Vehicle Purchase Research Articles

Forecasting the number of vehicle kilometers by applying the autoregression model, using Warsaw trams as an example

One of the biggest challenges of the 21st century is ecological responsibility. It also concerns the sustainable development of transport and the reduction of threats related to the negative impact of this phenomenon on the environment. A constant increase in transport congestion, atmospheric air pollution, and noise promotes the search for new solutions, especially in urban areas. One of the systematically implemented and improved ideas in this area is the development of urban transport systems. Their effectiveness and efficiency are evidenced by the level of meeting the transport needs of residents, with the optimal utilization of vehicles. The article analyses urban transport in Warsaw, focusing only on trams as the second most popular means of transport after wheeled vehicles. Two objectives of the study were adopted. The first was evaluating the current state and characteristics of the available options and indicating potential development directions, considering factors that determine it. The second goal was to select the appropriate model describing the number of vehicle kilometers accumulated by Warsaw trams in the years 2017-2019 and parametric identification of this model. The study allowed us to estimate and make a short-term forecast of transport services carried out by trams. The research has shown that the current situation regarding the performance of transport work by trams in Warsaw does not fit into the paradigm of sustainable transport development. This is due to the loss of vehicles from the records in the absence of new vehicle purchases. Additionally, the developed tool indicates a decrease in the number of vehicles-km performed in the following months and, thus, a reduction in the share of trams in transport in the Warsaw communication system. The identified problem (i.e., a downward trend in transport performance) is essential from the point of view of the quality of the system's operation and the ability to meet passengers' expectations. It also informs decision-makers about the need to implement changes leading to an increase in the share of tram transport, mainly in capacity and operating costs.

Predicting Electric Vehicle (EV) Buyers in India: A Machine Learning Approach.

Electric mobility has been around for a long time. In recent years, with advancements in technology, electric vehicles (EVs) have shown a new potential to meet many of the challenges being faced by humanity. These challenges include increasing dependence on fossil fuels, environmental concerns, challenges posed by rapid urbanization, urban mobility, and employment. However, the adoption of electric vehicles has remained challenging despite consumers having a positive attitude toward EVs and big policy pushes by governments in many countries. Marketers from the electric vehicle (EV) industry are finding it difficult to identify genuine buyers for their products. In this context, the present study attempts to develop a machine learning model to predict whether a person would “Buy” or “Won’t Buy” an electric vehicle in India. To develop the model, an exploration of EV context was done first by conducting a text analysis of online content relating to electric vehicles. The objective was to find frequently occurring words to gain a meaningful understanding of the consumer’s interests and concerns relating to electric vehicles. The machine learning model indicates that age, gender, income, level of environmental concerns, vehicle cost, running cost, vehicle performance, driving range, and mass behavior are significant predictors of electrical vehicle purchase in India. The level of education, employment, and government subsidy are not significant predictors of EV uptake.

Influential Factor Analysis and Prediction on Initial Metro Network Ridership in Xi’an, China

To satisfy the adaptability of forecasting the short-term and abrupt volume of the initial metro network, we build the multiple enter linear regression (MELR) model to explore the determinants and forecast the intensity during the twice expansion of the initial metro network in Xi’an. We further compare the prediction of the metro transport capacity between the MELR models with exponential smoothing and autoregressive integrated moving average (ARIMA) models. Results show that the passenger intensity significantly fluctuates with the months and days, and MELR model is more adapted for the short-term prediction of the abrupt volume than the ARIMA model during the new metro line opening and the old line expands, which avoids the drawback of time series models that need a huge database. This study provides a guide for the prediction of initial metro network volume and accurate purchase of the rail vehicles during the metro planning and expends stages.

Impact analysis of residential relocation on ownership, usage, and carbon-dioxide emissions of private cars

Even though the effects of residential relocation on travel behaviours have been explored worldwide, few studies discuss the impact of relocation on carbon dioxide emissions from travel. This study utilises the Fifth Travel Survey of Beijing Inhabitants and land-use data to build a structural equation model with latent variables that explore the effects of relocating direction, changes in built environments, commute distance, and household socio-economic attributes on changes in household car carbon dioxide emissions before and after moving. It is concluded that the change in commuting distance, private vehicle purchases, family size, electric bicycles, and public transit smart cards have direct effects on the change in carbon emissions, while the relocating direction, built environment latent variables, home size, and the number of adolescents in the family are found to have indirect effects. In addition, recommendations for reducing carbon emissions from private car travel in response to urbanisation are proposed. This study can help urban planners and managers understand the impact of residential relocation on vehicle ownership, usage, and the corresponding carbon dioxide emissions, and can also provide a reference to formulate planning and management policies on sustainable city and green transportation.

Analyzing Commute Mode Choice Using the LCNL Model in the Post-COVID-19 Era: Evidence from China.

The purpose of this paper is to gain an insight into commuting and travel mode choices in the post-COVID-19 era. The surveys are divided into two waves in Qingdao, China: the first-wave questionnaires were collected under the background of a three-month zero growth of cases; the second wave was implemented after the new confirmed cases of COVID-19. The latent class nested logit (LCNL) model is applied to capture heterogeneous characteristics among the various classes. The results indicate that age, income, household composition, and the frequency of use of travel modes are latent factors that impact users’ attitudes toward mass transit and the private car nests when undergoing the shock of the COVID-19 pandemic. Individuals’ trepidation regarding health risks began to fade, but this is still a vital consideration in terms of mode choice and the purchase of vehicles. Moreover, economic reinvigoration, the increase in car ownership, and an increase in the desire to purchase a car may result in great challenges for urban traffic networks.

Driving through dense fog: a study of the effects and control of sustainable public procurement of electric cars

Governments are large buyers of vehicles, thus contributing to pollution. To promote sustainability, policies have been shaped to replace government-owned fossil fuel cars with electric cars. Public procurement is seen as a strategic tool for the government to transition. This study identifies a research gap due to a lack of studies on how stakeholders at different levels identify and calculate the sustainability effects of public procurement of cars. Our approach uses a multilevel perspective to explore how various stakeholders perceive and assess the effects of sustainable public procurement. The data were obtained through a qualitative research design with documents and semi-structured interviews with stakeholders in Norway ranging from government agencies, public procurement officers, car suppliers, and end-users. (End-users in this setting are the ones who ultimately use the vehicles). The study’s findings are two-fold. First, it contributes to understanding that perceived effects of sustainable public procurement vary from the stakeholders' perspectives and that public procurement initiatives perceive to have cultural effects in addition to innovation, environmental, economic, and social impacts. Second, it contributes to understanding the importance of feedback mechanisms in public procurement to align the assessment of the effects. A better understanding of how effects are identified, and improved feedback mechanisms could help government representatives control the procurement system and accomplish the intended effects.

Analysis of Influencing Factors in Purchasing Electric Vehicles Using a Structural Equation Model: Focused on Suwon City

The global automobile market is promoting the introduction of eco-friendly vehicles such as electric vehicles and hydrogen vehicles. However, disadvantages such as expensive prices and limited mileage compared to internal combustion engine vehicles have become obstacles to the expansion of eco-friendly vehicles. Therefore, in this study, a survey was conducted on the purchase of electric vehicles for citizens of Suwon. Using the survey data, a structural equation model was constructed to analyze the factors affecting the purchase of electric vehicles, which are eco-friendly vehicles. The results indicate that a lack of information and government policy on EV, the level of EV recognition and subsidy policy do not have an effect on EV purchase. However, charging infrastructure, battery performance and safety, operating conditions including ramps or use of heaters and air conditioners, subsidy effects and charging services demonstrate positive effects on EV purchase. Using direct and indirect effect analysis, the study shows that higher government subsidy and visiting charging services are the two most influential factors on EV purchase, followed by EV driving environment, charging infrastructure, battery performance and safety, and a lack of information and electric vehicle supply policy.

Effects of providing total cost of ownership information on below-40 young consumers’ intent to purchase an electric vehicle: A case study in China

Compared to gasoline vehicles, electric vehicles have an advantage that financial benefits from reduced energy consumption offset much or all of the initial price premium. However, currently there exists consumer misconception of this advantage. One strategy to address this misconception is to provide life cycle cost information to consumers. This study investigates how providing information about 5-year fuel cost and total cost of ownership affects the stated preferences of ‘below 40’ young consumers for a gasoline, plug-in hybrid, or battery electric vehicle in the context of China. A rank-ordered logit model is developed to model consumers' stated preferences, based on data collected through a stated preference experiment. This study evidenced the significant positive effect of providing 5-year fuel cost and total cost of ownership information on the stated preference for electric vehicle. Socioeconomic attributes such as gender and education level are also found to have effects on consumers' electric vehicle purchasing intent. The results enhance the understanding of consumers' complex electric vehicle purchase decisions and have policy implications for electric vehicle promotion.

Analysis of the barriers to the adoption of zero-emission vehicles in Spain

This paper investigates Spanish drivers’ perceptions of the main barriers existing in Spain to the purchase of zero-emission vehicles (ZEVs). Following a comprehensive literature review in this field, this paper quantifies, by means of a survey conducted in Spain of 1474 Spanish drivers, the drivers’ desired levels for each barrier to consider ZEVs in their next purchase decision to replace their current usually-used car. The analysis of these reported levels with latent class cluster models revealed the existence, in the sample, of groups of consumers with homogeneous preferences regarding the barriers. These groups differ in terms of individuals’ characteristics, the car to be replaced, and journeys made with it. The most flexible groups comprise individuals with a significant knowledge of ZEVs, which underscores the importance of educational policies for the promotion of the use of ZEVs. The desired levels of the barriers for each group are confronted with the current status of the barriers for certain ZEVs. This comparison reveals that Fuel Cell Electric Vehicles (FCEVs) would have great potential if they received government support, because their only barriers are economic (purchase price and fuel availability). This paper also quantifies the effects that purchase incentives and infrastructure investment policies could have in terms of higher FCEV penetration rates.

How to support EV adoption: Tradeoffs between charging infrastructure investments and vehicle subsidies in California

Supporting the adoption of zero-emission vehicle (ZEVs), including plug-in electric vehicles (EVs), has become a priority for governments due to their ability to reduce petroleum demand, improve air quality, and reduce carbon dioxide (CO2) emissions. Optimal strategies to accelerate EV adoption must weigh the relative value of alternative policy mechanisms to consumers, including public charging infrastructure and vehicle purchase subsidies. We use a historically validated light-duty vehicle consumer choice tool, the ADOPT model, to simulate personal light-duty vehicle adoption and related emissions in California. ADOPT is updated to incorporate a quantification of the tangible value of public charging infrastructure, allowing us to simulate the impact of investments in public charging infrastructure and vehicle purchase subsidies under different scenarios. We show that both policies result in increased EV adoption, with the most effective policy varying depending on vehicle technology assumptions. Under conservative technology improvement assumptions, infrastructure investments are most effective in promoting EV sales and reducing CO2 emissions, while under optimistic technology improvement assumptions a combination of infrastructure and subsidies best supports EV sales and CO2 emission reductions.

Factors Influencing Electrification of Urban Freight Vehicles: A Case Study of Three Indian Cities - Ahmedabad, Delhi, and Surat

India’s high levels of transport-related air pollution endanger public health. The transition to electric mobility will address this challenge while also reducing the country’s dependence on oil imports. The Government of India has offered incentives to electric vehicle manufacturers and purchasers that promote faster adoption of electric vehicles. The states have framed electric vehicle policies offering additional fiscal and non-fiscal incentives supporting this objective, but the adoption rate of electric vehicles in India is far below expectations. This paper aims to understand the factors that influence the electrification of urban freight vehicles. It does so with reference to urban freight movements linked to textile markets in Ahmedabad and Surat, and the fruit and vegetable market in Delhi. By studying these, it attempts to assess the impact of key policy and market conditions on the adoption of electric vehicles. Based on stakeholder perceptions, it further tries to identify the enablers and barriers to the electrification of urban freight vehicles.

Factors Affecting Vehicle Purchase in Major Cities

Nowadays, personal vehicles have become synonymous with the modern city market. The development and splendor of the city can also portray by the use of such vehicles. The purpose of this study was to analyze the factors affecting vehicle buying in major cities. This research used descriptive and causal research design. Primary data were collected. Survey research was administered and 2000 responses were collected. A PLS-SEM tool was used to test validity, reliability and path of advance modeling. This research confirmed expected features, price, dealer/showroom influence has significant influence on car buying. After sales service has no effect on car buying. Marketers should identify target group who showed interest on cars and marketing programs should be directed to them.

COMPARATIVE STUDY OF DETERMINANTS OF THE MALAYSIAN HOUSEHOLD NONPERFORMING LOANS: EVIDENCE FROM NARDL

This study compares the sensitivity of each household nonperforming loans (NPLs) category in Malaysia, allowing asymmetry across different household credit types, credit cards, personal uses, purchase of residential properties and purchase of transport vehicles. Differences in the impact of household debt on the Malaysian household NPLs are found evident. In the sample period from 2006 to 2018, the findings suggest an asymmetric impact of credit card debts on the household NPLs. This linkage is explained by only short-run negative changes in credit card outstanding loans. Besides that, the residential property loans behave asymmetrically in the long and short run, with higher impact sources from the negative changes. In a disaggregated NPL analysis focusing on a different type of loan portfolio, the asymmetry further enhances policy and regulation-making in managing household credit risk.

Assessing consumer valuations of future costs versus purchase prices in Japan's auto market

To tax vehicles such that market distortions are minimized and environmental goals are promoted, policymakers must determine whether consumers correctly assess automobile prices versus future costs of ownership. Drawing from data spanning 2006 to 2015 for Japan's new car market, we apply the aggregate random coefficients logit model to estimate consumer demand with rich substitution patterns. We find that Japanese consumers primarily focused on fuel costs and future registration taxes followed by vehicle purchase prices. This finding implies that promoting eco-friendly vehicles requires Japanese policymakers' attention to fuel and vehicle registration taxes. This finding also suggests that salient features of preferential tax treatment in the auto market distort consumer preferences toward future costs. Counterfactual experiments under a fixed amount of tax revenue reveal that raising fuel taxes improves average fuel economy more than registration taxes do at the expense of broader social welfare.

SISTEM INFORMASI STAFF LOAN AND DOCUMENTATION (SLADU) PADA PT. BANK DBS INDONESIA

It is undeniable that technological developments that occur at this time can help humans in doing their jobs much faster and easier. Currently, employees who work at Bank DBS Indonesia are given special loan facilities for employees with much lower interest rates than general loans, such as personal loans, home renovation loans, vehicle purchase loans and home ownership loans. At this time the process running on the team of staff loan and documentation PT Bank DBS Indonesia is still manual such as inputting staff loan data, searching for staff covenant documents if there are staff who want to make tax payments or take covenant documents, and reports to see which staff have not submitted the covenant document according to the deadline is still manual, causing delays in the company's performance and has not been fully able to meet the system needs desired by the company. With a computer-based information system, the loan data storage process will be neater, safer and also efficient, because the data will be stored in files on a computer that can be viewed again or printed when needed. Thus, the staff in theteam of loan and documentation PT. Bank DBS Indonesia can collect data on loan data for employees who have loans and view data and reports quickly, accurately and can be accounted for.

The Green Light on Buying a Car: How Consumer Decision-Making Interacts with Environmental Attributes in the New Vehicle Purchase Process.

Although it is commonly understood that the average U.S. new vehicle buyer ranks price and safety above environmental attributes, a stated ranking of one shopping criterion above another is not necessarily maintained when consumers make an actual purchase decision. In fact, the distribution of shopping criteria rankings is not well understood, and it is unclear how rankings translate to the attributes of purchased vehicles. This raises several related questions: What is the distribution of shopping criteria rankings across the U.S. and how do they differ among demographic groups and purchasers of different vehicle fuel types or body styles?How do consumers weigh their purchase criteria?How does the environmental impact of a vehicle rank as a purchase criterion for U.S. new vehicle buyers, and its importance differ among gender, age, or income groups?Do purchase criteria differ for consumers who state that they value the environment?Is a consumer's shopping criteria ranking of environmental attributes reflected in the vehicles they consider and ultimately purchase? We explore these issues using data from an extensive survey of new vehicle buyers in 2014, 2015, and 2016 (approximately 250,000 respondents per year). We broadly find the environmental criterion outranked by preference for safety and performance, but different patterns emerge across groups defined by household income, purchased vehicle fuel type, and other measures of respondent attitude toward the environment. Stated preferences for environmental attributes align with higher fuel economy and greater likelihood of electric or hybrid fuel type within considered and purchased vehicles.

Analysis of barriers to adopt electric vehicles in India using fuzzy DEMATEL and Relative importance Index approaches

Electric vehicles (EVs) are introduced to mitigate air pollution, greenhouse gas (GHG) emissions and reduce the dependency on fossil fuels. But the present EV sales are not impressive in India compared with other countries. Previous studies have attempted to identify the challenges to EV adoption based on the opinions of experts or road users. But it is essential to study both expert and users’ perspectives to understand the actual scenario. In addition, the government of India announced a revised FAME II subsidy for EV purchase in 2021, which has an additional incentive on the purchase cost. Thus, this study aims to identify the key potential barriers based on expert and customers’ perspectives. For this purpose, data were collected from the potential experts who have rich experience in EV, and 361 samples were collected from the customers who visited the EV showroom for an inquiry and test drive in Ahmedabad city, India. This study adopted the fuzzy DEMATEL method to identify the potential barriers based on expert opinion data and the Relative Importance Index (RII) method for prioritizing the barriers based on customers’ perspectives. The results show that the Higher initial purchase cost of a vehicle, Many EV manufacturers are new companies, Technical specification of the vehicle (power, acceleration, etc.), Less Mileage on a full charge of a battery, Non-Reliable battery technology, Lesser Adaptive competitive vehicle design, Limited availability of local technician, Limited availability of public charging stations, Higher waiting time at charging station if many vehicles are there, Safety concern while charging, Non-availability of charging facility on a long trip, and lack of understanding of subsidy policies (vehicle purchase subsidy and other EV incentives by government) are the major significant barrier in each group. Also, the revised FAME II subsidy policy and improvement on charging facilities have a significant positive effect on EV adoption. The outcome of this study will help the transport planners to develop necessary infrastructure facilities to boost EV. Also, this study outcome will help the EV policy makers and manufacturers to understand the consumer requirements.

Why marginal CO2 emissions are not decreasing for US electricity: Estimates and implications for climate policy

Marginal emissions of CO2 from the electricity sector are critical for evaluating climate policies that rely on shifts in electricity demand or supply. This paper provides estimates of marginal CO2 emissions from US electricity generation using the most recently available and comprehensive data. The estimates vary by region, hour of the day, and year to year over the last decade. We identify an important and somewhat counterintuitive finding: While average emissions have decreased substantially over the last decade (28% nationally), marginal emissions have increased (7% nationally). We show that underlying these trends is primarily a shift toward greater reliance on coal to satisfy marginal electricity use. We apply our estimates to an analysis of the Biden administration's target of having electric vehicles (EVs) make up 50% of new vehicle purchases by 2030. We find that, without significant and concurrent changes to the electricity sector, the increase in electricity emissions is likely to offset more than half of the emission reductions from having fewer gasoline-powered vehicles on the road. Moreover, using average rather than marginal emissions to predict the impacts significantly overestimates the emission benefits. Overall, we find that the promise of EVs for reducing emissions depends, to a large degree, on complementary policies that decarbonize both average and marginal emissions in the electricity sector.

A Study on Consumers’ Intention and Buying Behaviour towards the Purchase of Electric Vehicles with Special Reference to Coimbatore City

Electric Vehicles are advanced technology for success in tenable transport sector in the future due to the cost effective low maintenance, Easy driving.This paper present briefly some exposure of Electric Vehicle including its Industries consumer perceptions and adaptation of Electric Vehicle behaviour, attitudes towards Electric Vehicle, environment benefit of Electric Vehicle, and its battery life. The government has to take initial measures to develop the usage of Electric Vehicle among consumer and the price of Electric Vehicles should be affordable by the consumers. The automobile industries have to develop the Electric Vehicle production.

Urban traffic regulation and air pollution: A case study of urban motor vehicle restriction policy

Many local governments have taken various regulations to control heavy traffic and alleviate vehicle pollution emissions continuously. In practice, one of the most common and widely used regulations is the motor vehicle restriction policy (MVR policy). But it is unknown whether a motor vehicle restriction policy can effectively alleviate traffic pollution. Moreover, few studies considered the duration of the policy and distinct the effects of different types of MVR policies. In this paper, we first classify the MVR policies into consecutive and intermittent restrictions policies and analyze their different effects on air pollution based on the difference-in-differences method. Further, we take the Mediation Effect Model to verify the conduction mechanism of the impact. The result shows that both consecutive and intermittent policies can improve air quality, while the latter's effect is more significant. Moreover, as for the impact's conduction mechanism, we find that consecutive policies alleviate pollution by changing driving preference and vehicle purchase. Still, the intermittent policies alleviate pollution by promptly easing traffic jams and avoiding unnecessary vehicle emissions of the idling engine. This founding provides a decision-making basis to the government for more efficient and reasonable regulation policies.