Search Results (84)

Traffic calming has emerged as a key urban strategy to reduce vehicle speeds and mitigate road traffic risks, with increasing recognition of its broader implications for public health, human behaviour, and urban liveability. This systematic literature review examines the multifaceted impacts of traffic-calming measures—from speed limit reductions to physical infrastructure and enforcement-based interventions—by synthesising findings from 28 peer-reviewed studies. Guided by the PRISMA framework, the review compiles research exploring links between traffic calming and outcomes related to public health, behaviour, and urban quality of life. Research consistently indicates that such interventions reduce both the frequency and severity of collisions, improve air and noise quality, and promote active mobility. These effects are shaped by user perceptions: non-motorised users tend to report higher levels of safety and accessibility, whereas motorised users often express frustration or resistance. Beyond safety and environmental improvements, traffic calming has been associated with greater use of public space, stronger social connections, and enhanced environmental aesthetics. The findings also show that key challenges may affect the effectiveness of traffic calming and these include negative attitudes among drivers, mixed outcomes for air quality, and unintended consequences such as traffic displacement or increased noise when interventions are poorly implemented. Overall, the findings suggest that traffic calming can serve as both a public health initiative and a tool for enhancing urban liveability, provided that the measures are designed with contextual sensitivity and supported by inclusive communication strategies. Full article

Over the last decade, a transformation in the automotive industry has been witnessed, as advancements in artificial intelligence and sensor technology have continued to accelerate the development of driverless vehicles. These systems are expected to significantly reduce traffic accidents and associated costs, making their integration into future transportation systems highly impactful. To explore this field in a controlled and flexible manner, scaled autonomous vehicle platforms are increasingly adopted for experimentation. In this work, we propose a set of methodologies to perform autonomous driving tasks through a software–hardware co-design approach. The developed system focuses on deploying a modular and reconfigurable software stack tailored to run efficiently on constrained embedded hardware, demonstrating a balance between real-time capability and computational resource usage. The proposed platform was implemented on a 1:10 scale vehicle that participated in the Bosch Future Mobility Challenge (BFMC) 2024. It integrates a high-performance embedded computing unit and a heterogeneous sensor suite to achieve reliable perception, decision-making, and control. The architecture is structured across four interconnected layers—Input, Perception, Control, and Output—allowing flexible module integration and reusability. The effectiveness of the system was validated throughout the competition scenarios, leading the team to secure first place. Although the platform was evaluated on a scaled vehicle, its underlying software–hardware principles are broadly applicable and scalable to larger autonomous systems. Full article

As an essential part of the low-altitude economy, low-altitude carriers are an important cornerstone of its development and a new industry that cannot be ignored strategically. However, it is difficult for the existing two-dimensional vehicle autonomous driving perception scheme to meet the needs of general key technologies for all-scene perception such as the global high-precision map construction of low-altitude vehicles in a three-dimensional space, the perception identification of local environmental traffic participants, and the extraction of key visual information under extreme conditions. Therefore, it is urgent to explore the development and verification of all-scene universal sensing technology for low-altitude intelligent vehicles. In this paper, the literature on vision-based urban rail transit and general perception technology in low-altitude flight environment is studied, and the paper summarizes the research status and innovation points from five aspects, namely the environment perception algorithm based on visual SLAM, the environment perception algorithm based on BEV, the environment perception algorithm based on image enhancement, the performance optimization of the perception algorithm using cloud computing, and the rapid deployment of the perception algorithm using edge nodes, and puts forward the future optimization direction of this topic. Full article

Due to accelerated urbanization, modern urban residents are facing increasing life pressures. Many citizens are experiencing situational aversion in daily commuting, and the deterioration in the traffic environment has led to psychological distress of varying degrees among urban dwellers. Cyclists, who account for about 7% of urban commuters, lack a sense of belonging in the urban space and experience significant deficiencies in the corresponding urban infrastructure, which causes more people to face significant barriers to choosing cycling as a mode of transportation. To address the aforementioned issues, this study proposes a bionic intelligent interaction helmet (BIIH) designed and validated based on the principles of bionics, which has undergone morphological design and structural validation. Constructed around the STM32-embedded development board, the BIIH is an integrated smart cycling helmet engineered to perceive environmental conditions and enable both human–machine interactions and environment–machine interactions. The system incorporates an array of sophisticated electronic components, including temperature and humidity sensors; ultrasonic sensors; ambient light sensors; voice recognition modules; cooling fans; LED indicators; and OLED displays. Additionally, the device is equipped with a mobile power supply, enhancing its portability and ensuring operational efficacy under dynamic conditions. Compared with conventional helmets designed for analogous purposes, the BIIH offers four distinct advantages. Firstly, it enhances the wearer’s environmental perception, thereby improving safety during operation. Secondly, it incorporates a real-time interaction function that optimizes the cycling experience while mitigating psychological stress. Thirdly, validated through bionic design principles, the BIIH exhibits increased specific stiffness, enhancing its structural integrity. Finally, the device’s integrated power and storage capabilities render it portable, autonomous, and adaptable, facilitating iterative improvements and fostering self-sustained development. Collectively, these features establish the BIIH as a methodological and technical foundation for exploring novel research scenarios and prospective applications. Full article

Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design. Full article

Urban noise pollution significantly degrades people’s health and well-being and, furthermore, traditional noise reduction strategies often overlook individual perception differences. This study proposed to explore the role of eco-acoustic indices in capturing the interplay between biophony, geophony, and anthrophony, and their relationship with classical acoustic metrics and the perceived soundscapes within a University Campus (University of “Mila-no-Bicocca”, Italy). The study area is divided in to eight different sites in “Piazza della Scienza” square. Sound measurements and surveys conducted in June 2023 across four paved sites and adjacent courtyards involved 398 participants (51.7% female, 45.6% male, 2.7% other). The main noise sources included road traffic, technical installations, and human activity, where traffic noise was more prominent at street-level sites (Sites 1–4) and technical installations dominated underground courtyards (6–8). Human activity was most noticeable at Sites 4–8, especially at Site 5, which showed the highest activity levels. A circumplex model revealed that street-level sites were less pleasant and eventful than courtyards. Pairwise comparisons of noise variability showed significant differences among sites, with underground locations offering quieter environments. Eco-acoustic analysis identified two site groups: one linked to noisiness and spectral features, the other to intensity distribution metrics. Technical installations, people, and traffic noises showed distinct correlations with acoustic indices, influencing emotional responses like stimulation and liveliness. These findings emphasize the need to integrate subjective perceptions with objective noise metrics in soundscape descriptions. Full article

Traditional rate–distortion (RD) theory examines the trade-off between the average length of the compressed representation of a source and the additive distortions of its reconstruction. The rate–distortion–perception (RDP) framework, which integrates the perceptual dimension into the RD paradigm, has garnered significant attention due to recent advancements in machine learning, where perceptual fidelity is assessed by the divergence between input and reconstruction distributions. In communication systems where downstream tasks involve generative modeling, high perceptual fidelity is essential, despite distortion constraints. However, while zero distortion implies perfect realism, the converse is not true, highlighting an imbalance in the significance of distortion and perceptual constraints. This article clarifies that incorporating perceptual constraints does not decrease the necessary rate; instead, under certain conditions, additional rate is required, even with the aid of common and private randomness, which are key elements in generative models. Consequently, we project an increase in expected traffic in intelligent communication networks with the consideration of perceptual quality. Nevertheless, a modest increase in rate can enable generative models to significantly enhance the perceptual quality of reconstructions. By exploring the synergies between generative modeling and communication through the lens of information-theoretic results, this article demonstrates the benefits of intelligent communication systems and advocates for the application of the RDP framework in advancing compression and semantic communication research. Full article

Road safety for vulnerable road users, particularly cyclists, remains a critical global issue. This study explores the potential of multimodal visual and haptic interaction technologies to improve cyclists’ perception of and responsiveness to their surroundings. Through a systematic evaluation of various visual displays and Haptic Feedback mechanisms, this research aims to identify effective strategies for recognizing and localizing potential traffic hazards. Study 1 examines the design and effectiveness of Visual Feedback, focusing on factors such as feedback type, traffic scenarios, and target locations. Study 2 investigates the integration of Haptic Feedback through wearable vests to enhance cyclists’ awareness of peripheral vehicular activities. By conducting experiments in realistic traffic conditions, this research seeks to develop safety systems that are intuitive, cognitively efficient, and tailored to the needs of diverse user groups. This work advances multimodal interaction design for road safety and aims to contribute to a global reduction in traffic incidents involving vulnerable road users. The findings offer empirical insights for designing effective assistance systems for cyclists and other non-motorized vehicle users, thereby ensuring their safety within complex traffic environments. Full article

Purpose: Constraint-induced movement therapy (CIMT) is an evidence-based intervention for arm recovery after acquired brain injury. Clinician knowledge, time and confidence in delivering CIMT are established barriers to the routine use of CIMT in practice. CIMT delivery via telehealth is one option to help overcome these barriers. This study aimed to understand clinician experiences of using an educational website and if the education and online resources contributed to their self-reported use of constraint-induced movement therapy via telehealth (TeleCIMT) in practice. Materials and Methods: Data were collected from a purposive sample of therapists registered to use the TeleCIMT website and website analytics. An online survey explored participants’ experience with CIMT delivery (both face to face and via telehealth), their perceptions of the website, and barriers and enablers to TeleCIMT implementation using the Capability, Opportunity, Motivation—Behaviour model. Website analytics were used to evaluate website traffic and resource use. Data were analysed using descriptive statistics (quantitative data) and content analysis (qualitative data). Results: Forty therapists responded to the survey; 72.5% (n = 29) of the respondents were occupational therapists, and 37.5% (n = 15) had delivered TeleCIMT. Most of the participants agreed that the website was easy to navigate (n = 26, 90%) and felt that they had the knowledge (n = 28, 96.6%) and skills (n = 24, 82.7%) to deliver TeleCIMT. The enablers to TeleCIMT included motivation to implement learnings from the website, confidence in delivering the programme, and the convenience of remote delivery. The perceived barriers to TeleCIMT use included limited access to technology and the availability of a client supporter to enable engagement in TeleCIMT. The resources used most frequently by the respondents were the participant preparation pack and participant programme pack. Shorter video learning modules (<11 min in duration) had greater engagement than longer video learning modules. Conclusions: Whilst online education and resources may enhance clinician knowledge of constraint-induced movement therapy and telehealth delivery, other barriers such as lack of technology access, may need to be addressed through additional learning and implementation strategies to support the routine use of TeleCIMT in practice. Full article

In this study, the urban air quality perceptions are explored in the metropolitan area of Târgu Mureș, Romania, emphasizing the interaction between socio-demographic factors, air quality measures, and industrial activity. The research addresses the need to understand how public perceptions align with objective air quality data and industrial influences, aiming to support sustainable urban planning. Data were gathered through a structured survey of 321 respondents and complemented by air quality measurements, including PM_2.5 and PM₁₀, and industrial production data. Statistical analyses, such as regression models and multidimensional scaling (PROXSCAL), were applied to identify patterns and relationships between socio-demographic characteristics, perceived air quality, and environmental factors. The results reveal significant links between demographic factors (e.g., age, awareness of local initiatives) and perceptions of air quality, alongside a nuanced interaction between air quality indicators and industrial activity. Respondents frequently identified traffic and industrial emissions as major contributors to air pollution, which was corroborated by sensor data trends. The findings underline the importance of integrating public perceptions with empirical data to design targeted policies and foster community engagement. This comprehensive approach provides actionable insights for improving urban air quality and advancing sustainable practices in mid-sized cities like Târgu Mureș. Full article

As the primary setting for students’ daily life and learning, university campuses are facing a growing concern about the impact of increased stress on students’ emotional well-being. The sound environment plays a critical role in affecting students’ mental health, learning efficiency, and overall well-being. However, research on the influence of campus soundscapes on students’ emotions is limited, and the mechanisms behind these effects remain to be explored. This study, using the Qishan Campus of Fuzhou University as a case, investigates the impact of campus soundscapes on students’ emotional perception and restorative effects. Four typical functional areas (academic zone (ACZ), residential zone (RDZ), recreational zone (RCZ), and administrative zone (ADZ)) were selected to analyze the effects of natural and artificial sounds on students’ emotions and physiological states. Based on EEG, eye tracking, sound level measurements, and questionnaire surveys, a one-way repeated measures ANOVA was used to assess students’ emotional arousal, valence, and physiological restoration under different soundscape conditions. The results showed that natural sounds, such as the sound of wind-blown leaves and flowing water, significantly improved students’ emotions and restorative effects, while artificial noises like construction sounds and traffic noise had negative impacts. Additionally, subjective perceptions of soundscape restoration were positively correlated with arousal, valence, and acoustic comfort, and negatively correlated with gaze frequency and pupil size. The findings provide a theoretical foundation for optimizing campus soundscape design and highlight the importance of natural sounds in enhancing students’ mental health and academic environment. Full article

This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as $R_{\mathrm{total}}$, is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within $R_{\mathrm{total}}$, and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception–reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. $R_{\mathrm{total}}$ has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity. Full article

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS). Full article

The subject of this paper stems from the potential threat to the development of tourism functions in coastal destinations, which carries significant consequences for the tourism labor market in these areas. This study examines the state and variability of cyanobacterial harmful algal blooms (cyanoHABs) and their potential impact on tourism, focusing on the Gdańsk agglomeration as a tourist hub in Northern Poland. Specifically, the research endeavors to explore the attitudes and career inclinations of prospective professionals in the tourism and hospitality (T&H) sector—students enrolled in higher educational institutions within the studied locale—toward the issue of cyanoHABs and its impact on their post-graduation employment aspirations within the T&H industry. The research employs both desk research methods and a structured questionnaire. The key findings reveal that despite the significant presence of cyanoHABs, particularly in July–August, there is also a peak in tourist flows. Thus, it is not definitively established that tourists select destinations based on the quality of water and beaches. Moreover, T&H students exhibit a comprehensive understanding of the cyanoHAB phenomenon and its detrimental effects on the perceived allure of tourist destinations and the employment market. Specifically, students predominantly acknowledged that cyanoHABs could diminish the tourist attractiveness of coastal regions, decrease tourist traffic, and foster negative opinions of the affected destination. Moreover, the findings confirm that, in students’ perceptions, cyanoHABs in seaside regions could detrimentally affect tourism-related businesses. Thus, it is not surprising that T&H students would not consider their future career in tourism within regions of high cyanoHAB risk. This study represents one of the pioneering efforts to examine the connections between cyanoHABs and students’ perceptions of their impact on tourism and career advancement in the T&H industry, which is directly linked with the environmental quality. The novelty of this research lies in its emphasis on students’ perspectives, offering insight into the future qualified workforce in the T&H sector. This approach may shed new light on a better understanding of how cyanoHABs may affect tourism and its labor market, influencing young people’s attitudes toward their future careers in T&H. Full article

The application of deep learning in autonomous vehicles has surged over the years with advancements in technology. This research explores the integration of deep learning algorithms into autonomous vehicles (AVs), focusing on their role in perception, decision-making, localization, mapping, and navigation. It shows how deep learning, as a part of machine learning, mimics the human brain’s neural networks, enabling advancements in perception, decision-making, localization, mapping, and overall navigation. Techniques like convolutional neural networks are used for image detection and steering control, while deep learning is crucial for path planning, automated parking, and traffic maneuvering. Localization and mapping are essential for AVs’ navigation, with deep learning-based object detection mechanisms like Faster R-CNN and YOLO proving effective in real-time obstacle detection. Apart from the roles, this study also revealed that the integration of deep learning in AVs faces challenges such as dataset uncertainty, sensor challenges, and model training intricacies. However, these issues can be addressed through the increased standardization of sensors and real-life testing for model training, and advancements in model compression technologies can optimize the performance of deep learning in AVs. This study concludes that deep learning plays a crucial role in enhancing the safety and reliability of AV navigation. This study contributes to the ongoing discourse on the optimal integration of deep learning in AVs, aiming to foster their safety, reliability, and societal acceptance. Full article