Search Results (502)

Pedestrian safety remains a pressing challenge in low- and middle-income countries (LMICs), where global predictive models often misrepresent local realities. This study tests the hypothesis that global predictive models, such as the International Road Assessment Programme (iRAP), overestimate countermeasure effectiveness in LMICs because key contextual factors are omitted. The two-phase research combined a PRISMA-based systematic literature review (SLR) with a quantitative iRAP performance gap analysis of the countermeasures implemented in the candidate studies of the SLR. The review systematically evaluated the effectiveness of pedestrian safety countermeasures, with an emphasis on their application in LMIC contexts. Following PRISMA 2020 guidelines, 14 longitudinal before–after studies were selected from 1911 records and screened with EPPI-Reviewer 4 software. The analysis identified 33 contextual factors shaping countermeasure performance across both high- and low-income settings; of these, 23 were specific to LMICs, and 13 are not accounted for in the iRAP model. The findings show that iRAP systematically overestimates countermeasure effectiveness in LMICs due to weak enforcement, poor maintenance, and informal road use. Transverse rumble strips were the only intervention consistently effective across diverse LMIC settings. A novel performance gap analysis of five LMIC case studies revealed an average discrepancy of 30.9% (SD = 29.7%) between predicted and observed outcomes. A risk of bias assessment showed that most LMIC studies were of moderate to serious risk, reflecting systemic data limitations and a frequent reliance on proxy outcomes. These findings highlight the urgent need for recalibrated, context-sensitive frameworks that incorporate enforcement, maintenance, and socio-economic variables. Policy implications include prioritising affordable and scalable countermeasures, pairing infrastructure with enforcement and education, and strengthening crash data systems to support more realistic, evidence-based road safety planning. Full article

Improving the efficiency of stair evacuation plays a crucial role in emergency management, which may be shaped by pedestrians’ lane-selection behavior. However, most existing studies describe pedestrians’ lane-selection preferences during stair evacuation, while the mechanisms behind these preferences are not yet well understood. To solve this issue, a stair evacuation observation experiment and a questionnaire survey were carried out to investigate pedestrian stair lane-selection preferences. Based on 1793 pieces of experimental data and 397 questionnaires, it is found that (1) pedestrians in the middle lane are more inclined to proactively change lanes based on their personal preference when sufficient space is available. (2) The primary factors influencing pedestrians’ lane-selection preferences are perceived safety, shortest path, and behavioral habit. (3) As the distance to the wall increases, the preference for the wall-side lane gradually decreases. Notably, the rate of decline accelerates at first, then slows down as the wall becomes farther away. This study deeply deconstructs pedestrians’ stair lane-selection preferences which helps understand the interactions among pedestrians, between pedestrians and their surroundings. It offers a basis for the optimization of evacuation strategies, the design of emergency evacuation plans, and the calibration of evacuation simulation models. Full article

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

Sustainability assessment in UNESCO World Heritage city centres often treats spatial configuration, functional accessibility, and heritage governance as separate analytical domains. This study addresses this fragmentation by developing a composite assessment framework to evaluate morphological sustainability in historic urban cores. The Morphological Sustainability Model (MSM) and its numerical expression, the Morphological Sustainability Index (MSI), are applied to the Bursa Khans District for the 2020–2025 period. The model integrates Space Syntax variables (integration, connectivity, choice, and intelligibility), 15-Minute City indicators related to proximity, pedestrian accessibility, active mobility, and inclusivity, and Historic Urban Landscape-based governance evaluations derived from UNESCO-compliant management plans. These components are synthesised into six weighted composite indicators (BKH1–BKH6). Results show that the MSI increases from 0.38 in 2020 to 0.51 in 2025 (+34.2%), indicating a strengthened alignment between spatial configuration, pedestrian-oriented functional performance, and heritage governance capacity. The findings reveal a shift from car-oriented axial dominance toward a more pedestrian-centred spatial structure along the historic bazaar spine. Overall, the study demonstrates that the MSI provides a transferable, decision-support-oriented framework for assessing morphological sustainability in historic urban environments. Full article

Saudi Arabia’s ambition to improve quality of life is paving its way, and this study aligns with that vision, adopting an experimental approach to explore urban solutions to enhance outdoor thermal comfort for pedestrians in neighborhoods within Riyadh City, Saudi Arabia. Given the city’s hot and arid climate, outdoor spaces are often subject to extreme thermal conditions that reduce the quality of life for residents. To address this issue, the study utilizes Ladybug in Grasshopper, a tool designed for modeling the microclimate and assessing the impact of urban design strategies on outdoor thermal comfort. A base model representing the current urban fabric of selected neighborhoods is developed, and then multiple alternatives of urban morphology (sidewalk, setbacks, fence, and vegetation) are evaluated for their effectiveness in mitigating heat stress and improving outdoor thermal conditions. The findings from this study provide valuable insights into how urban planning and design interventions can be tailored to the unique climatic challenges of Riyadh, with potential applications for enhancing the sustainability, livability, and overall quality of life of the city’s neighborhoods. Full article

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic. Full article

Under the pressing imperative of achieving “dual carbon” goals and advancing urban low-carbon transitions, understanding how neighborhood spatial environments influence carbon emissions has become a critical challenge for enabling refined governance and precise planning in urban carbon reduction. Taking the central urban area of Xining as a case study, this research establishes a high-precision estimation framework by integrating Semantic Segmentation of Street View Images and Point of Interest data. This study employs a Geographically Weighted XGBoost model to capture the spatial non-stationarity of emission drivers, achieving a median R² of 0.819. The results indicate the following: (1) Socioeconomic functional attributes, specifically POI Density and POI Mixture, exert a more dominant influence on carbon emissions than purely visual features. (2) Lane Marking General shows a strong positive correlation by reflecting traffic pressure, Sidewalks exhibit a clear negative correlation by promoting active travel, and Building features display a distinct asymmetric impact, where the driving effect of high density is notably less pronounced than the negative association observed in low-density areas. (3) The development of low-carbon neighborhoods should prioritize optimizing functional mixing and enhancing pedestrian systems to construct resilient and low-carbon urban spaces. This study reveals the non-linear relationship between street visual features and neighborhood carbon emissions, providing an empirical basis and strategic references for neighborhood planning and design oriented toward low-carbon goals, with valuable guidance for practices in urban planning, design, and management. Full article

Mid-sized Philippine cities commonly rely on fixed-time traffic signal plans that cannot respond to short-term, demand-driven surges, resulting in measurable idle time at stop lines, increased delay, and unnecessary emissions, while adaptive signal control has demonstrated performance benefits, many existing solutions depend on centralized infrastructure and high-bandwidth connectivity, limiting their applicability for resource-constrained local government units (LGUs). This study reports a field deployment of TrafficEZ, a lightweight edge AI signal controller that reallocates green splits locally using traffic-density approximations derived from cabinet-mounted cameras. The controller follows a macroscopic, cycle-level control abstraction consistent with Transportation System Models (TSMs) and does not rely on stationary flow–density–speed (fundamental diagram) assumptions. The system estimates queued demand and discharge efficiency on-device and updates green time each cycle without altering cycle length, intergreen intervals, or pedestrian safety timings. A quasi-experimental pre–post evaluation was conducted at three signalized intersections in El Salvador City using an existing 125 s, three-phase fixed-time plan as the baseline. Observed field results show average per-vehicle delay reductions of 18–32%, with reclaimed effective green translating into approximately 50–200 additional vehicles per hour served at the busiest approaches. Box-occupancy durations shortened, indicating reduced spillback risk, while conservative idle-time estimates imply corresponding CO₂ savings during peak periods. Because all decisions run locally within the signal cabinet, operation remained robust during backhaul interruptions and supported incremental, intersection-by-intersection deployment; per-cycle actions were logged to support auditability and governance reporting. These findings demonstrate that density-driven edge AI can deliver practical mobility, reliability, and sustainability gains for LGUs while supporting evidence-based governance and performance reporting. Full article

The scale and magnitude of urban heating are often assessed using Satellite-Derived Land Surface Temperature (SD-LST). Yet, discrepancies in spatial resolution limit SD-LST’s ability to reflect pedestrian thermal experience, potentially leading to ineffective mitigation strategies. Hyper-local measurements of urban heat, defined as surface temperatures (T_S) at the scale of pedestrian activity (e.g., bus stops or street segments), may provide more accurate insights into thermal comfort. This study compares hyper-local ~0.01 m resolution T_S collected via consumer-grade Forward-Looking Infrared (FLIR) thermography with resampled 30 m resolution SD-LST from Landsat 8 and 9 images to evaluate their utility in predicting thermal comfort indices across 60 bus stops in Denver, Colorado. During the summer of 2023, 270 FLIR measurements were collected over 19 dates, with a four-day subset (n = 33) coinciding with Landsat imagery. FLIR T_S averaged 25.12 ± 5.39 °C, while SD-LST averaged 35.90 ± 12.56 °C, a significant 10.77 °C difference (95% CI: 6.81–14.73; p < 0.001). FLIR T_S strongly correlated with biometeorological metrics such as air temperature and mean radiant temperature (r > 0.8; p < 0.001), while SD-LST correlations were weak (r < 0.3). Linear mixed-effects models using FLIR T_S explained 50–66% of the variance in thermal comfort indices and met ISO 7726 standards. Each 1 °C increase in FLIR TS predicted a 0.75 °C rise in mean radiant temperature. These results highlight hyper-local thermography as a reliable, low-cost tool for urban heat resilience planning. Full article

This study treats initial building modal planning as the organizing unit for tropical neighborhood design and unifies three pedestrian-scale objectives: perimeter daylight at 1.5 m (S), grey-space wind (W), and ground-plane visibility (V)—within a typology-aware, two-void layout grammars for Haikou. Using α-referenced deviations (|ΔMean| + 0.25|ΔIQR| per metric) and multi-objective simulated annealing over 16 morphotypes plus two VOIDs, we obtained a Pareto archive of 4000 layouts. A thick knee emerges: mid-field paired voids with bar–court compositions consistently suppress W and V deviations while keeping S close to α; the central spine and cross-breath prototypes dominate among the top solutions, and the 80-layout atlas enables direct selection. The configuration and α baselines were fixed for full reproducibility, supporting policy-grade traceability. All evaluations were performed at the human interface with metric-specific aggregation (S over 14 non-VOID blocks; w/v over all 16), coupling building morphotypes, pedestrian-layer analytics, and archive-aware Multi-Objective Simulated Annealing (MOSA). Collectively, these results provide evidence-backed rules—site two voids near the middle, composed of tempered courts and bars, and provide strong support for near-term tropical planning codes and schematic design decisions. Full article

Transit-Oriented Development (TOD), formalized by Calthorpe and Poticha in 1993, emerged to counter urban sprawl, reduce car dependency, and revitalize historical community centers. Rooted in “new urbanism”, TOD emphasizes integrated regional land-use planning and high-capacity public transportation. In the Middle East, TOD implementation remains understudied, particularly regarding heritage integration and social equity in arid climates. Doha’s rapid social and economic transformation presents both opportunities and risks: growth offers urban revitalization yet threatens to displace communities and dilute cultural identity. Shifts in urban planning have aimed to address sustainability, connectivity, and heritage preservation. This study examines Msheireb Downtown Doha (MDD) to assess how TOD can restore historic districts while managing gentrification, enhancing accessibility and promoting inclusiveness. A mixed-methods approach was applied, including 12 semi-structured interviews with stakeholders (Qatar Rail, Msheireb Properties, Ministry of Municipality and Environment), purposive surveys of 80 urban users, site observations, and spatial mapping. Using the Node-Place-People (NPP) model, the study evaluates TOD effectiveness across transportation connectivity (node), built environment quality (place), and equity metrics (people). The findings show that MDD successfully implements fundamental TOD principles through its design, which enhances connectivity, walkability, social inclusiveness, and heritage preservation. However, multiple obstacles remain: the “peripheral island effect” limits benefits to the core, pedestrian–vehicular balance is unresolved, and commercial gentrification is on the rise. This research provides evidence-based knowledge for GCC cities pursuing sustainable urban regeneration by demonstrating both the advantages of TOD and the necessity for critical, context-sensitive implementation that focuses on social equity together with physical transformation. Full article

To address the challenges in unstructured orchard environments, including high geometric similarity between fruit trees (with the measured average Euclidean distance difference between point cloud descriptors of adjacent trees being less than 0.5 m), significant dynamic interference (e.g., interference from pedestrians or moving equipment can occur every 5 min), and uneven terrain, this paper proposes an improved mapping algorithm named OSC-LIO (Orchard Scan Context Lidar Inertial Odometry via Smoothing and Mapping). The algorithm designs a dynamic point filtering strategy based on Euclidean clustering and spatiotemporal consistency within a 5-frame sliding window to reduce the interference of dynamic objects in point cloud registration. By integrating local semantic features such as fruit tree trunk diameter and canopy height difference, a two-tier verification mechanism combining “global and local information” is constructed to enhance the distinctiveness and robustness of loop closure detection. Motion compensation is achieved by fusing data from an Inertial Measurement Unit (IMU) and a wheel odometer to correct point cloud distortion. A three-level hierarchical indexing structure—”path partitioning, time window, KD-Tree (K-Dimension Tree)”—is built to reduce the time required for loop closure retrieval and improve the system’s real-time performance. Experimental results show that the improved OSC-LIO system reduces the Absolute Trajectory Error (ATE) by approximately 23.5% compared to the original LIO-SAM (Tightly coupled Lidar Inertial Odometry via Smoothing and Mapping) in a simulated orchard environment, while enabling stable and reliable path planning and autonomous navigation. This study provides a high-precision, lightweight technical solution for autonomous navigation in orchard scenarios. Full article

To address the limitations of conventional social force models in simulating high-density pedestrian crowds, this study proposes an enhanced model that incorporates visual perception constraints, group-type labeling, and collective avoidance mechanisms. Pedestrian trajectories were extracted from a bidirectional commercial street scenario using OpenCV, with YOLOv8 and DeepSORT employed for multiple object tracking. Analysis of pedestrian grouping patterns revealed that 52% of pedestrians walked in pairs, with distinct avoidance behaviors observed. The improved model integrates three key mechanisms: a restricted 120° forward visual field, group-type classification based on social relationships, and an exponentially formulated inter-group repulsive force. Simulation results in MATLAB R2023b demonstrate that the proposed model outperforms conventional approaches in multiple aspects: speed distribution (error < 8%); spatial density overlap (>85%); trajectory similarity (reduction of 32% in Dynamic Time Warping distance); and avoidance behavior accuracy (82% simulated vs. 85% measured). This model serves as a quantitative simulation tool and decision-making basis for the planning of pedestrian spaces, crowd organization management, and the optimization of emergency evacuation schemes in high-density pedestrian areas such as commercial streets and subway stations. Consequently, it contributes to enhancing pedestrian mobility efficiency and public safety, thereby supporting the development of a sustainable urban slow transportation system. Full article

Interest in the adoption of autonomous vehicles (AVs) continues to grow. It is essential to design new software architectures that meet stringent real-time, safety, and scalability requirements while integrating heterogeneous hardware and software solutions from different vendors and developers. This paper presents a lightweight, modular, and scalable architecture grounded in Service-Oriented Architecture (SOA) principles and implemented in ROS 2 (Robot Operating System 2). The proposed design leverages ROS 2’s Data Distribution System-based Quality-of-Service model to provide reliable communication, structured lifecycle management, and fault containment across distributed compute nodes. The architecture is organized into Perception, Planning, and Control layers with decoupled sensor access paths to satisfy heterogeneous frequency and hardware constraints. The decision-making core follows an event-driven policy that prioritizes fresh updates without enforcing global synchronization, applying zero-order hold where inputs are not refreshed. The architecture was validated on a 1:10-scale autonomous vehicle operating on a city-like track. The test environment covered canonical urban scenarios (lane-keeping, obstacle avoidance, traffic-sign recognition, intersections, overtaking, parking, and pedestrian interaction), with absolute positioning provided by an indoor GPS (Global Positioning System) localization setup. This work shows that the end-to-end Perception–Planning pipeline consistently met worst-case deadlines, yielding deterministic behaviour even under stress. The proposed architecture can be deemed compliant with real-time application standards for our use case on the 1:10 test vehicle, providing a robust foundation for deployment and further refinement. Full article

Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios. Full article