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Every state in the country has enacted consumer-protection legislation known as a lemon laws to assist new vehicle buyers with either recurring mechanical problems (commonly referred to as \"warranty nonconformities\") in spite of attempts by manufacturers and dealers to repair them, or situations where the defects have caused the vehicle to be out of service for a long period of time.As straightforward as lemon laws appear, a surprising number of very interesting-and sometimes complex-legal issues arise, leading to a substantial amount of litigation (numerous firms around the country specialize exclusively in lemon law cases). Such thorny legal issues include:Is substantial impairment of the defective vehicle \"to the consumer\" to be judged as an objective test or a subjective one?Must the consumer be completely unaware of the nonconformity before accepting delivery of the vehicle?How specific must the consumer be in notifying the manufacturer or dealer of the consumer's selection of the remedy to either replace or buy back the defective vehicle?Must all accessories of a vehicle replaced pursuant to the lemon law also be brand new, even if not defective?Can the consumer be required to sign a general release in order to receive the remedies afforded by the lemon law?Do negotiations between the consumer and the manufacturer or dealer toll the time in which the manufacturer must replace or buy back the defective vehicle?Is the manufacturer or dealer allowed more time to replace or buy back the defective vehicle if the consumer is engaged in obstructive tactics?Where is the line between personal and business use of the vehicle which makes the vehicle ineligible for lemon law enforcement?Are leased vehicles covered under lemon laws, and if so, how is the transfer of the vehicle's title handled?Do lemon laws apply to demonstrators, motor homes, and motorcycles?Each chapter is preceded by \"practice tips,\" and the text is organized logically to follow the progression and development of litigation claims.

We analyze decisions for a car rental firm using a common pool of cars to serve both rental, and purchasing customers. The rental customers arrive successively, and rent out cars for random durations while effectuating random incremental mileages on them. This stochastic rental behavior makes the decision of when to sell a rental car quite a crucial one for the firm because it involves a certain amount of risk. On one hand, selling a car when its mileage is low proactively avoids a huge decline in the car’s residual market value (even though it could also cause the firm to forfeit income from future rental customers who intend to rent that car for long durations while driving it sparingly). On the other hand, delaying selling is equally risky because the sample of early rental customers whom that car serves may only successively keep the car for short durations while significantly adding to its mileage. Such opportunistic customers would therefore have the effect of drastically diminishing the car’s market value without providing sufficient rental income to compensate. We use optimal stopping theory to derive the firm’s optimal selling policy algorithm which unfortunately comes with a practical implementation challenge. To address this issue, we propose three heuristic selling policies, one of which is based on the restart-in formulation introduced by Katehakis and Veinott (Math Oper Res 12(2):262–268, 1987). Numerical experiments using real and artificial parameter settings (1) reveal conditions under which the proposed heuristic policies outperform the firm’s current (suboptimal) policy, and (2) demonstrate how all four suboptimal policies compare to the optimal policy.

Although rental cars experience a higher collision rate per registered vehicle compared to nonrental cars, little research has been conducted to understand the differences in the factors contributing to crashes involving rental cars and nonrental cars, especially driver-related factors. This study develops a conceptual framework to compare the driver-related factors contributing to crashes involving rental cars and nonrental cars and tests the hypotheses developed using data from South Korea and applying the binary logistics, rare event logistics, Firth logistic models, and random parameters logit models. We found a significantly higher contribution of several risky driving behaviors but no differences in roadway, vehicle, and environmental factors. We also found that rental car crashes involve more males and drivers under 25 years of age.

This study involves the car rental system of ABC Car Rental Company in Shah Alam. The car rental company faces challenges to meet customer demand. They always face shortage or too many vehicles unused on certain days on weekdays, but they are fully booked on the weekend. So, they need a guideline to manage their car rental to meet their customer demand. This company also faces a problem such as a customer returned the car late as it will affect the next customer. When this problem happens regularly, it affects the company. To find a solution to this problem, simulation model is built using Excel spreadsheet. The simulation model is ran for ten 30-day to generate the result. The result of simulation contains customer not served, total sales and average number of the unused car. From this simulation model, 20 alternatives are identified. Then scoring model will be used to find the optimal number of car rental. The findings from this study will serve as a guideline for the car rental company to manage their number of cars for their business. The car rental owner needs to optimize their car rental fleet so that they can increase their business operations performance and managed their inventory of cars prudently.

Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

Car sharing services have expanded in order to meet the new necessities of mobility worldwide in an innovative way. Before the COVID-19 pandemic, car sharing was a very popular mode of transportation among young adults in big cities. However, during this ongoing pandemic and with public transportation considered a super-spreading transmitter, the usage of car sharing is unclear. Therefore, the aim of this study, which is explorative in nature, is to investigate the usage, advantages, drivers, and barriers to car sharing during this ongoing pandemic era. To this end, 66 interviews were conducted among users of car sharing during the COVID-19 pandemic. The findings provide key information for the planning of car sharing operations and public transportation in the context of avoiding COVID-19 infection and respecting the recommendations of local governments. In addition, new emerging profiles of car sharing users in the ongoing pandemic are identified. This research provides relevant insights for both business practice and policy makers.

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

The Industry 4.0 idea influences the development of both charging stations and electromobility development, due to its emphasis on device communication, cooperation, and proximity. Therefore, in electromobility development, growing attention is paid to chargers’ infrastructure density and automotive electric vehicles’ accessibility. The main goal of this scientific paper was to present the electromobility development represented in the number of charging stations and its infrastructure development calculations. In this study, the sequence of methods was used to indicate and explore the research gap. The first was the Structured Literature Review (SLR) variation method. The second method was the classical tabular comparison of gathered results. The third research method was a cluster analysis based on secondary data with cross-country comparisons of the number of charging stations and electric cars. Therefore, this paper presents a theoretical discussion and practical business implications based on the achieved results of clusters and rankings. The main finding of this paper is that charging stations play a pivotal role in electromobility development in countries with already developed road infrastructure and maritime transportation. The charging stations can support energetic infrastructure, especially in countries with vast geographical distances. The charging stations and electric vehicles statistics presented in ratios and ranks proved similarities in the electromobility development patterns in the analyzed countries. This paper also presents the limitations of the performed study and identifies possible future research avenues.

This study examines the environmental impacts of roundtrip car sharing services by investigating transportation behavior. Car sharing should contribute to reduced greenhouse gas GHG emissions; however, such schemes include both positive and negative environmental effects, including: (1) reduced CO2e (carbon dioxide equivalent) from substituting private vehicle use for more fuel-efficient car sharing vehicles, (2) increased CO2e as car-less individuals switch from public transit to car sharing vehicles and (3) reduced CO2e due to fewer vehicles. This study examines the impacts of this modal shift on greenhouse gas (GHG) emissions using three types of models: a mixed logit model to analyze car sharing service preferences; a binary logit model to analyze whether individuals are willing to forgo vehicle ownership or planned purchases to use car sharing services; and a linear regression to determine how much private vehicle or public transportation use would be replaced by car sharing and the resulting effects on mobility. Total emissions from the current car sharing market equal 1,025,589.36 t CO2e/year. However, an increase in electric vehicle (EV) charging stations to 50% of the number of gasoline-fuel stations would increase the probability of electric car sharing vehicle use, thereby reducing emissions by 655,773 t CO2e. This study shows that forgoing vehicle purchases does not offset the increased GHG emissions caused by the shift from public transportation or private vehicle use to car sharing.