Search Results (132)

As urban waterfront public spaces have increasingly become important settings for residents’ daily leisure activities, there remains a lack of empirical evidence based on objective image data regarding how street-level visual environments influence different types of leisure activities. The existing studies have largely relied on macro-scale built environment indicators and paid limited attention to micro-scale visual perception from the pedestrian perspective. To address this gap, this study focuses on the core waterfront section of the Pearl River in Guangzhou. Behavioral observations were conducted across nine spatial units during different time periods on weekdays and weekends, yielding 54 samples of passive, active, and social activity intensity. Meanwhile, 109 street-view sampling points were established, generating 436 pedestrian-view images. Using Mask2Former with an ADE20K pre-trained model, visual environmental indicators—including the Green View Index (GVI), Sky View Index (SVI), built environment proportion, road proportion, and visual diversity (Entropy)—were extracted. Spearman correlation and multiple linear regression were applied to examine their effects on activity intensity. The results show that leisure activities are generally more active in the evening and on weekends, with social activities exhibiting the strongest temporal variation. Active activities remain relatively stable, passive activities show temporal dependence, and social activities display localized high-intensity clustering. Regression results reveal differentiated environmental responses: visual diversity has a stable positive effect on passive activities, active activities show weak associations with visual variables, and social activities are the most sensitive, with GVI, SVI, and built proportion showing significant negative effects, while visual diversity shows a significant positive effect. The social activity model also demonstrates the highest explanatory power (Adj. R² = 0.488). Overall, this study develops a street-view semantic segmentation-based method for quantifying waterfront visual environments, demonstrates the critical role of visual environmental composition in shaping activity patterns, and provides empirical support for the fine-grained and activity-oriented optimization of waterfront public spaces. Full article

Urban plazas in hot-humid climates face severe heat exposure risks due to high sky view factors and limited shading, yet conventional thermal mitigation strategies predominantly rely on plaza-wide performance metrics that misalign with actual pedestrian exposure patterns. This study proposes a pedestrian-oriented microclimate optimization framework that integrates agent-based pedestrian movement simulation (PedSim) with coupled CFD microclimate modeling to enhance outdoor thermal comfort precisely where people walk and congregate. A representative urban plaza (32,300 m²) in a hot-humid climate was analyzed under extreme summer design conditions. Three scenarios were systematically compared: (1) baseline configuration, (2) plaza-wide greening optimization (uniform distribution), and (3) pedestrian-oriented optimization guided by exposure-weighted movement hotspots. Microclimatic variables were simulated using urbanMicroclimateFoam (OpenFOAM), incorporating coupled airflow, heat/moisture transport, radiation, and vegetation modules. Thermal comfort was quantified using Mean Radiant Temperature (MRT) and the Universal Thermal Climate Index (UTCI) at both plaza-wide and pedestrian hotspot scales. Winter simulations were further conducted to assess seasonal trade-offs. Results demonstrate that under identical green coverage ratio (6.6%), the pedestrian-oriented strategy achieves substantially greater thermal comfort improvements in high-use areas. Compared to the baseline, hotspot MRT and UTCI were reduced by up to 5.0 °C and 3.0 °C, respectively, whereas the plaza-wide scheme yielded only marginal improvements (ΔUTCI < 1 °C). Notably, the pedestrian-oriented layout outperformed plaza-wide optimization within hotspots by 0.8 °C UTCI reduction without compromising winter thermal comfort, maintaining 100% thermally comfortable area ratios in both scenarios. This research reveals that the spatial configuration of vegetation is equally critical as coverage quantity for pedestrian thermal exposure. By explicitly linking tree placement to movement patterns, the proposed framework offers a human-centered, resource-efficient pathway for climate-responsive urban design, providing actionable insights for mitigating heat stress in densely populated open spaces without increasing green infrastructure costs. Full article

In response to environmental degradation and social inequities exacerbated by automobile-dependent urban sprawl, this study proposes a framework for dynamic delineation and vitality assessment of 15-min walkable neighborhoods, using Baohe District, Hefei, China as a case study. Static service catchments were constructed using POI and road network data, then refined using one week’s mobile phone signaling trajectories calibrated to actual walking behavior, yielding 143 validated living circles (out of 156 initially delineated). These circles are classified into five typologies: commercial-residential, industrial-residential, educational-residential, predominantly residential, and public-service-oriented. A dual-index system—Facility Vitality Index (FVI) and Population Vitality Index (PVI)—is developed and synthesized into a Composite Vitality Index (VI) through normalization and weighting. Results show that only 27.3% of living circles achieve high vitality in both dimensions, indicating widespread service–demand misalignment. Conversely, 61.5% exhibit low or very low vitality, forming a “vitality depression” around the urban periphery—a pattern of service poverty with significant socioeconomic implications. High-vitality circles cluster along the Binhu New District corridor, while low-vitality circles concentrate in industrial parks (e.g., Feinan Industrial Park) and transport hubs (e.g., Hefei South Railway Station). The historic core lacks micro-infrastructures, whereas new districts—despite high-standard amenities—suffer from weak pedestrian activity. To address these deficiencies, we propose a differentiated zoning strategy: retrofitting micro-infrastructures in legacy neighborhoods, applying Transit-Oriented Development (TOD) principles in new urban extensions, and integrating community-serving functions within industrial peripheries. This framework provides actionable protocols for data-informed governance of 15-min living circles. Full article

Background/Objectives: A floating hip injury, defined as an ipsilateral fracture of the pelvis or acetabulum combined with a femoral fracture, represents a rare and devastating musculoskeletal injury resulting from high-energy trauma. Although Müller type A floating hip injuries comprising an acetabular fracture with an ipsilateral femoral fracture are recognized for their clinical complexity, the long-term prognostic factors influencing functional outcomes remain poorly elucidated. This study aimed to identify independent prognostic factors associated with unsatisfactory long-term functional outcomes in patients with Müller type A floating hip injuries. Methods: A retrospective study was performed on 68 consecutive patients with Müller type A floating hip injuries who underwent surgical fixation at a single tertiary trauma center, with a minimum follow-up period of 5 years. Functional outcomes were assessed using the Majeed score, and patients were dichotomized into satisfactory (n = 48; 70.6%) and unsatisfactory (n = 20; 29.4%) outcome groups. Acetabular fractures were classified according to the Judet–Letournel system, and femoral fractures were classified by fracture level (proximal, shaft, or distal). Radiological outcomes were evaluated using Matta’s radiological grading system. Demographic, injury-specific, and treatment-related variables were compared between groups using the Mann–Whitney U test and chi-square test with Bonferroni correction. A multivariate binary logistic regression model was constructed to determine independent predictors of unsatisfactory outcomes. Results: The mean age was 37.15 ± 12.07 years, with a male predominance (67.6%). The predominant mechanism of injury was pedestrian struck by vehicle (54.4%), followed by motor vehicle collision (27.9%) and fall from height (17.6%); collectively, high-energy vehicular trauma accounted for 82.3% of cases. In the univariate analysis, transverse with posterior wall acetabular fracture pattern (p = 0.001), proximal femur fracture level (p = 0.001), associated lower extremity fractures (p = 0.001), nerve damage (p = 0.001), higher body mass index (BMI) (p = 0.001), and lower Matta’s radiological scores (p = 0.001) were significantly associated with unsatisfactory outcomes. Three independent predictors emerged in the multivariate logistic regression: BMI (OR = 1.50; 95% CI: 1.05–2.15; p = 0.025), the presence of associated lower extremity fractures (OR = 29.02; 95% CI: 2.83–297.67; p = 0.005), and Matta’s radiological score (OR = 0.06; 95% CI: 0.01–0.56; p = 0.014). The model yielded internal discriminatory metrics within the acceptable range (overall accuracy 89.7%, sensitivity 95.8%, specificity 75.0%, Nagelkerke R² = 0.757); however, given the limited events-per-variable ratio (~6.7) and the wide confidence intervals observed for some predictors, these internal performance estimates are likely optimistic due to potential overfitting, and the findings should be interpreted as exploratory pending external validation. Conclusions: Elevated BMI, the presence of associated ipsilateral lower extremity fractures, and poor quality of acetabular reduction, assessed via Matta’s radiological criteria, are independent determinants of unsatisfactory long-term functional outcomes in Müller type A floating hip injuries. These findings underscore the critical importance of achieving anatomical reduction in the acetabulum and highlight the compounding effect of additional ipsilateral limb injuries on patient prognosis. Full article

Existing studies of street-related emotional perception mainly rely on static scene evaluations, which cannot capture the cumulative effects of environmental exposure during continuous walking. To address this limitation, this study proposes a method for predicting pedestrian physiological responses in sequential audiovisual street environments. Four real-world walking routes were selected, with outbound and return directions treated as independent paths, yielding eight paths and 32 valid samples. EEG, ECG, sound pressure level, first-person video, and GPS data were synchronously collected to construct a 1 s multimodal time-series dataset. Pearson correlation, Kendall correlation, and mutual information analyses were used to examine linear, monotonic, and nonlinear relationships between environmental variables and physiological indicators, and the resulting weights were incorporated into a Long Short-Term Memory (LSTM) model for multi-step prediction. Visual elements and noise exposure were the main factors influencing physiological responses. Among the models, the mutual-information-weighted LSTM performed best, achieving an R² of 0.77 for heart rate variability (RMSSD), whereas prediction of the EEG ratio (β/α and θ/β) remained limited. An additional independent street sample outside the training set was then used to generate a dual-dimensional EEG-ECG physiological response map, demonstrating the model’s potential for identifying emotional risk segments and supporting street-level micro-renewal. Full article

This paper presents ANAID (Autonomous Naturalistic obstacle-Avoidance Interaction Dataset), a new multimodal dataset designed to support research on autonomous driving, particularly with regard to obstacle avoidance and naturalistic driver–vehicle interaction. Data were collected using a Hyundai Tucson Hybrid equipped with a Comma-3X autonomous-driving development kit, combining high-resolution front-facing video with detailed CAN-bus telemetry. The dataset comprises four data collection campaigns, each corresponding to a single continuous driving session, yielding a total of 208 videos and 240,014 synchronized frames. In addition to the video data, the dataset provides vehicle state measurements (speed, acceleration, steering, pedal positions, turn signals, etc.) and an additional annotation layer identifying evasive maneuvers derived from steering-related signals. Data were recorded across four driving campaigns on an urban circuit at Universidad Europea de Madrid, capturing diverse real-world scenarios such as roundabouts, intersections, pedestrian areas, and segments requiring obstacle avoidance. A multi-stage processing pipeline aligns telemetry and visual data, extracts frames at 20 FPS, and detects evasive maneuvers using threshold-based time-series analysis. ANAID provides a fully aligned and non-destructive representation of naturalistic driving behavior, enabling research on control prediction, driver modeling, anomaly detection, and human–autonomy interaction in realistic traffic conditions. Full article

Autonomous cleaning robots operating in semi-structured dynamic environments must execute task-oriented motions while safely interacting with surrounding agents. These agents include pedestrians, vehicles, and other robots. In such environments (e.g., interaction-rich campus environments), reliable self-decision-making requires anticipating the future motions of surrounding agents rather than relying solely on reactive obstacle avoidance. This paper presents a trajectory prediction-enabled self-decision-making framework for autonomous cleaning robots in campus environments. A learning-based multi-agent trajectory prediction model is trained offline using public benchmarks and real-world operational data to capture typical interaction patterns in corridor-following, edge-cleaning, and intersection scenarios. The predicted trajectories are then incorporated as forward-looking priors into the robot’s online decision-making and planning process, enabling prediction-aware yielding, detouring, and task continuation decisions. The proposed framework is evaluated using real-world data-driven scenario reconstruction on a high-fidelity simulation platform that incorporates realistic vehicle dynamics and heterogeneous traffic participants. This evaluation focuses on short-horizon prediction performance and its impact on downstream decision-making stability. The results show that integrating trajectory prediction into the decision-making loop leads to more stable motion behavior and fewer abrupt adjustments in interaction scenarios. Under short-term prediction horizons, the evaluation results show that the proposed model achieves ADERate and FDERate exceeding 90% under predefined error thresholds, while lane-change prediction accuracy remains around 79%. In addition, the robot maintains stable speed tracking with only minor fluctuations under medium-density traffic conditions. Full article

Pedestrians are amongst the most vulnerable road user groups. Efforts to enhance pedestrian safety have mainly focused on intersections and midblock crossings. This study investigated the effect of bus stop environments on pedestrian safety in Kumasi, an area with a high incidence of pedestrian fatalities in Ghana. Crashes within a 50 m radius of bus stops were extracted using a spatial join. The Negative Binomial regression model was applied to model pedestrian crashes around bus stops as a function of three distinct non-collinear independent variable groups: road design features, bus stop characteristics, and pedestrian exposure measures. Formal bus stops were associated with higher crash rates than informal ones. The presence of medians and crosswalks was associated with lower crash rates, whereas wider carriageways were associated with higher crash rates. Higher crashes were linked to passing pedestrians and waiting pedestrians, while crossing pedestrians were associated with reduced crashes. These findings suggest that the combined effects of infrastructure and behavioural factors influence pedestrian safety at bus stops. Prioritising low-cost safety treatments, such as guard-railed waiting areas, marked crosswalks, medians, and raised crossings, around bus stops will yield substantial safety benefits for resource-constrained contexts and advance sustainable urban mobility. Full article

As a typical biomimetic robotic system, quadruped robots replicate the flexible locomotion of quadruped mammals, outperforming wheeled robots in human-centered daily scenarios. To improve the social navigation adaptability of biomimetic quadruped robots in human–robot shared environments, this paper proposes a collision-aware orthogonal steering social force model (COSFM), an enhanced social force model that integrates collision prediction and social norms, inspired by human-like collision avoidance behaviors and social interaction rules. The model addresses key limitations of conventional social force models: delayed responses to dynamic pedestrians and inadequate consideration of pedestrians’ comfort zones. It introduces a time-to-collision prediction mechanism to mimic human predictive decision-making in dynamic social interactions, enhancing the robot’s anticipation of pedestrian motion intentions, and designs an orthogonal steering-based avoidance strategy for four typical human–robot interaction scenarios (head-on encounters, intersecting paths, active overtaking, passive yielding). This strategy replicates humans’ natural priority of lateral steering over abrupt deceleration or retreat, generating socially compliant trajectories aligned with human behavioral expectations. The proposed method is validated via simulation and real-world experiments on a Unitree Aliengo quadruped robot. Results show that the COSFM algorithm achieves a higher navigation success rate and better performance in path length, navigation time, and minimum human-robot distance than existing approaches, while its human-like lateral avoidance priority effectively preserves pedestrians’ psychological comfort zones, demonstrating robust social adaptability and great application potential for biomimetic legged robots. Full article

Patterns of footfall counts in urban environments show regularity at various spatial and temporal scales. In this work, we study a lightweight hierarchical approach in which forecasts use four lagged higher-level aggregates as predictors trained with simple CPU-only models. For a fair comparison, the baseline is expanded to use a horizon-matched lag window, so that the variants have access to the same maximum lookback in time. The study uses hourly pedestrian counts from 13 sensors on two shopping streets in Newcastle upon Tyne, aggregated across spatial and temporal levels. Combined spatial and temporal aggregate predictors reduced forecast error by adding information from higher aggregation levels without changing the base learner. The best-performing configuration was SHTH+CP, which combines spatial and temporal parent features with a spatio-temporal cross-parent, and yielded an average pooled 4.3% improvement in RMSE and 3.5% in MAE, with the largest gains at 12 h directional counts, where RMSE decreased by 6.7% and MAE by 11.4%. Full article

Mitigating heat stress in high-density historical districts remains a critical challenge in urban renewal due to complex morphological heterogeneity. Existing research often relies on isolated intervention measures, lacking systematic, multi-strategy assessments driven by high-precision spatial data. This study addresses this gap by establishing a quantitative framework that couples thermal infrared remote sensing with Computational Fluid Dynamics (CFD) to optimize microclimate responses in Beijing’s Liulichang Historic District. Remote sensing data were utilized to retrieve high-resolution Land Surface Temperature (LST), providing accurate thermal boundary conditions for micro-scale wind-thermal simulations. A baseline scenario (S0) and seven renewal strategies (S1–S7)—integrating varying configurations of greenery, water bodies, and permeable pavements—were evaluated using pedestrian-level comfort indices. Results reveal that single-factor interventions yield marginal improvements or thermodynamic trade-offs; specifically, adding greenery (S1) in narrow street canyons increased aerodynamic roughness, thereby obstructing ventilation and inducing localized warming. Conversely, composite strategies significantly enhanced microclimatic quality. The “greenery-water-permeable pavement” strategy (S4) achieved optimal synergistic effects, characterized by substantial cooling and spatial homogenization. Regression analysis identified water bodies as the dominant cooling driver, where a 10% increase in water coverage resulted in a temperature reduction of approximately 5.17 °C. Conversely, greenery alone showed no statistically significant cooling contribution (p > 0.05) without the synergistic presence of water or pavement modifications. This research suggests that urban renewal in high-temperature zones (>36 °C) should prioritize composite cooling networks. Furthermore, vegetation layouts near wind corridors must be precisely regulated to prevent ventilation degradation. These findings provide a scientific basis for the climate-adaptive sustainable regeneration of culturally significant, high-density urban blocks. Full article

Pedestrian trajectory prediction remains a challenging task, particularly in long-tail scenarios where goal distributions are sparse and inter-agent behaviors are uncertain. In this work, we propose to disentangle the trajectory prediction task into two complementary components: interaction modeling and intention modeling. For interaction modeling, we introduce an adaptive meta-strategy that proactively extracts latent and rare-yet-critical interaction patterns often overlooked by conventional trajectory-only approaches. For intention modeling, we propose Continuous Waypoint Slot-Driven Prototypical Contrastive Learning (PCL). It adapts prototype learning to the multi-modal reality where conventional PCL fails to model diverse and continuous goal distributions. Capitalizing on the complementary strengths of both components, we orchestrate a unified frequency-based fusion module that seamlessly integrates interaction and intention modeling, yielding enhanced overall prediction accuracy. In particular, our method is model-agnostic and can be seamlessly incorporated into a wide range of existing prediction frameworks. Extensive experiments on several datasets demonstrate that our approach not only achieves consistent performance gains in standard settings, but also significantly alleviates degradation on hard or long-tail trajectory samples. Full article

As population aging accelerates, age-related declines in visual sensitivity and attentional control make older drivers more vulnerable to suboptimal in-vehicle interface designs. Head-up displays (HUDs) are intended to reduce gaze shifts by overlaying information within the forward field of view, yet empirical evidence remains limited on how specific HUD presentation strategies reshape older drivers’ visual attention allocation. Grounded in theories of visual attention and cognitive load, this study systematically investigates three design variables that are increasingly common in contemporary HUDs (including AR-HUDs): (1) dynamic versus static navigation cues, (2) pedestrian warning strategies under different lighting conditions, and (3) the spatial placement of high-priority information. We first conducted a formative user study to define variables and operationalizations, and then carried out three within-subject driving-simulator experiments using controlled HUD stimuli and eye tracking. Objective gaze measures (e.g., fixation count, total fixation duration, and time to first fixation) were combined with subjective preference ratings to characterize attentional capture, search efficiency, and potential attentional costs. Findings reveal a robust trade-off: continuously changing navigation cues enhance attentional capture but can also increase attentional “stickiness,” unnecessarily consuming older drivers’ limited attentional resources. In pedestrian hazard tasks, real-time overlay warnings that were spatially aligned with the hazard significantly improved visual localization under low-light conditions, outperforming early warnings and multi-stage strategies. Across tasks and layout conditions, the central HUD region showed a stable attentional advantage—placing critical information centrally elicited greater visual attention and stronger subjective preference. These results provide mechanistic evidence for how HUD parameters modulate older drivers’ attention and yield actionable implications for prioritization, temporal pacing of dynamic navigation cues, and a “center-first” layout strategy to guide age-friendly HUD design. Full article

Urban sustainable mobility requires understanding how people travel, which modes they use, and what impacts these choices generate. This study proposes a smart mobility analytics framework that integrates GPS traces, dynamic traffic variables, and machine learning to infer transport modes and sustainability metrics in Cuenca, Ecuador. Geospatial and kinematic data were collected at 1 Hz from 50 participants over four working weeks, yielding 8.99 million samples across five modes: walking, cycling, tram, bus, and private vehicles. A compact subset of physical and spatial predictors, derived from speed, acceleration, jerk, longitudinal forces, and distance to public transport routes, was selected using the Football Optimization Algorithm. A classification tree trained with a 70/15/15 train–validation–test split achieved an overall accuracy of 84.2%, with class precisions of about 99% for pedestrian and bicycle, 93% for tram, 76% for private vehicles, and 64% for bus. The classified trajectories show that walking and cycling account for approximately 65% of total travel time but only 2% of total distance and 1.7% of CO₂ emissions, whereas motorized modes generate more than 98% of emissions. Buses contribute nearly four times more CO₂ than private vehicles, despite carrying a larger passenger volume. The proposed framework delivers detailed, policy-relevant indicators to support low-carbon urban transport strategies. Full article

Urban waterfront walkways are everyday public built environments where people commonly engage in slow walking, yet evidence remains limited that links what pedestrians see to immediate psychophysiological responses under controlled first-person dynamic exposure. To address this gap, we developed a fixed-speed, fixed-duration VR walk-through model using real-world 360° panoramic video and quantified scene visual composition via computer vision-based image analysis. Based on the visible shares of key components (greenery, water, sky, hardscape, and built structures), clips were grouped into four interpretable waterfront typologies: Vegetation-Enclosed, Built-Dominant, Hardscape-Plaza, and Blue-Open. Fifty healthy adults completed within-subject VR exposures to the four typologies (50 s per clip), while multimodal physiological signals and brief affect and landscape ratings were collected before and after exposure. The results showed that scenes with more water and vegetation coverage, along with expansive views, were associated with promoted autonomic nervous system calming responses, whereas scenes with fewer natural elements and higher built structure density were more likely to induce tension responses. Negative emotions decreased significantly across all four scene experiences, though artificial scenes concurrently exhibited emotional improvement alongside physiological tension. Overall, brief first-person dynamic VR exposure can yield immediate emotional benefits, and waterfront designs combining water proximity, abundant greenery, and expansive vistas may maximize short-term restorative potential, offering quantitative targets for health-supportive planning and retrofitting. Full article