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Although the impact of carbon emissions regulations is evident to upstream automakers, their influence on downstream B2C car-sharing platforms remains unclear. This article reveals the influence of carbon emission regulations on the performance of supply chain members. In particular, we focused on the decision of B2C car-sharing platforms.

We develop a three-stage dynamic game model consisting of an automaker, a B2C car-sharing platform and consumers.

The carbon emission cap has a critical threshold. Above this threshold, the regulation is ineffective for the platform’s operating model. Below it, the regulation affects the platform, moderated by customers' green awareness. The threshold initially decreases (weakly) and then increases in awareness. Effective caps reduce profits for the manufacturer, B2C car-sharing platform and supply chain, while ineffective caps see higher profits with increased awareness.

Firstly, this paper explores the impact of carbon emission caps on the operational strategies of B2C car-sharing platforms within the sharing economy, complementing existing research. Secondly, it identifies conditions where stricter caps prompt B2C car-sharing platforms to adjust their operational models and offers fresh insights for managers and departments responsible for carbon emission policy formulation. Thirdly, the study uncovers how carbon emission caps affect the performance of supply chain members, providing crucial managerial insights for sustainable operations.

This paper aims to introduce a wheeled vehicle robot for adapting to the surface terrain of the 500-m diameter reflector of the FAST radio telescope in China.

By analyzing vehicles applied for different off-road environments, a six-wheeled architecture with a passive “triple-bogie” suspension is selected. A subscale model of the vehicle robot is designed, along with statics modeling and multibody simulations of the dynamics on simulated reflector panel surfaces. The slope- and step-climbing abilities of the subscale vehicle are discussed in accordance with numerical and experimental tests. An engineering scale vehicle is subsequently manufactured and tested on surface terrains of lateral as well as vertical gaps, and is finally validated on the FAST reflector.

This model of vehicle robot exhibits strong structure stability under desired payload. It can stably cross lateral gaps for maximum surface slope 28° and can traverse vertical gap for maximum surface slope 23°. The traversing abilities satisfy the mobility requirements subjected to surface terrains of FAST reflector.

The engineering vehicle robot negotiates the lateral as well as vertical gaps between triangle panels and has been successfully applied to the FAST reflector serving for inspection and maintenance work.

The purpose of this paper is to present an integrated data-driven framework for processing and analyzing large-scale vehicle maintenance records to get more comprehensive understanding on vehicle quality.

We propose a framework for vehicle quality analysis based on maintenance record mining and Bayesian Network. It includes the development of a comprehensive dictionary for efficient classification of maintenance items, and the establishment of a Bayesian Network model for vehicle quality evaluation. The vehicle design parameters, price and performance of functional systems are modeled as node variables in the Bayesian Network. Bayesian Network reasoning is then used to analyze the influence of these nodes on vehicle quality and their respective importance.

A case study using the maintenance records of 74 sport utility vehicle (SUV) models is presented to demonstrate the validity of the proposed framework. Our results reveal that factors such as vehicle size, chassis issues and engine displacement, can affect the chance of vehicle failures and accidents. The influence of factors such as price and performance of engine and chassis show explicit regional differences.

Previous research usually focuses on limited maintenance records from a single vehicle producer, while our proposed framework enables efficient and systematic processing of larger-scale maintenance records for vehicle quality analysis, which can support auto companies, consumers and regulators to make better decisions in purchase choice-making, vehicle design and market regulation.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

There is lack of knowledge about how the existing streets need to be redesigned and the infrastructural changes that need to be made to adopt autonomous vehicles. The purpose of this study is to investigate the infrastructure requirements of autonomous vehicles in terms of (1) lane widths, (2) parking spaces, (3) drop-off zones and (4) other facilities, followed by analyzing them and suggesting changes in the existing urban design of Msheireb Downtown Doha (MDD).

Mixed method of combining both qualitative (secondary research of analyzing the existing data about the urban design guidelines for an autonomous future, observations of the existing infrastructure) and quantitative methods (on-site measurements of pedestrian walkways and road lane widths) is used.

The outcome of the research consists of a series of major infrastructural changes with regard to lane widths, parking spaces, pick-up and drop-off zones and other facilities needed for the deployment of autonomous vehicles.

The results imply that Qatar can benefit by adopting the proposed urban design suggestions for the implementation of autonomous vehicles on the streets of MDD in particular, and smart cities of Qatar and the region in general.

The proposed changes can work as a reference and serve as a possible setting for addressing Autonomous Vehicle preparations in emerging cities.

The proposed urban design changes can be adapted for an autonomous future in emerging cities.

Electric vehicle adoption (EVA) drives sustainability by significantly reducing carbon emissions and reliance on fossil fuels. Despite EVA’s notable advantages from existing literature and its evolving nature, a gap persists in evaluating EVA research. This research presents a systematic literature review, offering insights into the current state of EVA advancements.

This study amalgamates various factors influencing EVA and elucidates their associations, fostering sustainable transportation. To evaluate progress in this domain, we adopt the Theory-Context-Characteristics-Methodology (TCCM) framework, systematically assessing the theories, contextual factors, characteristics and methodologies employed in EVA research to support efficient decision-making.

The study reveals 18 theories, prominently including the theory of planned behavior, innovation diffusion theory, technology acceptance model and UTAUT. The study identifies diverse factors such as perceived risk, effort expectancy, social norms, performance expectancy, government policy, personal norms, attitude, perceived behavioral control, subjective norms, demographics and ecological knowledge as pivotal in shaping attitudes and intentions toward electric vehicle adoption. Furthermore, structured equation modeling emerges as the predominant methodology, while including alternative approaches enriches the methodological landscape, contributing to a more comprehensive understanding of the factors driving EV adoption.

The insights gained from this research can inform policymakers, manufacturers and researchers, ultimately contributing to the global transition towards more sustainable transportation solutions.

This research’s cardinal contribution lies in developing an integrated theoretical framework, a novel approach that offers a structured and holistic perspective on the multifaceted determinants of EVA. This framework not only illuminates the intricate relationships among these variables but also opens up exciting avenues for future research.

The purpose of this study is to address the significant impact AVs will have on public services and the ability of first responders to conduct their jobs safely and effectively. Autonomous vehicles (AVs) are expected to drastically change the transportation industry, and it is vital that first responders be equipped to integrate them into their occupational responsibilities.

A systematic literature review was conducted, and following a multistep exclusion process, 161 articles were selected for detailed review. The impacts of AVs on first responders were identified, classified and categorized into lists of challenges and opportunities. Based on the findings of the literature review, a SWOT (strengths, weaknesses, opportunities and threats) analysis was conducted, and stakeholder management strategies were designed.

Through the examination of the impacts of AVs on first responders, 17 identified challenges and opportunities were classified into the following categories: AV-related emergency response and training, perceptions and acceptance of AVs, technology development and laws and regulations. The study revealed that the optimal benefits of AVs would require stakeholders to focus more on how they interact with first responders; thus, 14 stakeholder management strategies were identified. First responders, AV manufacturers, legislators and future research paths will all benefit from this study, as it can facilitate smooth interactions between AVs and first responders.

A range of studies have been published on the safety of AVs and the public’s perceptions of this new technology; however, the integration of AVs and their interactions with first responders has been neglected. The goal of this study was to fill that research gap by providing a thorough synthesis of autonomous driving systems in the context of their interactions with first responders.

To solve the problem that the underwater vehicles is difficult to turn and exit in a small range in the face of complex marine environment such as concave and ring under the limitation of its limitation of its shape and maximum steering angle, this paper aims to propose an improved ant colony algorithm based on trap filling strategy and energy consumption constraint strategy.

Firstly, on the basis of searching the global path, the disturbed terrain was pre-filled in the complex marine environments. Based on the energy constraint strategy, the ant colony algorithm was improved to make the search path of the underwater vehicle meet the requirements of the lowest energy consumption and the shortest path in the complex obstacle environment.

The simulation results showed that the modified grid environment diagram effectively reduced the redundancy search and improved the optimization efficiency. Aiming at the problem of “the shortest distance is not the lowest energy consumption” in the traditional path optimization algorithm, the energy consumption level was reduced by 26.41% after increasing the energy consumption constraint, although the path length and the number of inflection points were slightly higher than the shortest path constraint, which was more conducive to the navigation of underwater vehicles.

The method proposed in this paper is not only suitable for trajectory planning of underwater robots but also suitable for trajectory planning of land robots.

This study aims to explore tourist perceptions and behaviors toward electric vehicles (EVs) in the Delhi National Capital Region using the technology continuance theory.

An online survey involving 226 respondents uses structural equation modeling to analyze correlations among factors, including perceived enjoyment, facilitating conditions, ease of use, satisfaction, cost, image and performance.

This study reveals that enhancing perceived enjoyment and facilitating conditions can improve the user-friendliness of EVs. Additionally, reducing perceived cost, enhancing image and improving perceived performance can increase the perceived usefulness of EVs. Perceived ease of use strongly influences user satisfaction, while perceived usefulness and satisfaction positively impact users’ attitudes and intentions to use EVs. Although factors such as experience, environmental consciousness, age and gender influence perceptions, focusing on enjoyment, facilitating conditions, cost, image and performance can significantly enhance user satisfaction and intention to use EVs.

The findings underscore several actionable recommendations for businesses and policymakers to boost EV adoption at tourist destinations. The potential benefits of EV adoption, such as improved environmental sustainability, enhanced technological image and increased tourist satisfaction, can serve as a source of inspiration and motivation. Enhancing user experience by prioritizing comfort and convenience in EV design is crucial. Addressing cost concerns through incentives and cost-effective pricing strategies can make EVs more appealing. Marketing campaigns highlighting environmental benefits and technological advancements can improve EV image and performance perception. Prioritizing tourists’ satisfaction and support services is essential, along with educational campaigns to increase awareness. Infrastructure development, including expanding charging networks, and supportive policies like tax incentives, can further encourage EVs adoption, accelerating the transition to sustainable transportation.

This research contributes to understanding tourist perspectives on EV adoption within the context of sustainable tourism and technology adoption.

Spurred by the high turnover in the pharmaceutical industry, locating pharmacies inside urban areas along with the high product perishability in this industry, the pharmaceutical supply chain management has recently gained increasing attention. Accordingly, this paper unveils an inventory-routing problem for designing a pharmaceutical supply chain with perishable products and time-dependent travel time in an uncertain environment.

In this study, mathematical programming is employed to formulate a multi-graph network affected by the traffic volume in order to adapt to real-world situations. Likewise, by transforming the travel speed function to the travel time function using a step-by-step algorithm, the first-in-first-out property is warranted. Moreover, the Box–Jenkins forecasting method is employed to diminish the demand uncertainty.

An appealing result is that the delivery horizon constraint in the under-study multi-graph network may eventuate in selecting a longer path. Our analysis also indicates that the customers located in the busy places in the city are not predominantly visited in the initial and last delivery horizon, which are the rush times. Moreover, it is concluded that integrating disruption management, routing planning and inventory management in the studied network leads to a reduction of costs in the long term.

Applying the time-dependent travel time with a heterogeneous fleet of vehicles on the multi-graph network, considering perishability in the products for reducing inventory costs, considering multiple trips of transfer fleet, considering disruption impacts on supply chain components and utilizing the Box–Jenkins method to reduce uncertainty are the contributions of the present study.