Environmental sustainability is a crucial issue for all human beings, and vehicle emissions significantly contribute to climate change. This has prompted many countries, including China, Norway, and Germany, to focus on electrifying transportation. This study quantifies the life cycle carbon dioxide (CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) emissions of electric buses (EBs) in Guangzhou, China, via a life cycle analysis methodology, revealing an average life cycle emission of 1,097.07 g CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>·km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>·vehicle<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup>. The operation and charging stage contributes the most to the lifespan of CO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions at 69.6%, driven by carbon-intensive power grid. Compared with conventional internal combustion engine buses, EBs result in significant emission reductions, but regional grid carbon intensity variations across China mean that their benefits depend on nationwide green energy adoption. By 2030, emissions are projected to decline by 15.28%, aligning with carbon peak goals. The findings emphasize that transitioning to renewable energy grids and hybrid technologies is critical for sustainable transportation.

The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM's capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.

This study develops new methods for evaluating detector health via event-based outputs and existing traffic flow theory. In this work, event-based detector data outputs were used to develop empirical vehicle volume-density curves per Greenshields fundamental model. Through integration, these empirical lines were compared with a conceptual volume-density curve for each detector, which was generated with average headway and posted speed limit data. The detector performance and site information were also used to model a predicted volume-density relationship for each detector on the basis of empirical observations, which was then compared with the conceptual line in the same manner as the empirical lines. The outcomes of each comparison were then used to create a database for assessing detector health within the structure of an algorithm. The algorithm is presented and discussed, followed by directions for future research, applications for practice, lessons learned, and limitations of this work.

The mobility landscape is experiencing major changes due to two emerging transportation trends, autonomous vehicles (AVs) and on-demand transportation, and the convergence of these smart mobility innovations as shared autonomous vehicles (SAVs) can considerably alter travel behavior and consequently the ecological and societal aspects of the transportation sector. On-demand autonomous mobility is a promising transportation mode, but further research is necessary to evaluate its various aspects and implications prior to widespread adoption. Thus, this study investigates the effects of integrating automation and on-demand mobility by analyzing the effects on the environment, public transportation, land use, vehicle ownership, and public acceptance. A comprehensive literature review was performed, and through a detailed review of 210 articles, the impacts of each of these categories were determined and classified according to their causes, and the number of publications with which they were cited in the literature was determined. The review showed that SAVs can either positively or negatively impact categories and have the potential to minimize mobility obstacles and transportation inequity if legislators use technology to develop a better transportation system by initiating effective policies that govern the four impacted areas. A list of 22 policy recommendations designed to avoid the negative consequences of SAVs by maximizing the benefits of the technology while limiting the associated risks was also identified. The findings of this review will be beneficial to AV manufacturers, transportation professionals, and especially policymakers, who play an integral role in shaping how society benefits from SAV technology.

This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of respondents provided valuable insights toward various CAV scenarios such as riding in a vehicle with no driver, self-driving public transport, self-driving taxis, and heavy vehicles without drivers. The results highlight the significant impact of safety concerns about automated vehicles on individuals’ attitudes across all scenarios. Higher levels of concern were associated with more negative attitudes, and a strong correlation between concerns and opposition underlines the necessity of addressing these apprehensions to build public trust and promote CAV adoption. Interestingly, nearly 70% of respondents felt uncomfortable driving next to a CAV, but they displayed more confidence in adopting automated public transport in the near future. Additionally, around 40% of participants indicated a strong willingness to purchase a CAV, primarily driven by the desire to reduce their carbon footprint and safety considerations. Notably, respondents with health conditions or disability exhibited heightened interest (almost double those without health conditions) in CAV technology. Gender differences emerged in attitudes and preferences toward CAVs, with women expressing a greater level of concern and perceiving higher barriers to CAV deployment. This emphasizes the importance of employing targeted approaches to address the specific concerns of different demographics. The study also underscores the role of trust in technology as a significant barrier to CAV deployment, ranking high among respondents’ concerns. To overcome these challenges and facilitate successful CAV deployment, various strategies are suggested, including live demonstrations, dedicated routes for automated public transport, adoption incentives, and addressing liability concerns. The findings from this study offer valuable insights for government agencies, vehicle manufacturers, and stakeholders in promoting the successful implementation of CAVs. By understanding societal acceptance and addressing concerns, decision-makers can devise effective interventions and policies to ensure the safe and widespread adoption of CAVs in Australia. Moreover, vehicle manufacturers can leverage these results to consider design aspects that align with passenger preferences, thereby facilitating the broader acceptance and adoption of CAVs in the future. Finally, this research provides a significant contribution to the understanding of public perception and acceptance of CAVs in the Australian context. By guiding decision-making and informing strategies, the study lays the foundation for a safer and more effective integration of CAVs into the country’s transportation landscape.

Previous studies have identified trust as one of the key factors in the technology acceptance of autonomous vehicles. As these studies mostly investigated the population in general, little is known about segment-specific differences. Furthermore, the widely used survey methods are less able to capture the deeper forms of trust-which neuroscientific methods are much better suited to capture. The main objective of our research is to study trust as one of the key factors of technology acceptance related to autonomous vehicles by using neuroscientific methods for specific consumer segments. Real-time eye-tracking tests were applied to a sample of 113 participants, combined with a posttest self-report. The tests were carried out under laboratory conditions during which our subjects watched videos recorded with the internal cameras of autonomous vehicles. Based on the fixation count, total fixation duration, and pupil dilation, we empirically verified that the trust level of all five identified segments is relatively low, while the trust level of the "traditional rejecting" segment is the lowest. An increase in trust level can be shown if the subjects receive extra information about the journey. Another important finding is that the self-reported trust level is not always congruent with the eye-tracking analysis results; therefore, combined approaches can lead to greater measurement validity.

The on-ramp merging in multi-lane highway scenarios presents challenges due to the complexity of coordinating vehicles’ merging and lane-changing behaviors, while ensuring safety and optimizing traffic flow. However, there are few studies that have addressed the merging problem of ramp vehicles and the cooperative lane-change problem of mainline vehicles within a unified framework and proposed corresponding optimization strategies. To tackle this issue, this study adopts a cyber-physical integration perspective and proposes a graph-based solution approach. First, the information of vehicle groups in the physical plane is mapped to the cyber plane, and a dynamic conflict graph is introduced in the cyber space to describe the conflict relationships among vehicle groups. Subsequently, graph decomposition and search strategies are employed to obtain the optimal solution, including the set of mainline vehicles changing lanes, passing sequences for each route, and corresponding trajectories. Finally, the proposed dynamic conflict graph-based algorithm is validated through simulations in continuous traffic with various densities, and its performance is compared with the default algorithm in SUMO. The results demonstrate the effectiveness of the proposed approach in improving vehicle safety and traffic efficiency, particularly in high traffic density scenarios, providing valuable insights for future research in multi-lane merging strategies.

Throughout much of human history, the vast majority of people lived in small communities. However, in the last few centuries, and particularly in recent decades, there has been a dramatic shift. A massive migration has moved populations from rural to urban areas. United Nations reports state that over 4.3 billion individuals now inhabit urban regions, which accounts for more than half (55% as of 2017) of the global population. In most high-income nations, including Western Europe, the Americas, Australia, Japan, and the Middle East, over 80% of people live in urban areas. This figure ranges from 50% to 80% in upper-middle-income countries like Eastern Europe,