Search Results (89)

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

Traditional collision risk warning methods primarily focus on vehicle-to-vehicle collisions, neglecting conflicts between vehicles and vulnerable road users (VRUs) such as pedestrians, while the difficulty in predicting pedestrian trajectories further limits the accuracy of collision warnings. To address this problem, this study proposes a vehicle-to-everything-based (V2X) multi-fusion vehicle–pedestrian collision warning method, aiming to enhance the traffic safety protection for VRUs. First, Unmanned Aerial Vehicle aerial imagery combined with the YOLOv7 and DeepSort algorithms is utilized to achieve target detection and tracking at unsignalized intersections, thereby constructing a vehicle–pedestrian interaction trajectory dataset. Subsequently, key foundational modules for collision warning are developed, including the vehicle trajectory module, the pedestrian trajectory module, and the risk detection module. The vehicle trajectory module is based on a kinematic model, while the pedestrian trajectory module adopts an Attention-based Social GAN (AS-GAN) model that integrates a generative adversarial network with a soft attention mechanism, enhancing prediction accuracy through a dual-discriminator strategy involving adversarial loss and displacement loss. The risk detection module applies an elliptical buffer zone algorithm to perform dynamic spatial collision determination. Finally, a collision warning framework based on the Monte Carlo (MC) method is developed. Multiple sampled pedestrian trajectories are generated by applying Gaussian perturbations to the predicted mean trajectory and combined with vehicle trajectories and collision determination results to identify potential collision targets. Furthermore, the driver perception–braking time (TTM) is incorporated to estimate the joint collision probability and assist in warning decision-making. Simulation results show that the proposed warning method achieves an accuracy of 94.5% at unsignalized intersections, outperforming traditional Time-to-Collision (TTC) and braking distance models, and effectively reducing missed and false warnings, thereby improving pedestrian traffic safety at unsignalized intersections. Full article

Pedestrian detection systems are widely used in safety-critical domains such as autonomous driving, where deep neural networks accurately perceive individuals and distinguish them from other objects. However, their vulnerability to backdoor attacks remains understudied. Existing backdoor attacks, relying on unnatural digital perturbations or explicit patches, are difficult to deploy stealthily in the physical world. In this paper, we propose a novel backdoor attack method that leverages real-world occlusions (e.g., backpacks) as natural triggers for the first time. We design a dynamically optimized heuristic-based strategy to adaptively adjust the trigger’s position and size for diverse occlusion scenarios, and develop three model-independent trigger embedding mechanisms for attack implementation. We conduct extensive experiments on two different pedestrian detection models using publicly available datasets. The results demonstrate that while maintaining baseline performance, the backdoored models achieve average attack success rates of 75.1% on KITTI and 97.1% on CityPersons datasets, respectively. Physical tests verify that pedestrians wearing backpack triggers could successfully evade detection under varying shooting distances of iPhone cameras, though the attack failed when pedestrians rotated by 90°, confirming the practical feasibility of our method. Through ablation studies, we further investigate the impact of key parameters such as trigger patterns and poisoning rates on attack effectiveness. Finally, we evaluate the defense resistance capability of our proposed method. This study reveals that common occlusion phenomena can serve as backdoor carriers, providing critical insights for designing physically robust pedestrian detection systems. Full article

Battery electric vehicles (BEVs) are seeing widespread adoption globally due to technological improvements, lower manufacturing costs, and supportive policies aimed at reducing greenhouse gas emissions. Governments have introduced incentives such as purchase subsidies and investments in charging infrastructure, while automakers continue to broaden their electric vehicle portfolios. Although BEVs show high overall safety performance comparable to internal combustion engine vehicles (ICEVs), they also raise distinct safety challenges that merit policy attention. This review synthesizes the current literature on safety concerns associated with BEVs, with particular attention to fire risks, vehicle weight, low-speed noise levels, and unique driving characteristics. Fire safety remains a significant issue, as lithium-ion battery fires, although less frequent than those in ICEVs, tend to be more severe and difficult to manage. Strategies such as improved thermal management, fire enclosures, and standardized response protocols are essential. BEVs are typically heavier than ICEVs, affecting crash outcomes and braking performance. These risks are especially important for interactions with pedestrians and smaller vehicles. Quiet operation at low speeds can also reduce pedestrian awareness, prompting regulations for vehicle sound alerts. Together, these issues highlight the need for policies that address both emerging safety risks and the evolving nature of BEV technology. Full article

This study presents a systematic bibliometric review of wearable technologies aimed at vulnerable road user (VRU) safety, covering publications from 2000 to 2025. Guided by PRISMA procedures and a PICo-based search strategy, 58 records were extracted and analyzed in CiteSpace, yielding visualizations of collaboration networks, publication trajectories, and intellectual structures. The results indicate a clear evolution from single-purpose, stand-alone devices to integrated ecosystem solutions that address the needs of diverse VRU groups. Six dominant knowledge clusters emerged—street-crossing assistance, obstacle avoidance, human–computer interaction, cyclist safety, blind navigation, and smart glasses. Comparative analysis across pedestrians, cyclists and motorcyclists, and persons with disabilities shows three parallel transitions: single- to multisensory interfaces, reactive to predictive systems, and isolated devices to V2X-enabled ecosystems. Contemporary research emphasizes context-adaptive interfaces, seamless V2X integration, and user-centered design, and future work should focus on lightweight communication protocols, adaptive sensory algorithms, and personalized safety profiles. The review provides a consolidated knowledge map to inform researchers, practitioners, and policy-makers striving for inclusive and proactive road safety solutions. Full article

The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article

Urban environments and climate-related challenges impact older adults’ health and well-being. To address these challenges, climate adaptation strategies and urban design guidelines should be tailored to older adults’ needs. Ethnographic studies can help identify these needs by involving them directly in the research process. This study uses ethnographic research to explore older adults’ perceptions and behaviours regarding climate change risks and impacts, health, and mobility challenges in a vulnerable urban area—São Roque da Lameira, Porto, Portugal. It studies the applicability and complementarity of four participatory methods that can inform urban design: (I) semi-structured interviews, (II) ‘go-along’ interviews, (III) user observations, and (IV) emotional mapping. The qualitative data collected were analysed through thematic and spatial analysis. Common themes emerged between the four methods, including concerns about accessibility, safety, and comfort, such as uneven pavements, lack of seating, and poor infrastructure for people with reduced mobility. Participants recommended improvements, such as more green spaces and better pedestrian infrastructure quality. Notably, each method uncovered distinct dimensions, highlighting the added value of a multi-method approach. This study demonstrates that combining participatory methods offers deeper, context-specific insights to inform age-friendly and climate-resilient urban design. Future research should take climate-focused methods and a multidisciplinary approach into consideration. Full article

Optimizing the wind environment within university dormitory areas is essential for ensuring student safety, enhancing living comfort, and improving building energy efficiency. In this study, the wind environment of multi-story university dormitories in cold regions is comprehensively investigated through computational fluid dynamics (CFD) simulations conducted with the PHONECIS software (version 2019), combined with orthogonal experimental design methods for systematic analysis and optimization. Through orthogonal experimental design, the effects of key morphological parameters—including building layout, length, width, and height—on the near-ground wind environment were evaluated. Among these, building width exerted the greatest influence, followed by building length, layout form, and finally building height. Based on the analysis, the optimal design scheme features a staggered building layout, with individual dormitory buildings measuring 60 m in length, 16 m in width, and 11.4 m in height. This optimized design was implemented in the multi-story dormitory area of the eastern section of Chang’an University’s New Campus. A comparative analysis of wind speed distribution before and after optimization, conducted specifically for the outdoor spaces during the winter season, revealed that the average near-ground wind speed was reduced from 3.3 m/s to 2.7 m/s, achieving an 18% reduction. The staggered arrangement and adjusted building proportions effectively dispersed airflow, mitigated high-velocity zones, and significantly enhanced outdoor wind comfort and pedestrian safety. This study introduces a morphology–wind environment coupling strategy from an architectural perspective to guide the design of dormitory buildings in cold regions. Rather than focusing on mathematical modeling, the research emphasizes design-oriented outcomes aimed at informing and optimizing practical architectural solutions for safer, more comfortable, and energy-efficient campus living environments. Full article

Airports are complex environments where efficient localization and intelligent traffic management are essential for ensuring smooth navigation and operational efficiency for both pedestrians and Autonomous Guided Vehicles (AGVs). This study presents an Artificial Intelligence (AI)-driven airport traffic management system that integrates Visible Light Communication (VLC), rerouting techniques, and adaptive reward mechanisms to optimize traffic flow, reduce congestion, and enhance safety. VLC-enabled luminaires serve as transmission points for location-specific guidance, forming a hybrid mesh network based on tetrachromatic LEDs with On-Off Keying (OOK) modulation and SiC optical receivers. AI agents, driven by Deep Reinforcement Learning (DRL), continuously analyze traffic conditions, apply adaptive rewards to improve decision-making, and dynamically reroute agents to balance traffic loads and avoid bottlenecks. Traffic states are encoded and processed through Q-learning algorithms, enabling intelligent phase activation and responsive control strategies. Simulation results confirm that the proposed system enables more balanced green time allocation, with reductions of up to 43% in vehicle-prioritized phases (e.g., Phase 1 at C1) to accommodate pedestrian flows. These adjustments lead to improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian traffic across multiple intersections. Additionally, traffic flow responsiveness is preserved, with critical clearance phases maintaining stability or showing slight increases despite pedestrian prioritization. Simulation results confirm improved route planning, reduced halting times, and enhanced coordination between AGVs and pedestrian flows. The system also enables accurate indoor localization without relying on a Global Positioning System (GPS), supporting seamless movement and operational optimization. By combining VLC, adaptive AI models, and rerouting strategies, the proposed approach contributes to safer, more efficient, and human-centered airport mobility. Full article

Medium-sized cities face unique challenges in fostering sustainable mobility due to their socio-spatial characteristics, including recent decentralized services and urban sprawl. This study examines user-centric factors influencing mobility behaviors in Caceres, Spain, through qualitative focus group analysis with 18 participants across two age groups. By employing a co-occurrence methodology, this research identifies key relationships within four thematic areas: public transport, active mobility, innovation, and urban planning. The findings reveal persistent car dependency despite sustainable policies, driven by the following: (1) inadequate public transport coordination between urban and regional areas, poor information availability, and lack of service synchronization; (2) perceived safety concerns, insufficient infrastructure for cycling, and ineffective pedestrianization strategies; (3) limited adoption of technological solutions due to cultural barriers, preference for informal arrangements, and usability issues with transport apps; and (4) mismatches between urban form and service distribution, proximity perception, and consumer preferences reinforcing car dependency. This study underscores the need for integrated transport systems, mixed land-use planning, and improved accessibility measures to achieve equitable and sustainable mobility transitions. The conclusion includes a series of policy recommendations. Full article

Residential street lighting plays a crucial role in enhancing the reassurance for pedestrians returning home late at night. However, street lighting is sometimes recommended and required to be kept at lower levels at night, due to problems such as light pollution, energy consumption, and negative economics. To solve these problems, this study designed a new Dynamic tracking lighting control mode capable of greater interactivity. Our study aimed to determine whether this new interactive lighting model can balance pedestrian safety with energy savings, compared with other lighting approaches used in low-light environments. In this experiment, 30 participants explored four lighting conditions in a simulated nighttime street environment through virtual reality (VR) and completed their assessment of each lighting mode. The statistical analysis of the results using the Friedman ANOVA test revealed that the Dynamic tracking lighting mode had advantages in improving the pedestrians’ reassurance compared with the other three lighting modes. Moreover, an additional recognition test experiment recorded the distance between each other whenever a participant recognized a stranger agent. The experimental results showed that this Dynamic tracking lighting mode can improve pedestrians’ ability to recognize others in low-light environments. These findings provide new strategies and ideas for urban energy conservation and environmental protection. Full article

The influence of sidewalk paving materials on pedestrian safety and comfort remains an underexplored topic within the walkability literature. This pilot study aims to address this gap by evaluating the role of five surface-related attributes—roughness, friction, texture, heat retention, and maintenance—through a qualitative approach complemented by a simplified quantitative evaluation. The study was conducted along a pedestrian route in Braga, Portugal, where pedestrian perceptions were collected via a questionnaire and compared with objective measurements obtained at seven testing points with different paving materials. The results indicate a strong preference for concrete and mortar pavements due to their slip-resistant surfaces, smoothness, and overall regularity. Quantitative tests confirmed that these materials exhibited the highest slip resistance and surface regularity, reinforcing the general alignment between pedestrian perceptions and measured performance. Participants rated paving attributes higher than others, such as sidewalk width or obstacle-free paths. Notable demographic differences also emerged: women rated sidewalk attributes more highly than men, seniors preferred traditional stone pavements more, and adults favored concrete. These findings highlight the importance of integrating surface-related sidewalk attributes into walkability assessments and urban design strategies to promote safer, more comfortable, and more inclusive pedestrian environments. Full article

Rapid ribbon development in Qasimabad Taluka, Hyderabad, has resulted in fragmented neighborhoods due to unplanned urban expansion. This study evaluates the critical factors influencing social sustainability in residential communities, focusing on neighborhood connectivity, safety, and green spaces. Employing a quantitative approach, data were collected through quota sampling from 5–7% of government-approved housing schemes in Qasimabad. A Satisfaction Index was developed to assess residents’ perceptions of accessibility, safety, amenities, and community engagement and analyzed via SPSS-22. Key findings revealed severe dissatisfaction: only 19% of private vehicle users reported satisfaction, while over 60% rated pedestrian and cycling infrastructure as inadequate. Green spaces intended for communal use deteriorated, causing a 21.58% reduction in satisfaction. Despite moderate daytime safety ratings (+53.95%), the overall social sustainability index remained negative (−148.61%). This study underscores the urgency of integrated urban strategies—enhancing accessibility, revitalizing green spaces, improving safety measures, and fostering community engagement—to address fragmentation and achieve socially sustainable neighborhoods. Full article

Realistically modeling interactions between road users—like those between drivers or between drivers and pedestrians—within experimental settings come with pragmatic challenges. Due to practical constraints, research typically focuses on a limited subset of potential scenarios, raising questions about the scalability and generalizability of findings about interactions to untested scenarios. Here, we aim to tackle this by laying the methodological groundwork for defining representative scenarios for dyadic (two-actor) interactions that can be analyzed individually. This paper introduces a conceptual guide for operationalizing controlled dyadic traffic interaction studies, developed through extensive interdisciplinary brainstorming to bridge theoretical models and practical experimental design. It elucidates critical trade-offs in scenario selection, interaction approaches, measurement strategies, and timing coordination, thereby enhancing reproducibility and clarity for future traffic interaction research and streamlining the design process. The methodologies and insights we provide aim to enhance the accessibility and quality of traffic interaction research, offering a guide that aids researchers in setting up studies and ensures clarity and reproducibility in reporting, bridging the gap between theoretical traffic interaction models and practical applications in controlled experiments, thereby contributing to advancements in human factors research on traffic management and safety. Full article

Over the past two decades, walkability, accessibility, and urban street culture have become major study topics in several areas of contemporary urban research, including urban sustainability, urban economy, healthy cities, and the x-minute city. Due to a plethora of evidence that supports the benefits of an accessible and walkable neighborhood, many countries and cities have put in place urban reform agendas that prioritize accessibility and walkability and promote urban street culture. Saudi Arabia is among those countries, as evidenced by the goals established in Saudi Vision 2030. This study focuses on the City of Hail’s efforts to enhance the walkability of its neighborhoods and the city’s accessibility. This study looks at how the newly constructed pedestrian infrastructure matches people’s expectations and how it influences how people in Hail walk. This study also makes specific suggestions for improvement and identifies ways forward. This study employs a three-fold ‘post-occupancy evaluation’ methodology that includes qualitative interviews, quantitative surveys, and direct observation, focusing on how the community interacts with the new pedestrian streetscapes. This study recommends designing areas in the City of Hail with improved pedestrian rights-of-way, enhancing sidewalk design and continuity, creating pedestrian buffer zones, boosting shade and shelter, and increasing safety and security. The suggested design changes will have the added benefit of strengthening the sense of community of Hail residents while also promoting mixed-use development, which is generally recognized as a more ‘organic’, natural development path that also aligns with Saudi’s heritage architecture, returning Hail’s urban space to its roots. These findings are crucial for shaping city planning in the City of Hail and beyond by emphasizing inclusive strategies that create lively communities where walking is encouraged and enjoyed. Full article