Household Vehicle Research Articles

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Evaluating the accessibility of on-street household electric vehicle charging stations in London: Policy insights from equity analysis across emission zones

Understanding sociodemographic characteristics shaping the choice of large homes: lessons from Turkish metropolises

Purpose In metropolitan areas experiencing rapid urbanization and housing production, the size of housing units emerges as a crucial factor to consider in housing policy formulation. This study aims to focus on Turkey, a developing country undergoing rapid urbanization and a construction boom in recent years, to examine households’ housing size preferences. Through a detailed analysis, this research delves into the causal relationships between income, education and housing size preferences. Design/methodology/approach This study uses the Family Structure Survey in Turkey 2016 data set collected nationwide by the Turkish Statistical Institute (TurkStat). To address potential endogeneity issues related to income and education levels in households’ choice of house size, an extended regression model is used. In addition, survey weights are applied to the statistical model to generalize the results of the study. Findings The study demonstrates that household income correlates with an increase in house size, while household education is associated with a decrease in house size. Variables such as household age, composition and vehicle ownership are found to impact the choice of house size. Particularly, one-person and couple-only households tend to prefer smaller dwellings compared to others. Lastly, the results reveal that the influence of household composition on dwelling size varies according to household age. Originality/value This study presents a comprehensive analysis of the determinants influencing households’ housing size preferences within the framework of a developing country context, focusing on Turkey. It specifically offers insights into the causal impact of education and income levels on housing size preferences, as well as the intricate interplay between household characteristics in shaping these preferences.

Simulation of Discovering Market Opportunities Using AI Methods

Due to the strong turbulence that exists in the business environment, the view is sometimes articulated that the time horizon of decisions is no longer a hallmark of strategic management. Opposing views argue that a turbulent environment changes the trajectory of reaching future objectives determined by strategic decisions. Such trajectories require the company to be agile, which is manifested by seizing opportunities. Opportunities arise because the environment is volatile. An example of market opportunity is the unsatisfied demand for products. Buyers are driven by different motives when choosing between products that are alternative to them. From the producer's point of view, the purchase is therefore random. As a consequence, the alignment of the offered product portfolio with the actual purchasing decisions of customers is undetermined. In this article, we address the problem of determining the product portfolio structure expected by customers, i.e. one that is consistent with their future purchasing decisions. We hypothesise that this problem can be represented by a model of flows between alternative resources. Problems of this type are solved using Markov Chain Monte Carlo (MCMC) methods. In order to discover the expected demand (opportunity), we simulated the flow model we developed using the Metropolis-Hastings algorithm, which is a special case of the MCMC method used in AI. Due to operating on numerical data, such simulations fall under quantitative research. In the article, we present a model of customer flows between alternative products and then the simulation result, which is the expected value of these flows (stationary point). We convert this value into the structure of the product portfolio using a model of customer purchasing decisions that we have developed. The obtained results confirm the effectiveness of our method and have significant practical value, especially for SMEs. It also enables the assessment of the company's product strategy based on real sales data. The application of our methodology is limited to discovering opportunities in situations where purchasing decisions are influenced by variable environmental factors, causing irregular purchases by individual customers. It is useful, for example, in discovering opportunities in markets such as Household Articles, Furniture, Vehicles, Equipment Repairs, Construction, and many others.

From Trends to Drivers: Key Factors Propelling Electric Vehicle Sales

With the rapid advancement of human economic levels and modern civilization, the automobile manufacturing industry is increasingly confronted with challenges related to energy scarcity and environmental pollution. Low carbon emissions and energy savings have become the main focus of automotive development. Under the influence of government incentives, the sales of household electric vehicles (EVs) have increased significantly, although they still represent a small share of the overall car market. To examine the factors influencing consumer purchases of household EVs, this report integrates both qualitative and quantitative analyses, controlling for single variables. Using linear regression, an empirical analysis was conducted on 18 BYD models with varying ranges and prices. The results indicate a strong positive correlation between driving range, selling price, and EV sales. Looking ahead, the development of new energy vehicles should prioritize longer ranges, high-quality features, and cost-effective performance.

Household characteristics and electric vehicle adoption: changes from 2017 to 2022

ABSTRACT State and federal agencies have set several ambitious goals regarding electric vehicle sales in the United States. For example, the Biden Administration has set a target that 50% of new vehicle sales be electric by 2030. California aims to have 100% of new vehicles be zero-emission by the 2035 model year. This paper uses data from the 2017 and 2022 National Household Travel Surveys to document the extent of progress achieved in pursuit of these goals. These nationally representative surveys indicate that electric vehicle ownership has increased for both homeowners and renters, across all income groups, in single- and multi-vehicle households, and for urban and rural households. The results also highlight a few areas that vehicle manufacturers and policymakers need to address to accelerate the transition to a vehicle fleet that is less dependent on fossil fuels.

Examining the correlation of household electric vehicle ownership: Insights for emerging mobility and planning

While there is a global shift to electric vehicles (EVs), few studies have comprehensively quantified the relative contribution of individuals' demographic characteristics, alternative transportation choices, and built environment (BE) attributes to EV ownership. Applying extreme gradient boosting decision trees to the 2020 regional household travel data in Wuhan, this study estimate the respective effects of these factors on EV ownership. The results emphasize the contribution of the BE and alternative travel options in predicting EV ownership and reveal a nonlinear relationship between variables. The distance to the city center, density and charging station facilities are identified as key factors influencing EV ownership. The study revealed a positive correlation between household ownership of conventional cars and EVs, indicating a tendency for households possessing conventional cars to acquire extra EVs, thereby transitioning into multi-car households. The findings also suggest that, in addition to the emissions reduction benefits, planners should be concerned about the potential urban issues that may result from vehicle electrification. The study provides empirical evidence for urban planners to inform policy interventions aimed at guiding the sustainable development of emerging mobility modes.

A Study on Satisfaction of Tamil Nadu Government Free Bus Travel for Women in Madurai City

Abstract: In May 2021, the Tamil Nadu government launched a scheme that made public city and town buses (with certain caveats) free for women. While the rides are completely free (with no restrictions on the number of trips per day or month), the scheme is applicable only on ‘white board’ buses. The white board buses are what are known as Ordinary Bus Routes and represent the most inexpensive category of service that halts at all bus stops on a given route. The state government has provided an annual subsidy of Rs.1,200 crore to state transport undertakings to compensate for the fare box loss due to the scheme. The Tamil Nadu Chief Minister has extolled the scheme as an ‘economic revolution’, enabling women to join the workforce. In the eyes of many though, free transport remains a political gimmick. It’s not unusual to find citizens who question why travel should be free; and why just for women. A free travel scheme though is not unlike free health services or free education. Some services are so closely linked to ‘public good’, that the cost they incur is easily offset by the good that they achieve. Public transport is one of these. In general, women in India struggle to attain financial independence and a promising career. These challenges are multiplied in the case of women from underprivileged communities as they start with little to no education and a higher dependence on men. Much of this is culturally driven. They are also mostly unemployed; and when they do find employment it is within the unorganized sector, earning low wages. In the majority of Indian families, men tend to have access to the household vehicle, while women generally rely on public transport. Considering that the percentage of women undertaking paid work in India is only 21% while the global average is 47%, mobility becomes a life changing factor that could make or break a woman’s access to work opportunities. On the other hand, women who decide to not work outside the house, still rely on public transport to run errands and manage the household. This again incurs a cost.

Technology progress and clean vehicle policies on fleet turnover and equity: insights from household vehicle fleet micro-simulations with ATLAS

ABSTRACT This paper documents the design and application of ATLAS (Automobile and Technology Lifecycle-Based ASsignment), a comprehensive household vehicle transaction and technology adoption micro-simulator in the San Francisco Bay Area. ATLAS evolves the fleet mix of individual households by simulating the vehicle transaction and choice decisions in response to co-evolving demographics, land use, and vehicle technology simulations. While most existing literature has focused on the aggregate clean vehicle uptake, this paper differentiates distributional effects and decomposes the underlying mechanisms across heterogeneous sub-populations of households. Using scenarios and sensitivity simulations that vary vehicle technology and policy assumptions, we find that Zero Emission Vehicles (ZEVs) penetrate into higher income groups at a faster rate than into lower income groups, which is intuitive and aligns with expectations. Interestingly, the relative income disparity in ZEV ownership shrinks over time across all scenarios, with a ZEV mandate coupled with declining battery cost leading to the greatest reduction in disparity of ZEV ownership by 2050. Federal, state, and local financial incentives influence the redistribution of ZEV uptake across income groups and contribute to narrowing income disparity. Vehicle transaction frequency and new versus used market dynamics are found to be important factors contributing to the income disparity.

Estimating and Implementing a Vehicle-Type Model in an Activity-Based Travel Model Framework

Characteristics of household vehicles can influence daily travel behavior and can vary greatly by vehicle type. Vehicle type is defined here as a combination of body type, fuel type, and age. Using data compiled primarily from the National Household Travel Survey, a multinomial logit model was developed to predict vehicle type based on characteristics of the household that owns the vehicle. The model was then implemented for the San Francisco Bay Area in the ActivitySim activity-based modeling framework and validated against observed data. The ActivitySim project’s goal is to create and maintain advanced, open-source, activity-based travel behavior modeling software, based on best software development practices, for distribution to the public, free of charge. A naïve 2030 scenario was analyzed to show model response to changes in the vehicle fleet.

The Effect of Built Environment on Carbon Emission from Travel Behavior in the City center of Surabaya

Climate change is currently demanding attention due to global warming caused by increased Greenhouse Gas (GHG) emissions from the land transportation sector. The built environment is one of the factors that can influence non-motorized travel and emissions production. The built environment is identified using the 3D component approach (Density, Diversity, Design). The City center of Surabaya area is one of the Central Business Districts (CBDs) that is not exempt from emission issues and has the potential to decrease emissions through environmental arrangement scenarios. This research investigates the correlation between the 3D components and carbon emission production using the method of correlation analysis and multiple linear regression. The analysis results show a significant correlation between carbon emission production and the travel behavior of the city center of the Surabaya area community. Travel behavior variables influencing carbon emission production are household income, vehicles ownership, and travel distance. Furthermore, the study results indicate a significant relationship between travel behavior and the 3D components, as well as a significant association between the 3D components and carbon emission production. The 3D components that affect carbon emission production are population density, land use mix index, and pedestrian pathway density.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Factors influencing household VMT considering differences between ICE and electric vehicles

AbstractThis study examines factors affecting household vehicle miles traveled (VMT) with a focus on the differences between electric vehicles (EVs) and conventional internal combustion engine vehicles (ICEVs). This study mainly utilizes detailed individual‐level data from the 2017 National Household Travel Survey‐California Add‐on (2017 NHTS‐CA). We first classify households into three groups such as (1) households with only ICEVs, (2) households with only EVs, and (3) households with both ICEVs and EVs. We then employ ordinary least square regression models to analyze the determinants of household VMT across three groups. Second, we focus on households with both ICEVs and EVs to look at the substitute patterns between ICEVs and EVs. We employ a fractional logit model to analyze the factors affecting the share of EVs' VMT in total household VMT. Key findings are as follows. First, households with only EVs tend to have lower household VMT than others. Second, available charging stations near residential locations lead to longer households VMT in households with only EVs. Third, employment density has different effects on household VMT by groups. For instance, high employment density leads to shorter household VMT in households with only ICEVs and with both ICEVs and EVs. On the other hand, high employment density reveals a statistically positive effect on household VMT in households with only EVs. Lastly, in households with both ICEVs and EVs, the share of EVs' VMT is likely to increase in total household VMT if EVs are used more for work trips and shopping/family errands.

In-situ growth of MIL-101(Fe) onto PET base for high-efficiency adsorption of low concentrations benzene series VOCs and easy recycling

With the proliferation of vehicles in households, people are spending more and more time in their cars, so it is important to ensure the air quality and safety of the car. In this research, environmentally friendly, green PET@PDA@MIL-101(Fe) (PPM) was prepared by in-situ solvothermal growth and acid etching methods, which was categorized by SEM, FTIR, XRD, XPS, and BET. PPM was used to adsorb benzene, toluene, and m-xylene at low concentrations. The maximum amount of adsorption of PET@PDA@MIL-101(Fe) (PPM) for benzene, toluene, and m-xylene was 68.72 mg/g, 91.63 mg/g, and 78.47 mg/g respectively. The adsorption process of PPM is well fitted by the pseudo-first-order kinetic model and Langmuir isothermal adsorption model. Furthermore, PPM exhibited excellent toluene removal performance in simulated vehicles and excellent cycling capability in adsorption-desorption experiments. As a result, PPM can be used to treat and manage toluene volatile organic compounds (VOCs) at low concentrations.

Revisiting built environment and travel behavior: A natural experiment accounting for residential self-selection

Numerous studies have attempted to quantify the genuine impact of the built environment factors on travel behavior which is not through residential self-selection (RSS). Nevertheless, the interactions among built environment variables have rarely been included in investigations. To address this gap, this study utilizes a natural experiment formed by the unique urban housing context in Beijing, China. It specifically focuses on resettlement housing families, whose place of residence is largely exogenous, effectively mitigating the bias caused by RSS. By using a gradient boosting decision trees model, this study investigates the combined influence of the built environment on household vehicle kilometers traveled (VKT). The results show that the net built environment contributes about 64% to household VKT, and RSS plays a trivial role (less than 2%) in the relationship between the built environment and household VKT in Beijing. However, the built environment variables influence household VKT in different ways for resettlement households and commercial housing households. These results increase our understanding of how the built environment influences travel behavior.

Variation in performance and characteristics of long-term operated gas diffusion layer in a fuel cell electric vehicle

Proton-exchange membrane fuel cells (PEMFCs) operate at low temperatures and are used in household appliances and vehicles because of their high power density and fast start-up/shut-down capabilities. Previous research has focused on improving the performance of PEMFCs by enhancing their structures and constituent materials of their components. However, as PEMFCs are being commercialized, their durability remains a critical challenge that needs to be addressed. Although numerous studies have evaluated the durability of membrane electrode assemblies (MEA), limited research has been conducted on the durability of gas diffusion layers (GDL). The GDL, with its nanometer-to micrometer-sized pores and thickness of hundreds of microns, is prone to mechanical degradation during long-term operation. Moreover, the GDL is susceptible to carbon corrosion because it primarily consists of carbon materials, similar to the catalyst layer. Therefore, to ensure the performance and longevity of PEMFCs, it is of utmost importance to investigate the durability of the GDL. This study quantitatively investigates the variations in the performance and mechanical and chemical characteristics of GDLs during an extended period of operation in a fuel cell electric vehicle (FCEV), and two types of GDLs are analyzed.

Impact Energy Dissipation and Quantitative Models of Injection Molded Short Fiber-Reinforced Thermoplastics

Glass short fiber-reinforced thermoplastics (SGFRTP) are used to reduce carbon dioxide emissions from transportation equipment, especially household vehicles. The mechanical properties required for SGFRTP include flexural strength, impact resistance, etc. In particular, impact resistance is an important indicator of the use of SGFRTP. For this study, a mechanical model was developed to explain the notched impact strength of SGFRTP injection molded products in terms of their interfacial shear strength. The values obtained from the model show good agreement with the experimentally obtained results (R2 > 0.95). Results also suggest that the model applies to different fiber orientation angle and a range of fiber lengths in the molded product that are sufficiently shorter than the critical fiber length.

Mpox Vaccination and the Role of Social Vulnerability in Durham County, North Carolina, USA.

Disparities in vaccine coverage among groups in the USA is common, possibly due to higher vaccine hesitancy in certain populations, difficulty accessing vaccines, and underlying social vulnerability. The aim of this study was to investigate the association between mpox vaccine administration, social determinants of health, and social vulnerability index (SVI) in Durham County, North Carolina, USA. Random forest regression (RFE) and min-max scaling preprocessing were used to predict mpox vaccinations in Durham County at the census tract level. The top eleven most influential features and their correlations with mpox vaccination were calculated. Non-Hispanic white individuals, males, and those between the ages of 20 and 40 years were overrepresented in mpox vaccine reception in Durham County. Surprisingly, lacking a high school diploma, lacking health insurance, lacking a household vehicle, and living below the poverty line were all positively associated with receiving the mpox vaccine. Being a Black or African American and Hispanic or Latino individual was also positively associated with receiving the mpox vaccine. Vaccine outreach efforts in Durham County, North Carolina, had success in reaching at-risk individuals, including socially vulnerable individuals. Future research should focus more specifically on how social vulnerability relates to vaccine reception for vaccine-preventable diseases.

The Micromobility Tendencies of People and Their Transport Behavior

Addressing transport in cities requires a change in people’s behavior and a better distribution of different transport modes’ performances—a change in the modal split. This article focuses on detailed research on the transport behaviors of residents and their attitudes towards possible changes. We developed a questionnaire and distributed it online and physically. The data came from an anonymous survey, and basic statistical methods and a correlation analysis were applied to them. One of the research tasks was to find the correlations between individual characteristics. The analysis showed that the respondents’ education influenced their opinions about transport behavior. The results showed that the most common means of shared mobility was bicycles. The paper contains detailed results regarding the use of private cars and transport behavior in general. In addition, the study presents other significant findings regarding the average number of vehicles in households, the types of vehicles, and their usage patterns. The results of our study are useful for practical applications, because they describe traffic behavior patterns and can improve future decision making and transport planning.

Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership

Future smart transportation systems are anticipated to integrate automation and sharing of vehicles. Responding to the expected changes in shared mobility services, this study used representative data from over 4100 households in California to examine consumers' affinity to use shared automated vehicles (SAV) and their willingness to renounce existing vehicle(s) in the presence of SAVs - two interlinked factors determining the long-term success of SAVs. Results of the study showed that around 50 % of the households reported willingness to use SAVs but unwillingness to renounce their current vehicle(s). As innovators, another 9.1 % stated their affinity to use SAVs and renounce existing vehicle(s) but with heterogeneity across vehicle ownership levels. Random parameter logit models revealed that households using existing vehicles to work for ride hailing companies were significantly less likely to use SAVs and renounce their current vehicle(s). Households who had high awareness about self-driving cars, possessed full electric vehicles, used sustainable work travel modes (bike, e-bike, bikeshare), had greater number of leased vehicles and ridesharing trips were more likely to use SAVs and renounce current vehicle(s). Significant unobserved preference heterogeneity was recorded in the effects of behavioral and sociodemographic correlates. The study contributes by shedding new light on the behavioral determinants of the vehicle ownership impacts of SAVs. We discuss policy implications of the key findings.

Teleworking behavior pre-, during, and expected post-COVID: Identification and empirical description of trajectory types

Teleworking is a major lifestyle change whose adoption expanded substantially during the COVID-19 pandemic, and which might permanently change the working habits of some. To understand how much during-COVID teleworking behavior will “stick” in the post-pandemic world, this study explores pre-, during-, and expected post-COVID teleworking frequency trajectories based on during-COVID survey responses, which were weighted to represent the population shares of non–/non-usual/usual teleworkers (TWers). Taking respondents’ self-reported expectations at face value, post-COVID teleworking frequency will be 2.6–3.3 times that of pre-COVID, but we suspect that those expectations are over-optimistic. We developed a typology based on the teleworking trajectories, and identified eight mutually exclusive and collectively exhaustive worker segments: Climbers (13.6%), Plateauers (8.4%), Cappers (12.6%), Stable unables (43.1%), Stable non-choosers (13.5%), Stable non-usual TWers (1.5%), Stable usual TWers (3.2%), and Reducers (4.0%). We conducted three exploratory factor analyses and identified latent attitudinal constructs including 11 general, 7 work-related, and 7 telework-related attitudes. We profiled the eight segments regarding various attitudes and sociodemographic traits to reveal their potential connections to teleworking behavior, and to suggest further explorations with respect to residential location, vehicle ownership, and other travel patterns. Specifically, we see research needs pertaining to residential relocation and household vehicle ownership changes for segments expecting net increases in teleworking frequency between pre- and post-COVID. We also observed heterogeneity of teleworking adoption/frequency choices, attitudes, and travel behaviors among (tele)worker segments. Overall, this study deepens our understanding of post-COVID teleworking behavior changes, and provides a starting point for understanding the heterogeneity among (tele)workers.