in use by their partners. We examine the role of gender in access to household resources, in particular the household automobile. Drawing on data from the 2012 California Household Travel Survey, we isolate the determinants of vehicle use within dual-earner, dual-driver, heterosexual households in which drivers share a single vehicle. We test four gender-related hypotheses: the role of disparities in economic power, practical necessity related to the household division of labor, gender norms, and gender preferences. We find that practical necessity—the amount of time that spouses spend on household-serving or work-related activities—is the primary determinant of automobile use for both men and women. In contrast to previous research, we find that women have substantially more exclusive access to the household vehicle than their male partners. Rather than a measure of equality, this finding reflects the persistence of the gender division of household labor and the role of the car in its maintenance. The study underscores the broader need for policies to equalize gender roles both within and between the home and the workplace, as well as a role for transportation policies that serve the needs of household members who do not have primary use of the household car.

What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions

What makes you hold on to that old car? While the vast majority of household vehicles are still powered by conventional internal combustion engines, the progress of adopting emerging vehicle technologies will critically depend on how soon the existing vehicles are transacted out of the household fleet. Leveraging a nationally representative longitudinal data set, the Panel Study of Income Dynamics, this study examines how household decisions to dispose of or replace a given vehicle are: 1) influenced by the vehicle’s attributes, 2) mediated by households’ concurrent socio-demographic and economic attributes, and 3) triggered by key life cycle events. Coupled with a newly developed machine learning interpretation tool, TreeExplainer, we demonstrate an innovative use of machine learning models to augment traditional logit modeling to both generate behavioral insights and improve model performance. We find the two gradient-boosting-based methods, CatBoost and LightGBM, are the best performing machine learning models for this problem. The multinomial logistic model can achieve similar performance levels after its model specification is informed by TreeExplainer. Both machine learning and multinomial logit models suggest that while older vehicles are more likely to be disposed of or replaced than newer ones, such probability decreases as the vehicles serve the family longer. Pickup trucks and sport utility vehicles are less likely to be disposed of or replaced than cars, and leased vehicles are more likely to be transacted than owned vehicles. We find that married families, families with higher education levels, homeowners, and older families tend to keep their vehicles longer. Life events such as childbirth, residential relocation, and change of household composition and income are found to increase vehicle disposal and/or replacement. We provide additional insights on the timing of vehicle replacement or disposal, in particular, the presence of children and childbirth events are more strongly associated with vehicle replacement among younger parents.

Influencing factor analysis of household electric vehicle purchase intention of HaiNan Free Trade Port under the background of low-carbon lifestyle

Hainan Free Trade Port Layout decided to gradually ban fuel-based vehicles so as to achieve the goal of carbon emission peak by 2030, in 2021, HaiNan further required 100% use of new energy vehicles in official field. Therefore, as a complementary job, it is important to recognize key factors that influence purchasing will of electric vehicles of local residents. The study bases on field interviews and grounded theory, and we utilized qualitative analysis method to recognize four main scopes, namely: risk perception, value perception, mental pressure and self-control that influence the purchase intention of the public after three levels of notion encoding. Then a ridge regression model is built to get the relative weight of the factors, and we adopted theory of planned behavior by integrating factor weights and categories extracted from qualitative analysis into the TPB model so as to find psychological reasons of people’s choice, finding risk perception and value perception are dominate features that influence public purchase choice. At last, targeted suggestions are put forward based on above results.

Electric vehicles in multi-vehicle households

ABSTRACT This paper uses U.S. nationally representative data from the 2017 National Household Travel Survey to present a series of facts about electric vehicles (EVs) in multi-vehicle households. First, as of the time of the survey, 89% of households with an EV also had a non-electric vehicle in addition to the EV. Second, 60% of households with an EV also had a non-electric SUV, truck, or minivan. Third, 66% of households with an EV also had a non-electric vehicle that was driven more miles per year. The paper argues that these patterns have significant implications for the environmental impact of EVs and underscore the importance of better understanding how multi-vehicle households substitute between vehicles.

Identifying the factors influencing the choice of different ride-hailing services in Shenzhen, China

In recent years, transportation network companies have developed various services, such as express, ride-pooling, premium service and app-based taxi hailing service, to satisfy the diversified travel demands of residents. However, the existing knowledge on the factors that affect the choice of different ride-hailing services remains scarce, and the difference of this impact between the users with and without household vehicles (HVs) has been little examined. To fill these gaps, the goal of this study is to explore the influential factors of different ride-hailing services choice as for the users with and without HVs. The results of multinomial logistic models indicate that: (1) the users without HVs whose original travel mode is bus are more likely to utilize ride-pooling, the users with HVs whose original mode is private car are more inclined to use premium service and express. (2) These two types of users focus on different aspects of ride-hailing travel. The users with HVs are concerned about the travel service quality and travel experience, while the counterpart users care about whether their basic travel needs can be satisfied. (3) Premium service has a positive effect on reducing the use of private cars among users with HVs, but this effect is relatively weak. (4) Ride-pooling and express could delay private cars’ purchasing plan by users without HVs, indicating a governance way to inhibit the growth of cars, i.e., reducing car ownership by ensuring car use. Finally, some suggestions are proposed to promote the sustainable and healthy development of different ride-hailing services.

Long-term Energy Cost Labelling for Appliances: Evidence from a Randomised Controlled Trial in Ireland

Given the longevity of investments in energy-consuming products (such as household appliances, vehicles, and properties), underinvestment in energy efficiency can have long-lasting negative economic and environmental consequences. Previous research has indicated that underinvestment may be due to imperfect information in relation to the long-term benefits of investing in energy efficiency. This paper presents the results of a cluster randomised controlled trial examining an intervention which aims to overcome this information deficit by providing long-term energy cost information on appliances in an electrical retail chain in Ireland. Two treatments are considered: a label showing 10-year energy cost information based on typical usage for four appliance categories (fridge freezers, dishwashers, washing machines, and tumble dryers); and a second treatment which supplements this label with a QR code where consumers can gain personalised cost estimates based on their expected appliance usage. Results indicate that neither of the treatments resulted in an increase in the average energy efficiency of appliances sold. Also, engagement of customers with the QR code was extremely low. Given that the newly designed EU energy labels incorporate QR codes for personalisation, this low usage suggests that this element of the new labels may be ineffective in increasing the uptake of energy efficiency. Finally, a customer survey suggests that while the treatment increased the stated importance of energy efficiency in decision-making, this did not translate into an increase in efficiency of products purchased, i.e., stated preferences for energy efficiency did not translate into revealed purchasing preferences.

Beyond Adoption: Examining Electric Vehicle Miles Traveled in Households with Zero-Emission Vehicles

The environmental benefits of zero-emission vehicles (ZEVs) are affected by both consumer adoption and usage patterns. While numerous studies examine consumers’ stated or revealed preferences for ZEV adoption, ZEV usage patterns have received less attention. Based on the 2019 California vehicle survey data, this paper analyzes the annual mileage of three ZEV types: battery electric vehicle (BEV), plug-in hybrid electric vehicle (PHEV), and fuel cell electric vehicle (FCEV). Results show that ZEVs are driven as much as or more than internal combustion engine vehicles (ICEVs). Furthermore, focusing on households with one ZEV and one or more ICEVs, factors that influence household electric vehicle miles traveled (eVMT) are explored using multiple linear regression models. Greater battery range, home charging capability (regardless of charger type), and provision of special electricity rates for home charging are found to be positively correlated with the eVMT of PHEV households. The eVMT of BEV households is positively associated with Level 2 home charging capability, solar panel installation, access to workplace DC fast charging, and access to public Level 2 and DC fast charging stations. The number of routinely-used public hydrogen refueling stations is associated with higher FCEV household eVMT. Lastly, when high-occupancy vehicle lane access is rated as extremely important in the ZEV purchase decision, greater eVMT is found for both BEV and FCEV households, but not for PHEV households. Results of this study inform policies to encourage eVMT over vehicle miles traveled by ICEV in a household, achieving greater environmental benefits from ZEVs.

Nonlinear Relationships between Vehicle Ownership and Household Travel Characteristics and Built Environment Attributes in the US Using the XGBT Algorithm

In the United States, several studies have looked at the association between automobile ownership and sociodemographic factors and built environment qualities, but few have looked at household travel characteristics. Their interactions and nonlinear linkages are frequently overlooked in existing studies. Utilizing the 2017 US National Household Travel Survey, the authors employed an extreme gradient boosting tree model to evaluate the nonlinear and interaction impacts of household travel characteristics and built environment factors on vehicle ownership in three states of the United States (California, Missouri, and Kansas) that are different in population size. To develop these models, three main XGBT parameters, including the number of trees, maximal depth, and minimum rows, were optimized using a grid search technique. In California, the predictability of vehicle ownership was driven by household travel characteristics (cumulative importance: 0.62). Predictions for vehicle ownership in Missouri and Kansas were dominantly influenced by sociodemographic factors (cumulative importance: 0.53 and 0.55, respectively). In all states, the authors found that the number of drivers in a household plays a vital role in the vehicle ownership decisions of households. Regarding the built environment attributes, deficiencies in cycling infrastructure were the most prominent attribute in predicting household vehicle ownership in California. This variable, however, has threshold connections with vehicle ownership, but the magnitude of these relationships is small. The outcomes imply that improving the condition of cycling infrastructure will help reduce the number of vehicles. In addition, incentives that encourage the households’ drivers not to buy new vehicles are helpful. The outcomes of this study might aid policymakers in developing policies that encourage sustainable vehicle ownership in the United States.

Structural and Attitudinal Barriers to Bicycle Ownership and Cycle-Based Transport in Gauteng, South Africa

Policies that aim to facilitate and promote non-motorised transport (NMT), and in particular cycling, have been developed by many high-income countries facing increasingly congested roads and saturated public transport systems. Such policies are also emerging in many low- and middle-income settings where high rates of urbanisation have led to similar problems with motorised transport. The aim of the present study was to better understand the potential structural and attitudinal barriers to cycle-based transport in one such context: South Africa’s Gauteng Province, the industrial powerhouse of sub-Saharan Africa that has recently made a firm commitment to NMT. The study focussed on demographic and socioeconomic variation in bicycle and car ownership, and related this to: (1) the reported use of motorised and non-motorised transport (both private and public); and (2) perceived ‘problems’ with cycling. The analyses drew on interviews with key respondents from n = 27,490 households conducted in 2013 as part of the third Quality of Life survey undertaken by the Gauteng City Regional Observatory. The survey contained items on three outcomes of interest: household vehicle ownership (bicycles and cars); modes of transport used for the “trips” most often made; and respondents’ “single biggest problem with… cycling”. Respondent- and household-level demographic and socioeconomic determinants of these outcomes were examined using descriptive and multivariable statistical analyses, the latter after adjustment for measured potential confounders identified using a theoretical causal path diagram (in the form of a directed acyclic graph). Of the n = 26,469 households providing complete data on all of the variables examined in the present study, only n = 8722 (32.9%) owned a car and fewer still (n = 2244; 8.4%) owned a bicycle. The ownership of these assets was commonest amongst wealthier, economically active households; and those that owned a car had over five times the odds of also owning a bicycle, even after adjustment for potential confounding (OR 5.17; 95% CI 4.58, 5.85). Moreover, of household respondents who reported making ‘trips’ during the preceding month (n = 18,209), over two-thirds of those whose households owned a car (70.1%) reported private car-based transport for such trips, while only 3.2% of those owning a bicycle reported cycling. Amongst the specific responses given to the item requesting the “single biggest problem with… cycling” by far the commonest was “Don’t know how to cycle” (32.2%), less than half as many citing “Vehicle accident risk” (15.9%), and fewer still: “Destination is too far” (13.9%); “Crime” (10.3%); “Too much effort” (9.2%); or “Lack of good paths” (4.6%). While the first of these reasons was commonest amongst poorer households, concerns about risk and effort were both most common amongst better educated, economically active and wealthier/better serviced households. In contrast, concerns over (cycle) paths were only common amongst those owning bicycles. The low prevalence of household bicycle ownership, and the disproportionate number of households owning bicycles that also owned cars, might explain the very small proportion of the ‘the trips most often made’ that involved cycle-based transport (0.3%), and the preferential use of cars amongst households owning both bicycles and cars. Low levels of bicycle ownership might also explain why so many respondents cited “Don’t know how” as the “single biggest problem with… cycling”; although risk and effort were also substantial concerns (presumably for many who did, and some who did not, know how to cycle); the lack of suitable cycle lanes being only primarily a concern for those who actually owned bicycles. Structural and attitudinal barriers to cycle-based transport limit the use of cycle-based transport in Gauteng, not only amongst the vast majority of household respondents who lack the means to cycle (and the means to learn how), but also amongst those dissuaded from learning to cycle, purchasing a bicycle and/or using a bicycle they own by: the risks and effort involved; the lack of suitable cycle paths; and/or because they also own a car and prefer to drive than cycle.

Vehicle ownership models for a sharing economy with autonomous vehicle considerations

ABSTRACT This study investigates the impacts of the sharing economy and vehicle automation on household vehicle ownership by considering the earning potential of household vehicles. While many studies have examined the impact of the sharing economy (namely ridesourcing) on vehicle ownership of ridesourcing customers, there is a gap in understanding its impact on vehicle ownership among ridesourcing suppliers. Data from an original survey are used to develop discrete choice models that evaluate the effects of earning potential, automation, and shared autonomous vehicle services on vehicle transaction decisions. The estimated models are applied to perform national-level simulations. It is found that, in comparison to a non-sharing economy context, individuals in the sharing economy tend to purchase more vehicles in times of income gain, and to preserve their fleets more readily in times of income loss. This study provides evidence that the sharing economy incentivizes some individuals to own more – not fewer – vehicles..

Impact of ride-hailing usage on vehicle ownership in the United States

• Household-level data mainly from the 2017 NHTS are used, covering 42 US metro areas. • The recursive effect between ride-hailing and vehicle ownership was considered. • Regular/active ride-hailing users were negatively related to the change in ownership. • Policy implications on vehicle ownership and ride-hailing are discussed. Ride-hailing as an innovative urban mobility service has received increasing attention in the recent decade. Using data from the 2017 National Household Travel Survey, we investigate the relationship between ride-hailing usage and household vehicle ownership from 42 metropolitan areas across the United States. Previous studies have shown mixed findings on this relationship. Our study contributes to the literature by estimating a bivariate ordered probit model with considerations of the recursive effect between ride-hailing usage and household vehicle ownership. We find that compared to occasional users, regular and active users are more likely to own fewer vehicles. Also, we find no significant difference in possessing vehicles between regular and active users.

How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE

AbstractMachine learning (ML) is being used regularly in many different fields. This paper compares traditional econometric methods that have better explanations of data analysis to ML methods, focusing on predicting, understanding and unpacking ML methods which have higher prediction accuracies of four key transport‐planning variables: household vehicle‐miles traveled (continuous variable), household vehicle ownership (count variable), mode choice (categorical variable), and land use change (categorical variable with strong spatial interactions). Here, the results of ten ML methods are compared to methods of ordinary least squares (OLS), multinomial logit (MNL), negative binomial and spatial auto‐regressive (SAR). The U.S.’s 2017 National Household Travel Survey and land use data sets from the Dallas‐Ft. Worth region of Texas are used. Results suggest traditional econometric methods work pretty well on the more continuous responses (VMT and vehicle ownership), but the random forest (RF), gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost) methods delivered the best results, though the RF model required 30 to almost 60 times more computing time than XGBoost and GBDT methods. The RF, GBDT, XGBoost, light gradient boosting method (lightGBM), and catboost offer better results than other methods for the two “classification” cases, with lightGBM being the most time‐efficient. Importantly, ML methods captured the plateauing effect modelers may expect when extrapolating covariate effects.

School Travel Inequity between Students from Public and Private Schools in the City of Shenzhen, China

Understanding school travel inequities and promoting active travel policies more effectively is an increasingly important issue in the international transport policy agenda. Using the dataset of the 2014 Shenzhen primary and secondary school travel survey, this study empirically revealed the permanent residence permit (hukou) system in the context of China shapes the evident inequities between students from public schools and private schools. Students without a legitimated hukou to local areas suffer from more constraints, longer distances, and more time to access private schools which are excluded from the public sponsorship and have disadvantages in geographical locations. Applying the ordered logistic model, this study specifically investigated the influential factors of school commuting travel mode. Household vehicle ownership and travel features (i.e., chauffeuring and home-school distance) have a much more significant role in school travel mode decisions, which largely surpassed the role individual demographic attributes and the school surrounding built environment play. The implications of this study shed light on making more specific strategies for private schools to mitigate mobility inequity imposed on disadvantaged students.

Characterizing end-of-life household vehicles’ generations in China: Spatial-temporal patterns and resource potentials

China is now boasting a huge number of end-of-life vehicles (ELVs) with a low recovery rate, resulting in a waste of resources and severe environmental pollution. Predicting the generation of ELVs in future helps to enhance recycling efficiency by providing a basis for the planning of recycling industry. However, previous research did not provide a detailed, mid-term forecast. Based on a stocks-driven model and the bottom-up extrapolation of in-use stocks, this study characterizes the generation of end-of-life household vehicles (HVs) in China by addressing the detailed spatial-temporal patterns and resource potentials during 2019–2050. The results show that the annual end-of-life HVs in China will continuously increase during 2019–2050, resulting an accumulated 1.48 billion units, among which urban areas will account for 86%, and internal combustion engine vehicles (ICEVs) will take up 80%. Regarding the spatial patterns, eastern region will possess the largest proportion, and wide variations are found among all provinces due to the difference in population size and economic development level, which have important implications for further planning of end-of-life HVs recycling industry. Before 2050, accumulated quantity of all types of metals in end-of-life HVs will approach domestic mine production (in 2019), or even approach the current global mine production, indicating they have a great potential as an indispensable source for domestic resource supply.

Integrating plug-in electric vehicles (PEVs) into household fleets- factors influencing miles traveled by PEV owners in California

Household vehicle miles traveled (VMT) has traditionally been studied in the context of gasoline vehicles. In this study, we analyze VMT of Plug-in Electric Vehicles (PEVs) to understand PEV use and the factors that influence their use in a household. We use data from a unique repeat survey of PEV owners in California who were originally surveyed when they first bought their vehicle. Having two survey responses per household allows us to obtain more reliable VMT data and analyze the change in VMT over the vehicle ownership period. The results show that PEV VMT is correlated with traditional factors like population density, built environment, attitudes towards technology, and lifestyle preferences. Specific to PEVs, electric driving range and access to infrastructure, have a major influence on PEV VMT. Moreover, while lower electricity price at home may lead to a higher share of PEV VMT in total household VMT, we do not identify the presence of “rebound effect”. Overall, we observe that factors influencing PEV VMT are like those observed for conventional gasoline vehicles. We find that PEVs drive a similar amount as conventional vehicles, not less (due to range anxiety) as some have suggested. The results have implications for emissions impact assessments and travel demand models that depend on assumptions of average annual VMT for policymaking.

Latent vehicle type propensity segments: Considering the influence of household vehicle fleet structure

This study applies latent class cluster analysis to a sample of 1,111 survey respondents in Georgia, identifying naturally occurring vehicle type segments based on the influence of both individual vehicle type choices and household vehicle fleet structures. The developed model identifies seven latent vehicle type propensity segments, six of which include individuals who reported being the main driver for (respectively) car, SUV/van, and truck. In three of those segments this was generally their only available vehicle, while in the other three the “main driver” vehicle accompanied other available household vehicles. The seventh segment captures individuals who are main drivers of multiple vehicle types, and who also have other household vehicles available for use. We generate user profiles and discuss differences across segments regarding individual-level characteristics (e.g., gender), household-level characteristics (e.g., household income), land-use and travel-related preferences (e.g., neighborhood type, share of household-serving trips), attitudes (e.g., materialistic), and targeted marketing data variables (e.g., support for charitable causes). Selected results suggest that women choose SUVs/vans due to both personal preferences (e.g., feeling safer while driving a large vehicle) and household responsibilities; show that vehicle-owning behaviors and attitudes are generally consistent, except that strong pro-vehicle-owning attitudes exist within vehicle-deficit households; and suggest that vehicle-deficit households may be less open to alternative fuel vehicles, possibly due to reliability concerns. Overall, this study provides a new perspective on vehicle type propensity segments, and examines the association of a novel range of general and travel-related attributes with these segments, yielding nuanced insights with potential policy implications.

Stated Preference Analysis of Automated Vehicles among California Residents Using Probabilistic Inferences

As technology advances, improvements in the way people travel also occur. In 2017, the National Renewable Energy Laboratory (NREL) conducted a travel survey in California to understand residents’ perception of several aspects of mobility. This study used the data collected by NREL to understand various factors associated with safety perceptions and acquisition of automated vehicles among California residents. Bayesian networks (BNs) were used to learn the probabilistic interrelationships between aspects of automated vehicles. The predicted probabilities for safety concerns about vehicles with full driving automation (FAVs), purchase of vehicles with auto-drive assistance, and purchase of FAVs were determined after learning the BN structure and parameters from the data. The study found a strong relationship between the acquisition of automated vehicles and vehicles with auto-drive assistance. The BN model predicted that residents who are interested in purchasing vehicles with auto-drive assistance also have about 95% likelihood of purchasing FAVs. Moreover, ridesharing, number of vehicles in the household, housing type, and plug-in electric vehicle (PEV) ownership are among other factors playing a great role in the acquisition and safety perception of automated vehicles. Residents who are currently participating in ridesharing and living in apartments are more likely to purchase vehicles with auto-drive assistance. Residents who either own a PEV or have three or more vehicles are more likely to have safety concerns about FAVs. Additionally, residents who do not have safety concerns about FAVs have about a 45% likelihood of purchasing them. These results could provide valuable user opinion information for vehicle developers and other stakeholders.

Factors influencing public awareness of autonomous vehicles: Empirical evidence from Brisbane

Growing concerns, regarding transport disadvantage and increased socioeconomic disparities, demand innovative ways to improve equity and make transportation systems smarter. One of the most anticipated developments within the field of smart mobility is driving automation systems. Autonomous vehicles (AVs) have the potential to improve accessibility, affordability, safety and efficiency of transport systems, but come with risks including the intensification of private vehicle travel. Understanding public awareness is an important step to determine whether motivation and intention to use AVs will impact on its potential. This paper aims to investigate factors influencing public awareness of AVs as a solution to transport disadvantage. The study applies an ordinal logistic regression analysis to analyse survey data collected from the residents of Brisbane (Australia). Our findings reveal that awareness is positively correlated within young and middle-aged adults, low-income residents, disability, public transport users, and those who feel unsafe on public transport, while an increase in the number of household vehicles is negatively correlated. The study provides insights into public awareness towards the potential benefits of AVs, and highlights potential opportunities and challenges associated with these expectations.