Car Segment Research Articles

Enhancing Car Segmentation for Thailand's Expressway Industry With an Automated Hybrid Machine Learning Framework

Car segmentation on Thailand's expressways poses challenges for traditional models due to unique characteristics, often resulting in predictive inaccuracies. Manual data analytics in this field is time-consuming and human centric. This research introduces an Automated Hybrid Machine Learning (AHML) framework leveraging advancements in AutoML, tailored for personalized customer segmentation in Thailand's expressway industry. The framework streamlines and automates the machine learning process, aiming to expedite model construction while enhancing performance. By employing clustering as an initial step followed by the Random Forest classifier as a hybrid classification approach, significant performance improvements are achieved compared to existing methods. Specifically, the model outperforms by 9.15% and 12.84% in both clusters, respectively. This research highlights the potential of the framework to address complex segmentation challenges and advance personalized customer targeting.

Multilevel Hierarchical Bayesian Modeling of Cross-National Factors in Vehicle Sales

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.

Electric vehicles as an emission mitigation option: expectations and reality

AbstractGlobally, the reliance on thermal power, constituting 67% of electricity generation, prompts questions for many countries regarding the viability of electric vehicles (EVs) for emission reduction in the transportation sector. This study comprehensively analyzes CO2 emissions in the small family car segment, considering engines powered by different energy sources. The analysis is coupled with Worldwide Harmonized Light Vehicle Test Procedure driving profiles, accounting for specific ambient temperature structures representative of densely populated areas in Türkiye. The constructed model incorporates the hourly temperature of the relevant cities and the hourly electricity production mix by considering the indirect emissions and comparing the ICEVs (Internal Combustion Engine Vehicles) and EVs in different time frames from 2015 to 2020. The resulting emissions from each vehicle type in each city and driving profile, along with the ratio of instances in which EVs exhibit higher emissions than ICEVs, are presented.The findings reveal the intricate interplay between vehicle type, climate conditions, and driving profiles. While the urban driving profile emerges as superior for EVs regarding CO2 reduction, offering a comfortable driving experience without range anxiety and mitigating local pollutants, it exhibits less significant carbon emission reductions at lower and higher speed profiles, particularly in cold climate conditions. EVs are not a silver bullet but a valuable technology for emissions reduction under specific conditions. Policymakers are urged to consider factors favoring EVs over ICEVs regarding CO2 emissions, such as driving profiles, time and climatic conditions when formulating investment policies and subsidies. Graphical abstract

Impact of Brand Image on Consumer Buying Behavior towards Luxury Automobile Segment

The luxury automobile market has witnessed significant growth in recent years, attracting global players such as Mercedes, BMW, Audi, Jaguar, Range Rover, and more. This research project delves into the intricate realm of consumer behavior within the luxury car segment, focusing on the state of India. It aims to unravel the multifaceted factors influencing consumers' decisions to invest in prestigious luxury car brands. Luxury cars are not merely vehicles; they are a fusion of opulence and prestige. This study seeks to comprehend the perceptions and motivations driving individuals to opt for luxury car brands. Wealthy consumers are drawn to these vehicles not solely for their superior technical features but also for their non-technical attributes, particularly the perceived image of the car brand. Luxury cars serve as a conspicuous display of wealth and social status.

The Effectiveness of HEVs Phase-Out by 2035 in Favor of BEVs with Respect to the Production of CO2 Emissions: The Italian Case

The EU has planned the phase-out of new vehicles based on internal combustion engines in favor of high-efficiency battery electric vehicles (BEV) by 2035 (Fit for 55 package). However, many doubts remain about the effectiveness of this choice for each country of the Union in terms of CO2 emissions reduction, as each State is characterized by a different carbon intensity related to the production of electricity needed to manufacture and recharge vehicles. This study seeks to explore the Italian case. To this aim, carbon intensities related to electricity production were calculated considering both the Italian electricity mix production in 2022 and those envisaged in 2035, considering two energy scenarios based on different introductions of renewable energy sources (RES). Afterward, the values obtained were adopted for determining the CO2 emissions related to the whole production process of battery systems in Italy (emissions from mining and refining, scrap materials, and final assembly included) by comparing some of the most up-to-date Life-Cycle Assessment (LCA) analyses related to the manufacturing cycle of the batteries. Finally, the results were adopted to calculate the starting carbon debit for A, B, C, and M car segments for Mild Hybrid, Full Hybrid, and Full Electric powertrains. At the same time, statistical road fuel/electricity consumption data were collected and overall CO2 emissions were calculated for the same vehicles adopting a dynamic approach and plotted for a defined distance, so as to determine break-even points with respect to the cumulative (i.e., from battery and road) carbon emissions. The results showed that advantages related to electric vehicles are significant only if a low carbon intensity related to electricity production is reached by means of a very high introduction of RES, thus keeping the door open for innovative hybrid powertrain technologies, if fed with low carbon fuels.

Role of car segment and fuel type in the choice of alternative fuel vehicles: A cross-nested logit model for the English market

In this article, we study the role of car segment and fuel type in the choice of alternative fuel vehicles and in the prediction of its market. For this purpose, we propose a joint choice cross-nested logit model to understand the demand for alternative fuel vehicles (AFV) and to study substitutional patterns between fuel types and vehicle segments with a full revealed preference approach, using only publicly available real data at disaggregate (household) level in England. Our results show that, as hypothesised, fuel type choice is not independent from car segment choice. The correlation patterns in the chosen specification reveal that individual car alternatives belonging to the same car segment are strongly correlated, while a weaker correlation exists between alternatives from different segments which share the same fuel type. The results suggest that creating awareness for cleaner fuel alternatives might be more effective if a targeted approach that considers these substitutional patterns is used. From a policy standpoint, while purchase prices play an important role and government policies have been concentrated in reducing the gap between ICE vehicles and AFVs in this dimension, our models stress the relevance of the operating cost variable, suggesting that its effect might also be crucial in the purchase decision.

Car choice determinants in Italy and Norway: A comparison based on revealed and stated choices

Norway is the leading country in electric car adoption in the world, while in Italy electric cars are only recently gaining acceptance. We compared car choices in the two countries highlighting commonalities and differences in the choice determinants, distinguishing between the small and the large car segment. We analyzed actual choices made in the real-world conditions and stated choices under hypothetical scenarios. The comparison between the preference structures of the two countries shows important differences when the revealed preference dataset is analyzed, while the differences are much reduced with the stated preference dataset. All in all, we feel that the two countries present only differences associated with longer car driving habits of the Norwegian drivers, the higher percentage of large cars in Norway, and the more developed public charging infrastructure. Since the supply of cars is quite similar, such a consideration leads us to believe that the huge discrepancy in electric car uptake is mainly due to the different car policies adopted in the two countries. The evolution of the policy setting and of the technology will determine whether Italy will follow the Norwegian model of gradual BEV uptake.

Carbon footprint comparison analysis of passenger car segment electric and ICE propelled vehicles in Kuwait

Considering the decrease in the fossil-fuel reserves and environmental drawbacks of internal combustion engine (ICE) propelled vehicles, the usage of alternative prime movers such as electric vehicles (EVs) has gained significant potential in the last decades. Battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs) are a few currently available examples of vehicle electrification. EVs have zero in-use emissions, however, depending on the region to be operated, well-to-wheel (WTW) emissions need to be considered for a realistic carbon footprint effect considering global warming (GW) analysis. Especially, considering the countries where the usage of renewable energy (RE) sources is extremely limited, such as Kuwait and other GCC countries, the benefit of EVs significantly decreases. Within this perspective in this study, an inclusive CO2 emissions analysis for two passenger segment vehicles with different prime movers are analyzed: ICE and electric motor (EM). Longitudinal vehicle models were developed via MATLAB/Simulink software for both prime mover configurations which are capable of simulating the fuel/energy consumption using Toyota Camry and Lexus UX 300e vehicles. The generated CO2 emissions by the two prime movers were compared using the previously generated Kuwait driving cycle (Kuwait DC). The results indicated that the use of BEVs generated fewer CO2 emissions nearly half the amount of what ICE propelled vehicles generated.

TATA Motors Limited: A Revolution in Electric Cars - A Case Study

Purpose: The whole world is moving toward environmentally friendly and maintainable alternatives to conservative goods. Electric vehicles are regarded as an environmentally friendly and sustainable alternative for transportation. The purpose of the study is to analyze how tata motors became a market leader in the sales and manufacturing of Electric cars. Design/Methodology/Approach: India has the largest untapped market for electric vehicles (EVs), particularly in the two-wheeler category. In recent years, there has been a significant rise in the number of EVs on the road thanks to the rapid introduction of these vehicles by a number of automakers. The most recent electric car to come out of India is the Tata Nexon EV Max, and it promises to be very affordable. Tata Motors, an Indian automaker that operates in one of the world's largest automotive markets, is compared to Tesla. A survey was used by the author to carry out this study. The effect of has been the focus of the study. Secondary data are gathered for the study from a variety of sources, including TATA motors Ltd. websites, research papers, newspaper articles, and journals. Findings/Result: This paper has studied a SWOT analysis of Tata Motors Limited. The analysis reveals that TATA Motors has a huge customer base in the Indian market.it is quite popular and about 73% of electric cars on Indian roads belong to TATA Motors Tata Motors is India's third-largest company by market share and the market leader in the EV-Electric Vehicle sector. Tata Motors has been able to steadily expand by introducing new products in the most popular SUV and electric vehicle markets. To meet all of its customers' needs and provide them with the best vehicles, the company plans to launch new products in a variety of car segments using these new products and engine transmissions. Originality/Value: This paper studies about how tata became a market leader in electric car manufacturing and overall performance in terms of carrying customer satisfaction. Paper Type: Case Study.

Well-to-wheel performance of internal combustion engine vehicles and electric vehicles – study for future Indian market

ABSTRACT Thanks to top-notch vehicle testing and internationally standardized inspections, the modes of transportation are cleaner and more efficient today. Though the development for conventional internal combustion engine vehicles (ICEVs) has almost saturated today, yet it retains the commercial/heavy-duty (trucks and buses) automobile market. Thus, it becomes of utmost importance to analyze the well-to-wheel efficiency for determining the overall energy consumption and greenhouse gas (GHG) emissions while transitioning toward sustainable transportation. Electric vehicles (EVs) do have environmental impacts that are directly related to the country’s electricity generation, as they may involve fossil burning where other alternative sources of energy are not adequately present. This paper presents the overall energy efficiencies and GHG emissions from commercial/heavy-duty ICEVs and EVs. The study also briefly compares the other vehicle segments like 2 Wheeler, 3 Wheeler, and cars. Finally, the results show that commercial/heavy-duty ICEVs are advantageous over heavy-duty EVs when the GHG emissions are considered as of today. The commercial/heavy-duty ICEVs emit over 405 g CO2e/km, and counter EVs produce 706 g CO2e/km. The GHG emissions from 2 Wheeler ICEV were 46 g CO2e/km and by the same segment, EV is 13 g CO2e/km. Moving toward the car segment, an ICEV car produces around 158 g CO2e/km and an EV of a similar model emits 67 g CO2e/km. Furthermore, the 3 Wheeler ICEV emits around 99 g CO2e/km and a 3 Wheeler EV produces lesser at 24 g CO2e/km. Moving further on well-to-wheel energy efficiency, ICEVs have a maximum overall energy efficiency of 28%. Since EVs use various sources of energy for charging the battery, the overall efficiency was found to be at least 21% and maximum up to 39%. Thus, on an average EVs presently have a well-to-wheel efficiency of 32% when electricity generation mix is used for charging.

Siting and Sizing Charging Infrastructure for Electric Vehicles With Coordinated Recharging

The popularity of electric vehicles is increasing, but the public charging infrastructure is still insufficient. To reduce extra time spent with charging on everyday trips, we introduce a new charging infrastructure siting and sizing approach. We analyze daily schedules of drivers to find suitable locations for slow and fast charging stations. In simulation, we test how many charge points to assign to each charging station. Vehicles can be charged with either en-route or destination charging using a realistic model for charging and energy consumption for five electric vehicle models of different car segments. To reduce waiting times at charging stations, we use a centralized charging station database (CSDB), that coordinates charging between vehicles. We found that a combination of a few centralized fast charging stations and many distributed slow charging stations is the best option to improve the average extra time spent with charging for all vehicle types. We also found that by using the CSDB to coordinate charging between vehicles, we were able to significantly reduce the necessary number of charge points to achieve an acceptable average extra time. In our scenario, to reach an average extra time of 15 min, we only had to add 53 charge points when using the CSDB, compared to 104 without.

Methods to Reduce Dependency on Conventional Battery in Electric Vehicles

The proposed research work is with the aim of analyzing the sources of energy for EV with the intention to reduce dependency on battery pack. EV needs no introduction for the automobile world. Almost all vehicle manufacturers in the car segment are looking for alternate ways to propel the vehicle ever since fossil fuel operated vehicles have become a cause of concern by the spiraling cost of fuel and pollutants. The paper through descriptive research methodology intends to identify opportunities to reduce the dependency on battery pack. Consideration of utilizing the kinetic energy of the car, wind energy by using vested funnels and solar energy using flexible solar array is made. These findings may serve as input in improving efficiency, ensuring clean energy for EV and further research on the subject.

Promoting electric vehicle demand in Europe: Design of innovative electricity consumption simulator and subsidy strategies based on well-to-wheel analysis

Decarbonization in the transport sector, especially in private mobility, is one of the main objectives of the European Union (EU) for next few years. Battery electric vehicles (BEVs) represent a promising solution for reducing pollution and GHG emissions; however, their purchase price contributes to curbing their diffusion. In this scenario, the aims of this study are to develop a flexible, simulation-based analysis for EU car fleets in terms of energy consumption and GHG emissions and, based on the simulation results, to propose an innovative system of financial subsidies. This can support governments in encouraging EU customers to prefer sustainable and green options for mobility. Different car segments have been considered; the electrical energy consumptions have been obtained through the development of an ad-hoc simulation model in Simulink®-MathWorks environment, while the Well-To-Wheel analysis has been performed to estimate GHG emissions. Based on these assumptions, four different subsidy strategies have been proposed and designed for countries of the EU-27. According to different logics, economic subsidies have been linked to GHG emissions avoided thanks to the use of BEVs. The results obtained show how BEVs’ consumption of electricity is low, even for larger vehicles, and this allows BEVs to be considered less impactful than internal combustion engine vehicles (ICEV) with respect to GHG emissions. Furthermore, results are highly variable, depending on the electricity mix of each considered country, and they show how, for the countries that use the most renewable sources, the proposed subsidies even can generate gains from consumers’ perspectives.

Carsharing Vehicle Fleet Selection from the Frequent User’s Point of View

Short-term car rental services, i.e., carsharing, is a solution that has been developing better and better in urban transport systems in recent years. Along with intensive expansion, service providers have to face an increasing number of challenges to compete with each other. One of them is meeting the expectations of customers about the fleet of vehicles offered in the system. While this aspect is noticed in the literature review mainly in terms of fleet optimization and management, there is a research gap regarding the appropriate selection of vehicle models. In response, the article was dedicated to identifying the vehicles that were best suited to carsharing systems from the point of view of frequent customers. The selection of appropriate vehicles was treated as a multi-criteria decision issue, therefore the study used one of the multi-criteria decision support methods—ELECTRE III. The work focuses on researching the opinions of users (experts) who often use carsharing services in Poland. The study included a list of the most popular vehicles in Europe in 2021, including classic, electric, and hybrid cars, and a list of 11 evaluation criteria. The research results indicate for frequent users the advantage of conventional drive vehicles over electric and hydrogen vehicles. Moreover, they indicate that the best vehicles are relatively large cars (European car segments C and D) with the greatest possible length, boot capacity, engine power, number of safety systems, and quality. On the other hand, the least important issues are the number of seats in the vehicle and the number of doors. Interestingly, the vehicles selected by frequent users questioned the concept of small city cars, which occupied a small public space on which carsharing was supposed to focus. The results obtained support the operators of carsharing services in making fleet decisions.

A Text Analytics Framework for Performance Assessment and Weakness Detection From Online Reviews

Present research proposes a framework that integrates aspect-level sentiment analysis with multi-criteria decision making (TOPSIS) and control charts to uncover hidden quality patterns. While sentiment analysis quantifies consumer opinions corresponding to various product features, TOPSIS uses the sentiment scores to rank manufacturers based on their relative performance. Finally, U and P control charts assist in discovering the weak aspects and corresponding attributes. To extract aspect-level sentiments from reviews, we developed the ontology of passenger cars and designed a heuristic that connects the opinion-bearing texts to the exact automobile attribute. The proposed framework was applied to a review dataset collected from a well-known car portal in India. Considering five manufacturers from the mid-size car segment, we identified the weakest and discovered the aspects and attributes responsible for its perceived weakness.

Premium Car Brands Strategies and Regulator’s Actions in Russia (2009–2021)

This article analyses the premium segment of the Russian automotive market from 2009 to 2021 based on data from the Association of European Businesses. The authors conducted a comparative analysis of trends in the Russian automotive market with trends in its premium segment and found significant differences in the market cycles. The analysis of the main periods of the studied market was based on the SV matrix (strength/variety). The article also discusses the main external and internal factors that influenced the competition level in the periods under review. As the main conclusion of the study, it was noted that while in the mass segment of cars, the strength of companies and dominant groups is highly dependent on external factors, then in the premium segment the main driving forces for changing the market situation are the actions of the companies themselves, their competitive strategy, as well as adaptation to current market trends. The article also provides comments on the impact of economic events from February 24, 2022 on the market situation.

Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

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.

Variability in total cost of vehicle ownership across vehicle and user profiles

Widespread adoption of electric vehicles (EVs) is a common and critical component of international strategies to mitigate environmental pollution, climate change and oil dependency. The ability of consumers to assess the total cost of ownership (TCO) of EVs relative to internal combustion engine vehicles (ICEVs) remains an important factor for EV uptake. The TCO of vehicles is not universal across different car segments and user profiles. We analyse and compare the TCO of ICEVs and EVs from 17 car segments across short- and long-term ownership periods, and further advance existing TCO approaches by integrating detailed activity-based driving profiles, taxation, grant structures and pricing. Results show that EV options in the most popular Irish car segments have existing battery EV options with a TCO averaging respectively 26% and 42% less than their equivalent petrol and diesel ICEV options over a 4-year ownership term when the current grant is included. This integrated method for granular TCO evaluation offers important insights for this market and affords scope to investigate how changes in travel patterns, car-segment pricing, taxation, grant policy, fuel costs, and carbon pricing and other transport policies can all affect TCO values over time across a broad range of market offerings.

Risky and anxious-aggressive drivers: A key barrier to traffic safety legislation in Germany

Traffic safety is high on the agenda of European governments. Yet, countries including Germany maintain policies that are commonly identified as increasing traffic risks, such as speed limits set too high or trends of greater car motorization and mass. To better understand some of the interrelationships of driver characteristics and car segments, this paper identifies different groups of drivers, based on a representative sample of German automobilists (n = 1211). Psychological instruments used to identify driver segments include the Driver Behavior Questionnaire (DBQ), the Multidimensional Driving Style Inventory (MDSI), and the HEXACO Personality Inventory. Based on k-means clustering, we identified four distinct groups: Risky drivers, Showy anxious-aggressive drivers, Anxious drivers and Calm drivers. Questions focused on socio-demographics, political orientation, and traffic laws are used to describe these psychographic segments. Findings suggest that Risky and Showy anxious-aggressive drivers represent a greater threat to traffic safety than other driver segments for different reasons: they drive more powerful and larger cars, deliberately violate traffic laws – creating unsafe traffic conditions for others –, and oppose legislation making traffic safer, while also voting for parties holding on to the status quo. As their political choices appear dominated by automobile interests, there are widespread implications for society.

A net‐zero Swiss energy system by 2050: Technological and policy options for the transition of the transportation sector

AbstractThe Swiss government sets out a net‐zero emission goal by 2050 while phasing out nuclear energy. This study presents insights on the transport sector's contribution to meet this goal, in terms of which type of vehicle drivetrains and fuel supply options are cost‐effective? What could be critical for the transition of the transportation sector? We use the Swiss TIMES Energy system Model and assess prospective technological scenarios for the whole energy system. The transport module has fast‐charging stations and mechanisms to control e‐mobility charging. Results indicate that extensive direct and indirect electrification of mobility increases the electricity demand up to 22 TWh. While to a certain extent electric cars are competitive even if the climate would not be the priority, carbon neutrality requires a boost for electrified mobility where battery‐ and plug‐in hybrid electric vehicles are largely evolving as a solution for small‐medium size car segments while fuel cell cars are an attractive option for big size car segments. Hybridization of buses and trucks emerges as a competitive option but they are propelled with bio‐ and synthetic fuels. The transport sector emerges as a major driver for the deployment of hydrogen in the energy sector in the long term.