Car Market Research Articles

Logistics challenges in used car maritime traffic from Europe to west Africa

This paper addresses the main logistics challenges in used car maritime traffic from Europe to West Africa. Thus, the methodology (quantitative and qualitative) analyses data from the International Organization of Motor Vehicle Manufacturers (OICA), from 2015 to 2023 of government and port authorities to show the importance of used car market for mobility and socioeconomic activities. This is supplemented by surveys based on direct observation in the field, questionnaires and interviews involving in Europe 55 stakeholders and 127 in Africa. The results demonstrate that cars used and their parts, but not wrecks, are essential for motorization in West Africa. A pre-export process needs to be set up to ensure that exported vehicles are parked in better condition to meet the required common environmental standards for sustainable mobility.

Разработка предметных графов знаний на основе семантического аннотирования табличных данных

The article outlines an approach and software tool for the automated enrichment of domain-oriented knowledge graphs with new facts derived from semantically annotated tabular data. For semantic annotation of table columns, a combination of three heuristic methods is proposed, leveraging named entity recognition in cells, lexical matching, and feature grouping. This approach is implemented as a dedicated handler within the Talisman software platform. An example and experimental evaluation of the approach during the semantic annotation of columns are provided using a test set of tabular data across six thematic categories: "organization employees," "open vacancies," "car model market," "famous scientists," "book sales," and "tennis player rankings." Evaluation metrics included precision, recall, and F-measure, with final results across all six categories as follows: precision - 79%, recall- 63%, F-measure - 70%. These results highlight the potential of the developed approach for enriching domain-oriented knowledge graphs with new facts from semantically annotated tabular data. The limitations of the proposed approach are also discussedfrom semantically annotated tabular data. The paper also provides a number of limitations of the proposed approach.

Research on the Development Trend of New Energy Electric Vehicles in China based on Mathematical Model

As the global economic development moves closer to the direction of green sustainability, the international automobile market pays more and more attention to the development of new energy vehicles. New energy electric vehicle (NEV), as a kind of new energy vehicles, has developed rapidly in recent years due to its characteristics of low pollution, low energy consumption, peak power consumption and adjustment capacity, and has been welcomed by global consumers and governments. The Chinese government conforms to the development of The Times and actively promotes the development of its industry. This paper aims to study the development trend of China's new energy electric vehicles in China, through the establishment of multiple linear regression equation model analysis of different factors of new energy electric vehicles, and using the stochastic comparison model compared the influence of new energy electric vehicles on the traditional energy vehicle industry, and establish the influence factor model, analyze the international policy influence on the development of new energy electric vehicles, in the whole research process to test and the advantages and disadvantages analysis.

An Analysis of Tesla's Disruptive Innovation Strategy

With the rapid development of new energy vehicles, governments and automobile manufacturers are generally aware that environmental protection is an important direction for the development of the future automotive industry, and Tesla, as a representative of the global new energy electric vehicles, has great research significance. In the fierce environment of the automobile market, Tesla has won the leadership position with many innovations. Therefore, the company is considered a disruptive innovation by the public. This paper focuses on whether Teslas innovation is disruptive innovation, and studies Teslas disruptive innovation strategy through literature reading and qualitative analysis. This paper discusses the definition of disruptive innovation, Teslas current innovation status and explores the difference between it and incremental innovation. This paper draws the conclusion that Tesla is a disruptive innovation by considering whether it creates a value network as a breakthrough point. Finally, the paper puts forward some suggestions, including extending the product line, adjusting its price to maintain its competitive advantage and innovation, adjusting the channel level based on local markets, provide more extreme and detailed experiential marketing and after-sales service, so as to promote Tesla's sustainable innovation.

Scenario-Based Analysis of Electrification Effects on Value Creation and Employment Structures for the Automotive Industry in the Federal State of Baden-Wuerttemberg, Germany

The transformation path to electric mobility will have fundamental impacts on the existing value chain and employment structures in the automotive industry. The purpose of this paper is to derive and examine these effects based on two different electric mobility market scenarios for the European (EU27) passenger car as well as truck market 2040 with special focus on the highly export-oriented industrial automotive cluster in Baden-Wuerttemberg. To achieve this, both a moderate and a progressive market scenario were simulated using the scientifically validated DLR VECTOR21 vehicle technology scenario model, based on two different parameter sets derived from actual and possible framework conditions on the European car and truck markets. Based on a detailed analysis of the industrial branch, value creation, and employee structure in Baden-Wuerttemberg and its automotive cluster, the effects resulting from the transformation to electric mobility will be displayed. With detailed Fade-In and Fade-Out analysis, the shifts from internal combustion engine components to electrified components will be derived and illustrated for each employee segment in the automotive cluster in Baden-Wuerttemberg, which leads to completely new and original results at this level of detail. Furthermore, the detailed display of the automotive cluster in this study allows for regionalized statements on employment effects, considering, for the first time, not only the car but also the truck segment. The analysis shows that battery electric vehicles will achieve a share of 34% or 57% for new registrations on the German car market in 2030, depending on the scenario framework conditions. The resulting employment effects for the entire automotive cluster in Baden-Wuerttemberg reach −37,000 (−8%) or −66,000 (−14%) by 2030 with further negative development until 2040 (−155,000, −30%) for the respective scenarios. Employment segments, in particular powertrain-dependent production employees, are at risk, with a potential decline of up to −60%. R&D employees could be also be significantly, affected with a reduction in workforce of about −50%.

Фронтиры развития автопроизводства в РФ в условиях адаптации к новым вызовам

The automotive industry is one of the main industries in mechanical engineering. It accounts for a large share of capital investments. The conveyor assembly system, which was widely used in this industry at the time, radically changed the entire industry of the XX century. The multiplicative effect of car production makes the industry the "locomotive" of the economy. The automotive industry is today one of the frontiers in the development of our country's economy. The purpose of the study is to analyze the situation in the Russian car market and assess the prospects for the development of the Russian automotive industry in the context of the consequences of the COVID-19 pandemic and the strengthening of sanctions restrictions by unfriendly countries. In the process of working on the study, methods of analysis, synthesis, description, comparative, problematic, logistic and dynamic methods were used. According to the results of the study, the following conclusions were drawn: the automotive industry plays an essential role in the processes of globalization of the modern economy. However, the disruption of logistics chains had a negative impact on the situation in the domestic automotive industry and the consumer market, led to a reduction in production and an increase in unemployment in this industry. The way out of this situation is to reorient domestic car production to cooperate with companies from friendly countries, such as China, and establish logistics chains through intermediary countries, such as Kazakhstan, but adjusted for minimizing costs. An important role in solving this problem can be played by the development and implementation of an import substitution mechanism in the production of domestic cars, including electric cars. The novelty of the study lies in the fact that the proposed frontiers in the stabilization of Russian automotive production have been identified taking into account the impact of both external and internal factors in the current volatile economic situation caused by the reaction of unfriendly countries towards Russia.

Car Sales, Fuel Economy and Decarbonization in Mexico

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.

Exploring vehicle preference dynamics: Analyzing the influence of oil price volatility on consumer car purchase decisions - A comparative survey experiment

China's automobile market is continuously expanding, leading to ongoing growth in oil demand. Fluctuations in oil prices will inevitably impact the travel patterns and car purchasing behavior of automotive consumers. This paper analyzes the influence of gasoline price fluctuations on the travel patterns and car purchasing behaviors of auto consumers with varying incomes, employing a dynamic perspective. We conducted a questionnaire survey of auto consumers in four first-tier cities in China and performed a comparative analysis of questionnaire data from 2015 to 2022. The empirical findings are as follows: (1) Under conditions of elevated oil prices, fuel car consumers exhibit an increased susceptibility to oil price fluctuations, which may cause fuel car consumers to curtail their travel activities. (2) The 10 yuan/liter is the threshold for middle - and upper-income consumers to change their car buying or travel habits, which has not been affected by oil price fluctuations. (3) For the cohort considering electric vehicles, gasoline prices stand as the primary determinant influencing their car buying choices. The primary conclusions serve as significant references for the government to craft corresponding carbon emission reduction policies amidst fluctuating oil prices.

Aspects of Spodumene Lithium Extraction Techniques

Lithium (Li), a leading cathode material in rechargeable Li-ion batteries, is vital to modern energy storage technology, establishing it as one of the most impactful and strategical elements. Given the surge in the electric car market, it is crucial to improve lithium recovery from its rich mineral deposits using the most effective extraction technique. In recent years, both industry and academia have shown significant interest in Li recovery from various Li-bearing minerals. Of these, only extraction from spodumene has established a reliable industrial production of Li salts. The current approaches for cracking of the naturally occurring, stable α-spodumene structure into a more open structure—β-spodumene—involve the so-called decrepitation process that takes place at extreme temperatures of ~1100 °C. This conversion is necessary, as β-spodumene is more susceptible to chemical attacks facilitating Li extraction. In the last several decades, many techniques have been demonstrated and patented to process hard-rock mineral spodumene. The objective of this review is to present a thorough analysis of significant findings and the enhancement of process flowsheets over time that can be useful for both research endeavors and industrial process improvements. The review focuses on the following techniques: acid methods, alkali methods, carbonate roasting/autoclaving methods, sulfuric acid roasting/autoclaving methods, chlorinating methods, and mechanochemical activation. Recently, microwaves (MWs), as an energy source, have been employed to transform α-spodumene into β-spodumene. Considering its energy-efficient and short-duration aspects, the review discusses the interaction mechanism of MWs with solids, MW-assisted decrepitation, and Li extraction efficiencies. Finally, the merits and/or disadvantages, challenges, and prospects of the processes are summarized.

경차 브랜드의 헤드라이트에 적용된 디자인 조형성이 소비자 만족도 및 구전의도에 미치는 영향

Design is an important factor in the compact car market beyond economic feasibility and practicality, especially headlights that determine the first impression of the vehicle and greatly influence the brand image. The purpose of this study is to analyze the effect of design formativeness applied to the headlights of compact car brands on consumer satisfaction and word-of-mouth intention. To this end, case studies and empirical studies were conducted on major domestic compact car models, and structural equation model were used for the data collected through the survey. As a result of the study, it was found that formative factors such as form, size, and space of compact car headlights had a positive effect on consumer satisfaction, and this satisfaction led to positive word-of-mouth intentions of consumers. On the other hand, the material factor had no significant effect. This result suggests that compact car manufacturers should emphasize visual elements and move toward improving the consumer experience when developing design strategies. In addition, it was confirmed that there is a need to strengthen strategies to promote positive word-of-mouth by efficiently allocating design resources. In conclusion, in order to secure competitiveness in the compact vehicle market, a strategic approach that considers design originality and technical efficiency at the same time is needed, and it will be important to consider customized marketing strategies that consider the characteristics and strengths of each vehicle.

Car Price Prediction Using Enhanced Technique

The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs. The price of a new car in the industry is fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But, due to the increased prices of new cars and the financial incapability of the customers to buy them, used car sales are on a global increase. Therefore, there is an urgent need for a used car price prediction system which effectively determines the worthiness of the car based on multiple aspects, including vehicle mileage, year of manufacturing, fuel consumption, transmission, road tax, fuel type, and engine size. We have developed a model which will be highly effective. This model can benefit sellers, buyers, and car manufacturers in the used cars market. Upon completion, it can output a relatively accurate price prediction based on the information that user‘s input. Various regression methods were applied in the research to achieve the highest accuracy. Because of which it will be possible to predict the actual price a car rather than the price range of a car. User Interface has also been developed which acquires input from any user and displays the Price of a car according to user‘s inputs.

Advancements and challenges of fuel cell integration in electric vehicles: A comprehensive analysis

Fuel cell technology emerges as a promising green solution, offering mitigation to global warming, air pollution, and energy crises. This eco-friendly approach is witnessing a surge in adoption within the automotive sector, with fuel cell buses, cars, scooters, forklifts, and more, becoming increasingly prevalent. The automobile industry has been rapidly advancing fuel cell technology, inching closer to the commercialization of fuel cell vehicles. As various technical hurdles are surmounted and costs are reduced, fuel cell vehicles are poised to become a competitive force in the automobile market, presenting an excellent solution for environmental sustainability and energy efficiency. This review paper delves into the fundamentals of fuel cells, their characteristics, and their applications in the automotive realm, exploring their prospects in comparison to traditional technologies. Furthermore, it sheds light on the existing research and industrial developments in hydrogen and fuel cell technologies. Additionally, a comprehensive comparison is provided between various fuel cell cars that have already been commercialized, enabling readers to understand the current market landscape. The review also analyses the advantages and challenges associated with fuel cell technology, offering insights into its future development trajectory. Through this comprehensive exploration, readers can gain a deeper understanding of fuel cell technology and its potential in revolutionizing the automotive industry.

Passenger car market and regional price discriminations for new cars: Case study and empirical analysis for Poland 2004–2021

Abstract Poland disposes on a large stock of mostly aged private passenger cars. In contrast younger cars still play minor role in individual mobility. The contribution analyzes reasons of lagging modernization by focusing on new cars prices with respect to regional price discrimination. It finds in 2004-2021 substantially increasing new cars prices and regional price discrimination between Poland and Germany. Price differentials were positive, with lower prices in Poland, and increased during time. Producers discriminated by prices increasingly but also limited arbitrage transactions by controlling excess differentials. Optimal regional price discrimination could support car markets developments in Poland by stock age compositions shifts towards younger cars. However, applied discrimination proves in observed market environments not sufficient to achieve a great car market transition in near future.

Comparative Analysis of Machine Learning Algorithms for Used Car Price Prediction

After 2021, over 90 million passenger automobiles were produced, marking a significant increase in auto production. This growth has led to a flourishing used car market, which has become a highly lucrative sector. One of the most critical and fascinating areas of research within this market is automobile price prediction. Accurate price prediction models can greatly benefit buyers, sellers, and businesses in the used car industry. This paper presents a detailed comparative analysis of two supervised machine learning models: K-Nearest Neighbour and Support Vector Machine regression techniques, to predict used car prices. We utilized a comprehensive dataset of used cars sourced from the Kaggle website for training and testing our models. The K Nearest Neighbour algorithm is known for its simplicity and effectiveness in regression tasks. On the other hand, the Support Vector Machine regression technique uses a different approach, finding the optimal hyperplane that best fits the data. Both methods have their strengths and weaknesses, which we explored in this study. Our results indicated that both KNN and SVM models performed well in predicting used car prices, but with slight variations in accuracy. Consequently, the suggested models fit as the optimum models and have an accuracy of about 83 percent for KNN and 80 percent for SVM. The results indicate that the KNN model slightly outperforms the SVM model in predicting used car prices.

Second-hand car price prediction based on multiple linear regression and random forest

The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

Certified data chats for future used car markets

Used car market platforms are interested in extending their offering from information provision to the whole customer journey. Providing certified data on the car’s state and history enables this extension by eliminating the need to physically inspect the car before buying it. Hence, communication and negotiations can move entirely to a used car platform to cover the entire value chain. How can such a market communication be designed when certified data come into play? This study designs and develops a certified data chat for the selective and controlled exchange of blockchain-based certified data in used car negotiations. An experimental market game is played with students to evaluate the usefulness of the chat. The study contributes to the augmentation of market communication with valuable and sensitive data exchange and demonstrates what a key component of a future used car market can look like. It offers three design principles and insight into why certified data chats are useful.

The Determinants of Supply Chain Performance in Manufacturing Industries: A Case Study of Proton Malaysia

This study examines the key elements that significantly impact supply chain performance in Proton Malaysia, a prominent participant in the automotive sector in Southeast Asia. The objective is to understand the impact of crucial factors on Proton's supply chain's performance, including information quality, information technology, information sharing, big data analytics capacity, supply chain integration, traceability, and agility. The study used a qualitative research methodology to examine Proton's supply chain dynamics, focussing on its strategic collaboration with Geely and the incorporation of new technology. Both primary and secondary data are utilized for analysis. The results demonstrate that Proton's focus on up-to-date information, sophisticated analysis, and robust supplier connections has greatly improved its ability to respond quickly and effectively to operational challenges and maintain its ability to recover from disruptions. Furthermore, the research emphasizes the significance of supply chain agility and integration in effectively responding to market fluctuations and reducing risks. The findings indicate that Proton must consistently engage in technology and supply chain innovation to retain its competitive advantage and successfully traverse the intricate nature of the global automobile market. These lessons apply to Proton and other manufacturing enterprises aiming to optimize their supply networks in a progressively dynamic and linked environment.

Machine Learning-Based Models for Accurate Car Prices Prediction

The used car market is becoming more and more popular in various countries, but some car trading platforms have inaccuracies in predicting prices. So, it is necessary to select machine learning models with high accuracy to predict used car prices for both buyers and sellers. This study selected three factors that have the greatest impact on used car prices, which are: car name, car use year, and car use mileage. In the selection of dataset, this study chooses more than 2000 data from four brands of BMW, Volkswagen, Acura, and Tesla which are very popular in the market to predict. In the part of comparing the accuracy of machine learning models, this paper uses three machine learning models: linear regression, decision tree regressor and random forest regressor. Based on criteria such as root-mean-square error and R-variance, with K-Fold cross validation method to compare the advantages and disadvantages of every model. Through the analysis, all the test criteria consistently show that random forest regressor has the highest performance and can achieve an R-square value of 0.8562. After getting the suitable model, accurate price prediction will be realized in more car trading platforms to help buyers and sellers to understand the real price of used cars.

When Should a Sharing Platform Adopt the Bilateral Review System?

The increasing popularity of sharing platforms is raising concerns about a lack of information on the demand side: the cost of serving specific buyers is typically unknown to sellers. To mitigate this concern, some platforms, such as Airbnb, have adopted the bilateral review system (BRS), allowing buyers and sellers to rate each other. This differs from the traditional unilateral review system (URS), which allows only buyers to rate sellers. In this study, we examine how these two review systems impact the operation of a peer-to-peer (P2P) sharing platform, where sellers with different qualities are matched with buyers with different serving costs. Our analysis reveals that under URS, even with perfect seller information, high-quality sellers can still be driven out of the market due to unknown information about the buyer and the “coproduction” nature of the serving cost. This differs from the adverse selection problem often observed in the used car market, for example, where sellers of high-quality products withdraw due to generally unknown product quality. Additionally, we highlight the critical role of the expected buyer cost: When this cost is high, BRS can benefit the platform by alleviating the adverse selection problem; however, when this cost is low, BRS becomes detrimental to the platform. Our analysis also shows that BRS helps buyers but can hurt sellers. However, BRS has the potential to generate a favorable outcome for all parties involved, resulting in a win-win-win situation benefiting the platform, buyers, and sellers. Our results not only shed light on review system design but also provide a plausible explanation for the widespread use of BRS by sharing platforms such as Airbnb, Fiverr, and Turo.

Cost-Benefit Analysis of converting vintage cars into EVs in India

Overview The following research paper attempts to analyses the viability of retrofitting old car models to electric cars in India. The increasing global problem of climate change and the developments in the area of electric cars make the concept of rebirth of old cars into EV’s a perfect marriage of youth into age. India with its tradition of having presence of vintage cars from quite some time can quite rightly wish for such a change. As it is this research aims to gather what one stands to gain both financially and non-financially by converting old model cars to EVs, PHEVs some of the questions included are; feasibility of the idea, cost, and its market. Purpose and objectives of the study The primary objective of this work is to provide the systematic analysis of the potential costs for the implementation of the change of the old generation vehicles to the electric propulsion system with regard to the initial capital outlay and recurrent costs and subsidies in India. Thus, the study seeks to also determine the prisms that underpin, the benefits in such conversion in terms of; Environmental gains for instance emission of carbon, economical gain such as cost of fuel, and the; social gain which is conservation of vintage cars. In this paper therefore, the authors endeavor to provide what they hope will be a useful analysis of the cost and issues involved in retrofitting old cars to make them electrically propelled, with a view to helping owners, businesses and governance institutions to make the right decision regarding this innovation. Its objectives are to find out if the benefits of environmental and economic nature will outweigh the costs and whether there is a market for remanufactured old model electric cars. Besides, this work will examine the factors that may be thought to slow this trend in the future such as technicalities, law demands and customers’ perceptions. This research assumes importance especially at this time in relation to India where there has been earlier association with cars of certain years of manufacture and especially the government to support its desire to champion massive usage of EV to tackle problems of pollution and use of fossil fuel. Therefore, this study will also contribute to the existing discussion on the gap of increasing the shares of sustainable transportation keeping the historic vehicles intact. The results could also include directions for the policymakers and the automobile enthusiasts who wishing to proceed with the change over from classic autos to electric automobiles as a possible and viable process.