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When it comes to the car buying and selling market it is constantly developing and increasing at a fast rate. However, the traditional car market system is very centralized, has high transaction costs, less transparency, and lacks proper management of physical records. These parameters play a very important role when it comes to buying or selling. But these flaws in traditional systems can be removed using blockchain technology. Blockchain provides high security, and more transparency, and reduces the cost required for the intermediaries in the process. This paper proposes a decentralized system AutoXchange which helps users to buy or resell their cars without any complications like fraud or mismanagement. The system provides security and transparency between the parties. AutoXchange provides features like vehicle registration, making a vehicle for sale, or in case of a buyer sending a request to the owner as an interest in buying his car, also provides a secure platform to perform transactions of money. As this system is decentralized the intermediary cost are also removed. [ABSTRACT FROM AUTHOR]

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%. [ABSTRACT FROM AUTHOR]

In the city of Bandung, it is recorded that until February 2023 as many as 500 thousand private cars crowded the streets in the city of Bandung. People's needs for private cars are met through the purchase of new cars from dealers or used purchases. As a dealer, the main task is to meet car sales targets every month and year. Suzuki dealers, especially in Bandung, do not have solid information about what type of car is most liked by the people of Bandung, what is the background of the customers and what marketing efforts are most optimal to increase sales. Suzuki car sales data for the June-October 2023 period was analyzed as many as 165 sales from various types of cars, customer domicile, customer's profession and marketing efforts carried out until the purchase occurred and the choice of payment method. In this paper, a clustering analysis of the K-means method with 4 clusters with car type, customer domicile location, marketing effort, customer profession, transmission type and payment method is made. Analysis performed with IBM SPSS v.29 program. The type of Carry passenger vehicle is the choice of many Suzuki customers in Bandung and Suzuki customers mostly come from the people of Bandung and around Bandung who work as entrepreneurs and traders. Suzuki Bandung needs to maintain and improve Canvansing as an effort to acquire customers as can be seen from the analysis of customer segmentation data in this paper [ABSTRACT FROM AUTHOR]

This paper provides insights into differences in carsharing users' attitudes, motives for joining carsharing, and transport behaviour between users with and without another car at their disposal. It builds on revealed and stated data about members of the oldest carsharing company in the Czech Republic. Carsharing adopters without a car utilise shared cars more intensively than carsharing users with a car available in their household. On the other hand, unlike the second group, they drive fewer kilometres by car in total. The car availability in households also influences the shift in car use after joining carsharing. The sale of a car thanks to adopting carsharing is a factor leading to a decrease in overall car use. Those who have a car at their disposal within their household have a lower probability of decreasing kilometres driven after joining carsharing. Households without an additional car available seem to be less car-dependent on average than those utilising carsharing as a second or third car. They tend to be more environmentally conscious and more inclined towards policies supporting alternative modes and restricting private car use, although both groups share these beliefs. The findings open a debate over whether carsharing increases the legitimacy of restrictive transport measures against private car ownership and use. [ABSTRACT FROM AUTHOR]

The car market in Mexico has undergone substantial change over the last twenty years, as sales have increased dramatically and as policy measures have been introduced to improve fuel economy so that decarbonization targets can be achieved. The argument presented in this paper is that overall fuel economy is driven by vehicle sales and the fuel economy standards imposed. In addition, this picture is complicated by the purchasing preferences of buyers, and this might reduce the effectiveness of the policy measures introduced. A case study approach allows analysis of the shifts in transport policy (2003–2020) to be undertaken by linking the fuel consumption of cars (L/100 km) to the purchasing patterns of consumers, and from this estimate the levels of CO2 emissions. From the empirical analysis, it is found that, although there have been fuel economy gains every year, this is countered by (a) increasing sales of SUVs, and (b) a car market that is increasingly being dominated by larger cars. The current fuel standards are not sufficient to control the continued growth in fuel consumption, and levels of carbon emissions are continuing to increase. In conclusion, tighter emissions standards are needed, together with stronger governance structures and a range of further policy measures to improve car efficiencies and limit growth of the use of larger vehicles. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of private cars and the development of used car market, used car become the first choice for the customers for various reasons like financial capability, overpriced car and so on. The online used car platform provides both the seller and buyer person to person trade. For such type of systems, the accuracy of used car price evalution check whether the seller and buyer can get more efficient platform. For this paper, we have conducted a comparative study on the performance of regression based on supervised machine learning models. Each model is trained using data from the used car market collected from the German e-commerce website. Car price prediction enable the customers to make decisions based on different inputs or factors namely name of the car, Year of manufacturing, type of fuel, Price range, etc. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to identify relationships between web data and social/economic variables, such as inflation. Our method enables the selection of relevant time series from a large data sample by employing a criterion based on a few hypotheses regarding their dynamics. Specifically, we examine the correlation between web activities and the dynamics of two macroeconomic variables: the unemployment rate and US automotive sales. We demonstrate how changes in the search volume of specific keywords, as measured by corresponding Google Trends data, are reflected in the underlying dynamics of these variables. The findings presented in this paper, along with the versatility of our approach, suggest the potential extension of this study to other economic variables. [ABSTRACT FROM AUTHOR]

With China's vigorous implementation of the two-carbon policy. the automotive industry is transitioning from traditional to alternative energy sources. Accurate forecasting of new energy vehicle sales can provide statistical support for policy planning. This paper proposed a grey Lotka-Volterra model based on an HP filter to forecast new energ vehicle sales trends. The proposed model overcomes the limitations of the original model in forecasting cyclical data and improves its applicability in small sample data systems. And the competitive forecasting model allows us lo explore the market competition or cooperation between various vehicle types. Compared to the two existing models, the new model perfbrms better in forecasting vehicle sales. The study further applies the LCA method to assess the scale of energy consuniption and GHG emissions in the automotive sector. The prediction and evaluation results show that new energy vehicle sales will surpass traditional fuel vehicles by 2035 and dominate the vehicle market before 2050. Unfurtunately, vehicle electrification has not significantly reduced the transportation industry's environmental concerns. To fully utilize the potential for enet·gy-savings and emission-reduction of new energy vehicles, it is urgent to pursue technological advancements in battery production and power-generating structure. [ABSTRACT FROM AUTHOR]

This paper aims to research the influence of several macroeconomic variables on German premium car brands Audi, BMW, and Mercedes-Benz in the Malaysian automotive market between 2010 and 2019. The qualitative analysis determined the sales of premium passenger vehicles in units produced by the top German car manufacturers as the dependant variable. In contrast, the PESTEL analysis determined the relevant macroeconomic variables, namely the gross domestic product, interest rate, consumer price index, exchange rate, and the unemployment rate, as the independent variables. Quantitative analysis has been conducted in the form of a multiple regression analysis. The multiple regression results show that none of the five macroeconomic variables have influenced the sales of Audi, BMW, and Mercedes-Benz passenger vehicles in Malaysia. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Estudios Andaluces is the property of Revista de Estudios Andaluces and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the sales of vehicles in the US are examined to understand if the shock caused by the current COVID-19 pandemic has had permanent or transitory effects on its subsequent evolution. Using monthly data from January 1976 until April 2021 and fractional integration methods, our results indicate that the series reverts and the shocks tend to disappear in the long run, even when they appear to be long lived. The results also indicate that the COVID-19 pandemic has not increased the degree of persistence of the series but, unexpectedly, has slightly reduced its dependence. Thus, shocks are transitory, long lived but, as time goes by, the recovery seems to be faster, which is possibly a sign of the strength of the industry. [ABSTRACT FROM AUTHOR]

Past research in operations management and marketing on inventory levels and product variety has predominantly focused on their effects on brand performance indicators, such as sales and market share while overlooking the influence on consumers' perceptions of brands. Brand perceptions, encompassing reputation, quality, credibility, and emotional associations, go beyond typical revenue metrics and offer foresight into a brand's future performance. Hence, understanding the effects of inventory and product variety on brand perceptions is crucial, and that constitutes the main contribution of this paper. Through a consumer-facing automobile search platform, we collect data on new cars' inventories of more than 20,000 dealerships in the United States from August 2020 to March 2021. We measure brand perceptions using 273,991 responses by in-market consumers collected by YouGov. To address endogeneity concerns, we model the effects of inventory and variety on perceived brand strength using three different empirical approaches: (1) High-dimensional fixed effects, (2) instrumental variables, and (3) causal forest. Across all analyses, we find that inventory has a positive effect on perceived brand strength but the main effect of product variety is not significant. Our second contribution is related to the fact that past research on inventory and variety does not, for the most part, investigate systematic heterogeneity due to brand- or consumer-specific factors that impact the effectiveness of inventory or variety; to help fill this gap in the literature, we investigate the role of two important and theoretically motivated moderators: Consumer income and luxury status of the brand. We find that consumers' income levels and brands' luxury status negatively (positively) moderate the effects of product inventory (variety) on perceived brand strength. Our results have managerial implications for effective assortment planning under scarcity, determining the right range of product offerings for luxury versus nonluxury brands, optimizing the customer's online browsing experience, and targeting advertisements based on brand and consumer characteristics. [ABSTRACT FROM AUTHOR]

The article aims to investigate the level of adoption of revenue management activities in small and medium-sized hotels in the Czech Republic. One hundred fifty members of the Association of Hotels and Restaurants from the Czech Republic were questioned. The questionnaire was derived from the MERMI model used to evaluate the adoption of revenue management activities in hotel operations. For this research, the scope was reduced to fit better the focus on the SME based on the Delphi method. The results show the low level of adoption of revenue management in this segment, where 23 % of the respondents stated no interest in revenue management, and only 17 % had the revenue manager in their organisational chart. The GM or marketing and sales managers prosecute the revenue management activities for the rest of the hotels. As revenue management is one of the departments highly supported by information and communication technologies, their adoption is low, as 30 % of the respondents from small hotels are using these technologies. It is crucial to mention that these are not only the revenue management tools but also the WBEs, Channel Managers or CRMs. The results support the increasing gap between academia and revenue management practitioners. The paper's novelty lies in its focus on the SME segment, where the previous studies mainly focused on the applications in upper-scale and luxurious hotels. The SME segment, with its majority in the segment (in the context of the number of hotels), was omitted entirely. Therefore, the paper showcases a significant theoretical and practical knowledge gap. Further research should focus on the SME segment and deliver actionable proposals for the hoteliers losing competitiveness by omitting the revenue management principles. [ABSTRACT FROM AUTHOR]

With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference. [ABSTRACT FROM AUTHOR]

Objective - This study was initiated to develop an alternative positive car-user experience to enhance automotive sales by considering customers' desires. Method - The study employed the "Systematic Literature Review Synthesis Process," harnessing the online EAGLE Navigator System to document the literature review synthesis process to identify relevant literature and establish a theoretical background for research ideation. Findings -- A theoretical model for combining personal cultural customization, simplicity, accessibility, affordable technology, and sensory modalities could improve the in-car experience and satisfaction of younger Chinese car buyers, thus leading to satisfying car-user purchasing experiences. Novelty - This study breaks new ground by offering a holistic perspective on enhancing the automotive user experience within the context of the Chinese market. An innovative blend of cultural customization and technology accessibility is introduced, which is poised to reshape the automobile industry's engagement with younger consumers in China. [ABSTRACT FROM AUTHOR]

Copyright of Osjecki Matematicki List is the property of Osjecki Matematicki List and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The promotion and use of new energy vehicles are important means to reduce carbon emissions. However, its effect is usually affected by the rebound effect. The current research does not consider the impact of the substitution effect of new energy vehicles on traditional fuel vehicles to the carbon emission rebound effect, including its situation in different scenarios. This paper considers the purchase stage, use stage of the new energy vehicles and constructs a comprehensive two-stage almost ideal demand system model. The rebound effect of carbon emissions from 2014 to 2019 and different scenarios from 2020 to 2030 is quantified. The results show that the rebound effect of carbon emissions without car purchase subsidies is 4%–9% less than that with car purchase subsidies in the corresponding year. In the prediction results of the rebound effect under different future scenarios, the values of partial rebound effects are more than 80%, and the values of backfire effects are less than 115%, which indicates that the rebound effect of carbon emissions shows a convergence trend. In the future, the rebound effect of carbon emissions will show a trend of convergence. When the total vehicle sales remain unchanged, the replacement proportion of new energy vehicles reaches 85%, and there is no subsidy policy, the rebound effect of carbon emissions is the smallest, indicating that this scenario is the most favorable for reducing carbon emissions and reducing environmental pressure. [ABSTRACT FROM AUTHOR]

We promote a view that more attention should be given to the freight sector in order to recognise that many initiatives designed to impact on passenger travel do also impact on the performance of the movement of freight vehicles and hence the ability to distribute commodities from the locations in which they are generated. This paper develops a practical freight demand model system and applies the models within an integrated passenger and freight model system for the Greater Sydney Metropolitan Area using a distance based charge for trucks and cars as a way of highlighting the importance of not ignoring truck traffic when assessing road pricing reform in the car passenger sector. [ABSTRACT FROM AUTHOR]

The scenario of the Automobile Industry has changed, not only in India but also for the world as a whole. Rise in demand for more sustainable mobility and vehicles with increased usage of renewable energies have given rise to a revolution. This revolution has not only been affecting mankind for better implementation of resources but has also shown tremendous greener and cleaner effects on the flora and fauna of the land as a whole. There has been electrification of vehicles, addition of superior systems to generate lesser harmful effluents and also changing the guidelines of emissions to cleaner and more sustainable ones. The purpose of this paper is to review and state the existence of different pieces of literature for the transition from Bharat Stage-IV (BS-IV) to Bharat Stage-VI (BS-VI). Much has been written about the transition to BS-VI, the difficulties, and the positive effects on vehicle reliability, economy and the environment. In particular, this work explores the parallels between BS-VI and the corresponding Euro norms and their sequence of implementation. The aforesaid transition has increased the number of oil refineries in India that produce BS-VI-compliant fuels, keeping in mind the increased need for it. This paper explores the current state of the literature and throws light on many of the questions regarding this significant transition. The authors have tried to study the ongoing issues including the stress of inventory management of dieselrun passenger cars, the different refinements required to process and manufacture the BS-VI-compliant vehicles, people's reaction on the changes brought in by the issuing bodies with respect to the norms, to name a few. The focus of the research has been on Bharat Stage emission norms, their implementation challenges and automobile pollution in India to name a few. The authors also discuss the needs, advantages and challenges faced by the industry, producers and customers and conclude that this change is one of the reasons for the decline in car sales in India as it may affect the buying power of the customers. [ABSTRACT FROM AUTHOR]

This paper estimates the reference-price effects in consumers' car purchase decisions exploiting a unique panel data-set on the non-negotiable daily list prices for used cars advertised by a large national dealership. Guided by the theory of Köszegi and Rabin (The Quarterly Journal of Economics, 1133–1165, 2006), the paper identifies the reference price for a car as the car's list price on the previous day, a point with a large probability mass in car buyers' price expectations. The paper finds that — controlling for a car's actual price, its detailed attributes, the competitive environment, and a rich set of fixed effects — a positive (negative) deviation of the car's actual price from the previous day's list price lowers (increases) its daily sale probability significantly more than what can be accounted for by the standard effect of the car's resulting actual price. The magnitudes of the estimated reference-price effects are comparable to that of the standard price effect and are significantly stronger for cars with more exposures to prior consumer search. Implications for the dynamic pricing strategy for car dealers (and retailers in general) and inflation dynamics are discussed. [ABSTRACT FROM AUTHOR]

This paper studies the sharing mode selection and optimal pricing for an electric vehicle manufacturer (EVM). We evaluate three sharing modes, i.e. Mode N, car sales business only, Mode S, car sales business and car-sharing service provided by a self-built sharing platform, and Mode T, car sales business and car-sharing service provided by a third-party sharing platform. Further, we consider two types of government subsidy policies: purchase and production subsidies. We find that for purchase subsidies, the EVM should select Mode S if the transaction cost per sharing service is low, and select Mode T otherwise; for production subsidies, the EVM should select Mode S. Regardless of the subsidy policy, the optimal sales prices of EVs under Mode N and Mode S are the same, but both are lower than that under Mode T. The optimal rental price of EVs under Mode S is lower than that under Mode T. [ABSTRACT FROM AUTHOR]

Road transport accounts for around 12% of global greenhouse gas (GHG) emissions. As many high-income countries are moving to decarbonise their road vehicle fleets, Kyrgyzstan – with rapid growth in passenger car ownership – is seeing a significant increase in transport-related GHG emissions and air pollution in urban areas. Whilst a transition to electric vehicles (EVs) is a key part of Kyrgyzstan's Nationally Determined Contribution to the Paris Agreement, the potential for successful EV deployment in the region is under-researched. To fill this research gap, this paper presents an assessment of the potential for EV deployment in Kyrgyzstan. Firstly, we present an investigation of the policy and institutional landscape relating to transport and the promotion of EVs in Kyrgyzstan. Secondly, based on research of 50,000 car sales and interviews with 23 key stakeholders in the country, we present analysis of the existing Kyrgyz vehicle fleet. Thirdly, using information ascertained from the interviews and desk-based research, we conduct a Total Cost of Ownership assessment of EVs versus internal combustion engine vehicles (ICEVs) in the Kyrgyz context. We find that under the current conditions, EVs can have a lower total cost of ownership against similar ICEVs in Kyrgyzstan. However, it is evident that this is not typically the case: the analysis in this paper suggests that cost-competitiveness between EVs and ICEVs is more likely for larger and higher-powered vehicles, for which the ICEV version has a higher retail price. Finally, combining analysis of these results and analysis of the current conditions in Kyrgyzstan, trajectories for EV adoption in geographically and economically similar nations, and data from expert stakeholder interviews, we propose a set of policy recommendations to accelerate EV uptake in Kyrgyzstan. • We present a study into electric vehicle (EV) adoption in Kyrgyzstan. • Interviews with 23 expert stakeholders and over 50,000 car sales are analysed. • A total cost of ownership (TCO) model is presented for the Kyrgyz case. • Policy recommendations are made on the basis of this study. • The recommendations presented are applicable to the Central Asian region. [ABSTRACT FROM AUTHOR]

This paper examines the risk and returns of classic car price indices over the 1994–2021 period. We calculate the central tendency, dispersion, shape of risk and returns, the unit root tests, and correlations. The results indicate a moderated volatility, a low range of returns, and a weak expectation of financial gain given the ancillary costs related to the auction, transport, insurance, guarding, maintenance, and restoration. There are low correlations among the classic car markets. These results provide a better understanding of the risk and returns of the classic car market for many actors such as individual and professional investors, collectors, and wealth managers. Investing in a classic car is more of a passion and emotional investment than a simple desire for financial gain. [ABSTRACT FROM AUTHOR]

The automobile sector is playing a pivotal role in providing impetus to the manufacturing activities in India. India occupies a prominent position in the global automobile market. Its passenger car market was valued at $32.70 bn in 2021, and is estimated to grow to $54.84 bn by 2027, while registering a CAGR of more than 9%. The electric vehicle market is expected to reach 50,000 cr ($7.09 bn) by 2025; while total passenger vehicles sales touched 3.07 million in FY2022. The buoyancy in the automobile sector has led to huge academic interest to reconnoitre various crucial aspects like government initiatives to foster the Indian automobile sector. This paper analyzes the overall performance of this sector in terms of production, domestic sales and exports, and performance of automobile stocks. [ABSTRACT FROM AUTHOR]

With the popularization of driverless technology, more and more vehicles have begun to apply this technology. Under these circumstances, user satisfaction with driverless function begins to have an increasing impact on car sales. This paper adopts the KANO model, a two-dimensional model, to analyze users' attitudes toward driverless functions. It is found that users have a high dependence on functions such as forward collision warning, autonomous emergency braking, and stated-speed sign recognition. Therefore, relevant enterprises should continue to develop these functions. Besides, users have high expectations for functions such as blind spot detection systems and rear cross-traffic alerts. Enterprises can achieve more support from users by optimizing these functions. Functions like lane-keeping assist, lane departure warning, parking distance control, and door open warning systems belong to indifferent attributes of driverless function, which are not cared about by users. There is no need for enterprises to optimize these functions. However, the lane change assist system has been criticized by users and should be improved by corresponding manufacturers. [ABSTRACT FROM AUTHOR]

The transport sector is the largest contributor to greenhouse gas emissions in the EU. Reducing emissions from transport is therefore essential to meet the EU's climate neutrality targets. Sales of electric vehicles have increased significantly since 2020, and a continued increase in sales of electric vehicles in Europe could help the EU reach its CO2 emission reduction targets. Our paper presents an analysis of electric car sales in Romania, together with a forecast for the coming years. For this study, data from Eurostat was collected and used for analysis to present the results. To obtain the desired results, the collected data were analysed using forecasting methods that can help to resolve uncertainties about the future. Sales of electric cars in Romania were analysed using both linear and exponential function methods. The results showed that the exponential function analysis of total sales of electric cars in Romania had a higher forecast accuracy than the linear function method. From the analytical methods used we can conclude that the sales of electric cars are on an increasing scale and the choice of an appropriate method is important in data analysis. [ABSTRACT FROM AUTHOR]

Social media has become an integral part of people's lives, resulting in a constant flow of information. However, a concerning trend has emerged with the rapid spread of fake news, attributed to the lack of verification mechanisms. Fake news has far-reaching consequences, influencing public opinion, disrupting democracy, fueling social tensions, and impacting various domains such as health, environment, and the economy. In order to identify fake news with data sparsity especially with low resources languages such as Arabic and its dialects, we propose a few-shot learning fake news detection model based on sentence transformer fine tuning, utilizing no crafted prompts and language model with few parameters. The experimental results prove that the proposed method can achieve higher performances with fewer news samples. This approach provided 71% F1 score on the Algerian dialect fake news dataset and 70% F1 score on the Modern Standard Arabic (MSA) version of the same dataset, which prove that the approach can work on the standard Arabic and its dialects. Therefore, the proposed model can identify fake news in several domains concerning the Algerian community such as politics, COVID-19, tourism, ecommerce, sport, accidents and cars prices. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the automobile sales management becomes more and more complicated, this paper proposes a sales system software platform based on a computer network. The automobile sales management system based on the MVVM framework, with Java as the development language and MySQL as the database, is implemented by the progressive framework Vue in the front end and developed by the Spring Boot framework in the back end. Starting with the design and implementation of the system, the technical framework, functional modules, and implementation processes used to develop the system are studied. The experimental results show that the function tests are normal, the response time of the system client is generally 1-2 seconds, the processing speed is fast, and the influence ability is good. The system improves the comprehensive management ability and promotes the rapid development of automobile industry. [ABSTRACT FROM AUTHOR]

In this paper we investigate differences between groups of Norwegian electric vehicle owners, sorted by their adoption level. The grouping is based on adoption theory and the share of battery electric vehicles in new car sales numbers. We investigate Norwegian adopters' preferences, values, and motivations for choosing a battery electric vehicle. The main data source is a yearly survey between 2015 and 2020 amongst Norwegian electric vehicle drivers. The motivation of the study is to reveal different choices by the adopter groups, contributing to policy recommendations and incentives for other countries. However, the Norwegian case might be a special one, having economic advantages which many other countries do not have access to. We assess the validity of the results and policy recommendations by analysing the results of a survey amongst the Nordic countries on investment choices concerning battery electric vehicles. [ABSTRACT FROM AUTHOR]

Private cars are the most active and important incremental factor in the electric vehicle market and are expected to account for 80% of the new energy vehicle sales market by 2030. As the most common charging scenario for private cars, orderly charging in the community can optimize the distribution load curve by dynamically adjusting charging time and power of electric vehicles, so as to achieve peak-load shaving and turn electric vehicles into a friendly load to the distribution grid. However, after the traditional orderly charging strategy was released, the complexity of the operation on the user's side was a heavy strike to the user's willingness to participate in orderly charging, resulting in the quite low participation rate and insufficient demonstration of the characteristics of EV's elastic power demand. To solve this issue, the paper proposes a senseless orderly charging strategy with user charging demand prediction and substation capacity constraint considered to minimize user charging fee or maximize service provider revenue. After a five-month practical application, the proposed strategy was found to effectively improve the user participation rate in orderly charging and to regulate electric vehicles as an elastic load to meet the grid demand. [ABSTRACT FROM AUTHOR]

The number of displays inside an automotive cockpit is increasing, and so is the possibility to tap into new technologies. CuJTently, LCD displays are used in most of the mid to high price range cars, but OLED displays have also started making their appearance in cars like Audi E‐Tron, Cadillac Escalate, Mercedes EQS etc. There can be scenarios where the cockpit might have displays with mixed technologies, for example, one display can be an LCD and the other can be an OLED. In this paper we have evaluated the color difference acceptability threshold for such a mixed technology scenario (an LCD and an OLED). A detailed workflow to study the acceptability of users from different age groups through a psychophysical experiment is designed and documented. The acceptability threshold is published using the major color difference formulae available in literature, i.e., CIEDE2000, CIEDE76, CIECAM02‐UCS, DI99d, OSA‐GP and CAMI6‐UCS. This study acts as the first pilot study in the series of studies where several other factors affecting this acceptability threshold will be studied. [ABSTRACT FROM AUTHOR]

This paper develops linear and nonlinear forecasting models to propose a sophisticated and accurate forecasting method in a fiercely competitive environment, such as the U.S. auto industry. Our results indicate that companies could operate successfully in a highly competitive market by using the competitors' sales to accurately predict their sales and plan for raw material, production, and finished goods inventories. Our suggested methodology is beneficial when the competitors are within similar strategic groups. The data for this study are obtained from the "U.S. Automotive News" data services, which contain time series records for inventory and sales for multiple automakers. To keep the analysis straightforward, we have chosen data for four major automotive companies known for their high‐level competition: the General Motors Company, the Ford Company, the Toyota Corporation, and the Honda Company because of intense rivalry due to competing within the same strategic business units. The results show a benefit is achieved by including the total sales for at least one competitor in the linear or the nonlinear forecasting models to predict domestic sales for the desired company. [ABSTRACT FROM AUTHOR]

Automotive aftermarket industry is possessed of a wide product portfolio range which is in the 4th rank by its worldwide trade volume. The demand characteristic of automotive aftermarket parts is volatile and uncertain. Nevertheless, the cause-and-effect relationship of automotive aftermarket industry has not been defined obviously heretofore. These conditions bring automotive aftermarket sales forecasting into a challenging process. This paper is composed to determine the relevant external factors for automotive aftermarket sales based on expert reviews and to propose a sales forecasting model for automotive aftermarket industry. Since computational intelligence techniques yield a framework to focus on predictive analytics and prescriptive analytics, an artificial neural network model constructed for Turkey automotive aftermarket industry. Artificial intelligence is a subset of computational intelligence that focused on problems which have complex and nonlinear relationships. The data which have complex and nonlinear relationships could be modelled successfully even though incomplete data in case of implementation of appropriate model. The proposed ANN model for sales forecast is compared with multiple linear regression and revealed a higher prediction performance. [ABSTRACT FROM AUTHOR]

The evolution of electric mobility is a promising strategy for decarbonizing the automotive sector. India is one of the countries that support having at least 30% of new vehicle sales be electric by 2030. To achieve the EV ecosystem to a considerable level, many efforts are required in EV charging infrastructure. Battery swapping is treated as a promising solution to accelerate the mass adoption of EVs in India. It is swapping model as an alternative to a lack of charging stations and to reduce range anxiety. This review paper discuss the role of batter swapping, its importance, pros & cons in 4-wheeler passenger car segments. It highlights the policy guidelines in India related to battery swapping, comparison of swapping vs. charging & ideal places for battery swapping station and role of EV battery pack. The review summary recommends the need to standardize the battery pack to enhance swapping among a single OEM across various name plates and among all OEMs with a collaborative effort. [ABSTRACT FROM AUTHOR]

As a crucial emerging policy for the Chinese automotive industry to achieve energy saving and emission reduction as well as to promote the development of new energy vehicles (NEVs), the dual-credit policy has attracted considerable attention from scholars since its implementation. Nonetheless, the specific implementation outcomes have been less than satisfactory, necessitating further improvements. This paper establishes a prediction model based on the principles of the dual-credit policy and the core parameter of automobile sales using the Bass model as the system, thereby constructing a complex dynamic system composed of three subsystems to explore the effects of adjusting specific credit standards. The research findings are as follows: (1) In the initial years of policy implementation, the selection of different credit standards has little impact on the credit market. Only after more than 5 years, the differences become increasingly apparent. It is recommended that the government should persist in dynamically adjusting the credit policy. (2) Strict credit policies have a certain promoting effect on the proportion of new energy vehicles and their driving range capabilities. (3) This policy has limitations on the energy-saving and emission-reduction effects of traditional fuel vehicle enterprises. [ABSTRACT FROM AUTHOR]

The adoption of e-mobility in the countries of the European Union shows significant differences in terms of dynamics of electric car sales and in terms of differences in the share of electric cars in the total fleet of passenger cars in individual Member states. Despite the common European policy of transition to less emission-intensive car drives, the economic performance of countries, as well as other determinants, turn out to be the key drivers of differences in the sales and shares of electric cars on the car fleet within individual countries. In light of these findings, the paper contributes to the existing discussion and knowledge with additional recent findings and how intensive main consensual factors cause differences in the recent electric cars market shares in the EU countries. [ABSTRACT FROM AUTHOR]

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The production of cars has been decreasing in most countries since the COVID-19 pandemic from 2020 to 2021. Due to this, the used car market has grown to be a booming industry by itself. Recent advances in online portals and platforms have made it possible to get more information about the factors that determine used car values. Hence, car price prediction has become a high-interest field of research. This paper aims to investigate the power of machine learning to build a model that will be able to predict the approximate price of a used car by utilizing the “Saudi Arabia Used Cars” Dataset which is collected from the Syarah platform and available on the Kaggle platform. The model assists both customer and seller to estimate the approximate price of a used car in the market. Three different Machine learning techniques were utilized which are Linear Regression, Random Forest, and XGBoost which score an MSE of 0.15, 0.10, and 0.19 respectively. The Random Forest Regressor algorithm outperformed other algorithms where it achieves the best result on the three evaluated metrics RMSE, MSE, and R-squared. [ABSTRACT FROM AUTHOR]

China's industry is facing a carbon lock-in (CLI) dilemma. Based on the complexity and dynamics of CLI, exploring diversified unlocking paths is an important way to achieve emission reduction goals. This paper innovatively applies system dynamics (SD) to the study of CLI prediction and unlocking path, and systematically constructs an industrial CLI SD model, trying to break the positive feedback mechanism of CLI through multiple causality and feedback chains. This paper simulates and predicts the changing trend of the CLI level of China's industry and 8 major subsectors from 2020 to 2050. At the same time, the random forest algorithm is used to select the regulatory variables of policy analysis. Compared with the previous research methods, the accuracy and objectivity of the selection of regulatory variables are improved, so as to test the effectiveness of the unlocking path under each scenario. The results show that (1) the whole industry faces a serious CLI problem and is still in a deep lock-in state. From the perspective of subsectors, the light, electromechanical, and textile industries have achieved unlocking; the oil and extractive industries are in a moderately lock-in state; the chemical, steel, and power industries are in a deep lock-in state; and the CLI situation is serious. (2) The results of the random forest algorithm show that R&D investment, the proportion of fixed asset investments in the energy industry, car sales, energy structure, and carbon emission industry structure are the most significant factors affecting CLI in each subsystem. (3) Compared with the existing economic development model, the technological, institutional, social, and comprehensive unlocking scenarios can all enable industry to achieve carbon peak and carbon unlocking in advance. However, the effect of each scenario is as follows: comprehensive unlocking scenario > technological unlocking scenario > institutional unlocking scenario > social unlocking scenario. (4) The unlocking times of the oil, chemical, steel, and power industries are relatively late in each scenario. To realize carbon unlocking in industry, these four areas should be focused on in the future. This study helps policymakers formulate reasonable policies to accelerate industrial carbon unlocking and promote the implementation of the "dual carbon" strategy. • The whole industry faces a serious carbon lock-in problem. • The technological, institutional, social and comprehensive unlocking scenarios can all achieve carbon unlocking in advance. • The comprehensive unlocking scenario has the best effect, and the social unlocking scenario has the worst effect. • The oil, chemical, steel and power industries should be focused on in the future. [ABSTRACT FROM AUTHOR]

Aiming at the problem of "dynamic" accurate determination of rates in UBI auto insurance pricing, this paper proposes a UBI auto insurance pricing model based on fuzzy controller and optimizes it with a parameter adaptive SASPO. On the basis of the SASPO algorithm, the movement direction of the particles can be mutated and the direction can be dynamically controlled, the inertia weight value is given by the distance between the particle and the global optimal particle, and the learning factor is calculated according to the change of the fitness value, which realizes the parameter in the running process. Effective self-adjustment. A fivedimensional fuzzy controller is constructed by selecting the monthly driving mileage, the number of violations, and the driving time at night in the UBI auto insurance data. The weights are used to form fuzzy rules, and a variety of algorithms are used to optimize the membership function and fuzzy rules and compare them. The research results show that, compared with other algorithms, the parameter adaptive SAPAO algorithm can calculate more reasonable, accurate and high-quality fuzzy rules and membership functions when processing UBI auto insurance data. The accuracy and robustness of UBI auto insurance rate determination can realize dynamic and accurate determination of UBI auto insurance rates. [ABSTRACT FROM AUTHOR]

The automotive industry is one of the manufacturing sub-sectors that play a vital role in countries' economies. However, this industry is not exempt from the effect of fuel price and financial risk components. This study investigates the effect of fuel price and financial risk components on the South African motor trade sales for the period 2008-2021. The research is built on previous work and research on the linkage between fuel price and country risks. Auto-regression Distributed Lag (ARDL) model was applied to monthly financial data (exchange rate, interest rate), fuel price and motor trade sales. Findings suggested the presence of a long-run relationship among these variables. Findings suggested the presence of a long-run relationship among these variables. It was found that both interest rate and exchange rate have negative impacts on long-run motor trade sales. Nonetheless, the fuel price was found to be statistically insignificant to influence motor-trade sales in the long run. The results also indicated that both interest rate and exchange rate cannot impact motor trade sales in the short run. Based on these results, the study stressed that policies that strengthen and stabilise the South African currency (exchange rate) are imperative to increase sales in the automotive industry and economy in general. Easing and lowering interest rates can also assist in boosting motor trade sales irrespective of the fuel price. The paper uniquely provides the interactions between fuel price, financial risk and sales within the South African automotive industry. It also suggests strategies that can be implemented to improve sales within the aforementioned industry. [ABSTRACT FROM AUTHOR]

The development prospect and sustainability of new energy vehicles (NEVs) are facing numerous challenges under the coupling influence of various factors, which has become a major strategic issue in the automotive industry research within China. Establishing a prediction method to forecast the development trends of NEVs under the complex conditions is of great significance. This paper comprehensively addresses the relevant factors influencing the sales of NEVs over temporal depth and spatial breadth, including random white noise and lag effect resulting from lag operators of influencing factors. A multivariate forecasting approach utilizing the ARIMA model and its optimized model, alongside the GBDT model were adopted to forecast NEV sales in China. The findings demonstrate that: 1) NEVs have exhibited a trend of rapid growth, but are still susceptible to random interference and lag effects caused by influencing factors. Key technology breakthroughs, accessibility of charging infrastructure, and national policy support remain as primary drivers of NEVs development; 2) The ARIM–IO– LSTM model achieved high prediction accuracy with a goodness-of-fit R 2 exceeding 90%, outperforming the ARIMA and ARIMA-IO models by 23.586% and 20.790%, respectively, while the ARIMA-IO model improved accuracy by 5.126% compared to ARIMA. The method of model combination and outlier incorporation have proved to be practical solutions for strengthening model accuracy. 3) In the next 3–5 years, the NEV industry will enter a stage characterized by steady progress with an upward trend. Adapting the intensity, scope, and direction of policy support, coupled with achieving technological breakthroughs and enhancing infrastructure construction, are essential for sustaining industrial development and attaining desired objectives. [ABSTRACT FROM AUTHOR]

Accurate automobile-demand forecasting can provide effective guidance for automobile-manufacturing enterprises in terms of production planning and supply planning. However, automobile sales volume is affected by historical sales volume and other external factors, and it shows strong non-stationarity, nonlinearity, autocorrelation and other complex characteristics. It is difficult to accurately forecast sales volume using traditional models. To solve this problem, a forecasting model combining trend extrapolation and causality analysis is proposed and derived from the historical predictors of sales volume and the influence of external factors. In the trend-extrapolation model, the historical predictors of sales series was captured based on the Seasonal Autoregressive Integrated Moving Average (SARIMA) and Polynomial Regression (PR); then, Empirical Mode Decomposition (EMD), a stationarity-test algorithm, and an autocorrelation-test algorithm were introduced to reconstruct the sales sequence into stationary components with strong seasonality and trend components, which reduced the influences of non-stationarity and nonlinearity on the modeling. In the causality-analysis submodel, 31-dimensional feature data were extracted from influencing factors, such as date, macroeconomy, and promotion activities, and a Gradient-Boosting Decision Tree (GBDT) was used to establish the mapping between influencing factors and future sales because of its excellent ability to fit nonlinear relationships. Finally, the forecasting performance of three combination strategies, namely the boosting series, stacking parallel and weighted-average parallel strategies, were tested. Comparative experiments on three groups of sales data showed that the weighted-average parallel combination strategy had the best performance, with loss reductions of 16.81% and 4.68% for data from the number-one brand, 25.60% and 2.79% for data from the number-two brand, and 46.26% and 14.37% for data from the number-three brand compared with the other combination strategies. Other ablation studies and comparative experiments with six basic models proved the effectiveness and superiority of the proposed model. [ABSTRACT FROM AUTHOR]

Governments around the world promote the purchase of electric vehicles (EVs) as part of their climate change strategy, with many committing to EV-only sales of new passenger vehicles by 2035 and complete use of EVs by 2045 (California) or 2050 (e.g., Canada, EU). Rebates (purchase subsidies) are offered to consumers to promote uptake of EVs, and growth in their uptake has been quite strong, although EVs remain a small proportion of registered vehicles. In this study, we first analyze the economics of EV subsidies and then use Canadian electrical generation capacity, EV efficiency data, and distances driven, along with Monte Carlo simulation, to project the increased demands that greater numbers of EVs will place on an electrical grid. We find that the current grid's capacity will not be adequate to power the anticipated growth in EVs, and major new power plants or hydroelectric dams will need to be constructed. The analysis suggests that Canada might need to build 17 new hydroelectric facilities or 14 additional gas plants, as there is likely to be much resistance to new hydroelectric projects. [ABSTRACT FROM AUTHOR]

The article examines consumers' perceptions about electric vehicle (EV) adoption and user satisfaction in Southern Kerala, India. The study evaluates the vehicle preferences of youthful, educated men and factors that influence buying decisions including demographic characteristics, economic issues, environmental concerns, financial gains and operational efficiency. It summarizes EV sales trends and cost-benefit of EVs and outlines opportunities and challenges in EV adoption.

Objective: To investigate the current situation of child safety seat use and children's cognition of safe riding in Leshan City, and to provide a basis for the promotion and application of child safety seats in Leshan City. Methods: From November 2021 to February 2022, a total of 500 car-owning families who visited or were hospitalized in the department of pediatrics of our hospital were surveyed by self-filling questionnaire method. Through the investigation of personal situation, family situation, travel habits, use of child safety seat, cognition of safe riding, etc., the influencing factors of possession and use of child safety seat and cognition were analyzed. Results: The ownership rate of car seats was 57.8%, the use rate was 47.6%, and the always use rate was 18.8% among all families with children surveyed. There were no statistically significant differences in the use rate and always use rate of child safety seats among different genders of children, parents' education background, and car ownership price. The use rate and constant use rate of child safety seats in urban households were significantly higher than those in rural areas. When the main driver was the mother, the use rate of child safety seats was higher, and the difference was statistically significant. From the perspective of cognition, the use rate and constant use rate of the child safety seat of parents with correct cognition were 64.5% and 25.7% respectively, while the use rate and constant use rate of the child safety seat of parents with incorrect cognition were only 11.1% and 4.3%, which were significantly lower than those with correct cognition, and the difference was statistically significant. In terms of cognition, 10.4% of parents think it is safe for children to sit in the back seat with adults in their arms, and 9.0% of family members think it is safe for children to sit in the back seat with seat belts alone. Conclusions: At present, the ownership rate, utilization rate, and parents' correct cognition of child safety seats in Leshan City are all at a low level, and there is a lack of regional legal guidance, so the safety situation of children in cars is worrying. To effectively improve the use of child safety seats, the promotion of child safety seats should be promoted from three aspects: improving parents' cognition, forming good traffic habits, and legal guidance. [ABSTRACT FROM AUTHOR]

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. [ABSTRACT FROM AUTHOR]