Search Results (301)

Drive-through establishments are becoming increasingly prominent in urban transport systems; however, their impacts on traffic generation, spatial form, and sustainability remain insufficiently understood. Conventional trip generation manuals often rely on static predictors, such as gross floor area, which can misrepresent demand in high-turnover, convenience-driven contexts and fail to capture operational, behavioral, and environmental effects. This knowledge gap underscores the need for an integrated framework that supports both effective planning and congestion mitigation, particularly in cities experiencing rapid motorization and shifting mobility behaviors. This study investigated the evolving dynamics in trip generation associated with drive-through services and their influence on urban development patterns. A mixed-methods approach was employed, combining a systematic literature review, meta-analysis of queue data, cross-comparison of trip generation rates from international and Asian datasets, and case-based scenario modeling. The results revealed that drive-throughs intensify high-frequency, impulse-driven vehicle trips, thereby causing congestion, reducing pedestrian accessibility, and reinforcing auto-centric land use configurations, while also enhancing consumer convenience and commercial efficiency. This study contributes to the literature by synthesizing inconsistencies in regional datasets; introducing a systems-based framework that integrates structural, behavioral, and environmental determinants with road network topology; and outlining policy applications that align trip generation with zoning, design standards, and sustainable infrastructure planning. Full article

Indoor navigation (IN) systems are increasingly essential in environments where GPS signals are unreliable, such as hospitals, airports, and large public buildings. This study explores a smartphone-based approach to indoor positioning that leverages inertial sensor data for accurate step detection and counting, which are fundamental components of pedestrian dead reckoning. A long short-term memory (LSTM) network was trained to recognize step patterns across a variety of indoor movement scenarios. The generalized model achieved an average step detection accuracy of 93%, while scenario-specific models tailored to particular movement types such as turning, stair use, or interrupted walking achieved up to 96% accuracy. The results demonstrate that incorporating activity-specific training improves performance, particularly under complex motion conditions. Challenges such as false positives from abrupt stops and non-walking activities were reduced through model specialization. Although the system performed well offline, real-time deployment on mobile devices requires further optimization to address latency constraints. The proposed approach contributes to the development of accessible and cost-effective indoor navigation systems using widely available smartphone hardware and offers a foundation for future improvements in real-time pedestrian tracking and localization. Full article

Building-integrated photovoltaic (BIPV) systems play a pivotal role in advancing low-carbon urban transformation. However, replacing conventional building envelope materials with photovoltaic (PV) panels modifies heat transfer processes and airflow patterns, potentially influencing urban environmental quality. This study examines the impacts of BIPV on building energy efficiency, PV system performance, and street canyon micro-climates, including airflow, temperature distribution, and pollutant dispersion, under perpendicular wind speeds ranging from 0.5 to 4 m/s, across three installation configurations and three installation positions. Results indicate that rooftop PV panels outperform facade-mounted systems in power generation. Ventilated PV configurations achieve optimal energy production and thermal insulation, thereby reducing building cooling loads and associated electricity consumption. Moreover, BIPV installations enhance street canyon ventilation, improving pollutant removal rates: ventilation rates increased by 1.43 times (rooftop), 3.02 times (leeward facade), and 2.09 times (windward facade) at 0.5 m/s. Correspondingly, canyon-averaged pollutant concentrations decreased by 30.1%, 87.7%, and 85.9%, respectively. However, the introduction of facade PV panels locally reduces pedestrian thermal comfort, particularly under low wind conditions, but this negative effect is significantly alleviated with increasing wind speed. To quantitatively evaluate BIPV-induced micro-climatic impacts, this study introduces the Pollutant-Weighted Air Exchange Rate (PACH)—a metric that weights the air exchange rate by pollutant concentration—providing a more precise indicator for evaluating micro-environmental changes. These findings offer quantitative evidence to guide urban-scale BIPV deployment, supporting the integration of renewable energy systems into sustainable urban design. Full article

Aim: Older adult pedestrians are at greater risk of severe injuries than younger pedestrians due to gradual physical changes and coexisting medical conditions. This leads to longer hospital stays, increased mortality risk, and higher inpatient costs. Focusing on the aging population, this study explores the characteristics and injury profiles of pedestrian crossing accidents in the capital city of Bern, Switzerland. Methods: Our retrospective cohort study comprised adult patients admitted to our ED between 1 January 2013 and 31 December 2023, as crossing (or zebra crossing)-related pedestrian victims. Two cohorts were formed on the basis of age < 65 and ≥65 years and compared according to the setting of the accident, type, pattern of the injury, and clinical outcomes (short-term mortality, ICU/hospital length of stay). Results: Of a total of 124 patients, 31.5% (n = 39) of patients were elderly (65+ group). In contrast to the younger patients, the aging population was predominantly admitted as inpatients (64.1% vs. 35.3%, p = 0.001) and was hospitalised in the intensive care unit (20.5% vs. 6%, p = 0.020). Older patients were more likely to be polytraumatised (41% vs. 11.8%, p = 0.001) and to have been tossed or hurled than patients under 65 years (75% vs. 47.3%, p = 0.016). Fractures of the upper extremities (17.9% vs. 4.7%, p = 0.016), pelvis (30.8% vs. 9.4%, p = 0.003), and thoracic spine (12.8% vs. 2.4%, p = 0.019) were significantly more common in the elderly population. Intracranial haemorrhage (35.9% vs. 17.6%, p = 0.026), abdominal trauma (17.9% vs. 5.9%, p = 0.035), and relevant vessel damage (30.8% vs. 3.5%, p < 0.001) were also significantly higher in geriatric patients. Trauma indices were slightly more increased in the older population than in the younger group (ISS; p = 0.004 and AIS > 2 of chest and thoracic spine; abdomen, pelvic contents, and lumbar spine; extremities & bony pelvis p < 0.05). The 65+ group had a longer length of hospital stay (p = 0.001) and ICU stay (p = 0.002). A hospital stay longer than 7 days was also significantly more common in elderly individuals (p = 0.007). In-hospital (15.4% vs. 1.2%, p = 0.001) and 30-day mortality (17.9% vs. 1.2%, p < 0.001) were significantly higher in patients over 65 years of age. Conclusion: In our study, the impact of pedestrian crossing accidents was more severe in the elderly, as indicated by the severity of injuries, hospitalisation rate, longer length of hospital and ICU stays, and higher mortality rates. These findings underline the importance of developing tailored strategies to reduce crosswalk accidents and to optimise management approaches for these vulnerable patients. Full article

This study examines the impact of canopy structural characteristics on pedestrians’ visual perception and psychophysiological responses along four roads in the subtropical city of Guangzhou: Huadi Avenue, Jixiang Road, Yuejiang Middle Road, and Huan Dao Road. A Canopy Structural Index (CSI) was innovatively developed by integrating tree height, crown width, diffuse non-interceptance, and leaf area index, establishing a five-tier quantitative grading system. The study used multimodal data fusion techniques combined with heart rate variability (HRV) analysis and eye-tracking experiments to quantitatively decipher the patterns of autonomic nervous regulation and visual attention allocation under different levels of CSI. The results demonstrate that CSI levels are significantly correlated with psychological relaxation states: as CSI levels increase, time-domain HRV metrics (SDNN and RMSSD) rise by 15%–43%, while the frequency-domain metric (LF/HF) decreases by 31%, indicating enhanced parasympathetic activity and a transition from stress to relaxation. Concurrently, the allocation of visual attention toward canopies intensifies. The proportion of fixation duration increases to nearly 50%, and the duration of the first fixation extends by 0.3–0.8 s. The study proposes CSI ≤ 0.15 as an optimization threshold, offering scientific guidance for designing and pruning subtropical urban street tree canopies. Full article

As cities strive for human-centered and fine-tuned development, Agent-Based Modeling (ABM) has emerged as a powerful tool for simulating pedestrian behavior and optimizing walkable neighborhood design. This study presents a comparative bibliometric analysis of ABM applications in block-scale walkability research from 2015 to 2024, drawing on both Chinese- and English-language literature. Using visualization tools such as VOSviewer, the analysis reveals divergences in national trajectories, methodological approaches, and institutional logics. Chinese research demonstrates a policy-driven growth pattern, particularly following the introduction of the “15-Minute Community Life Circle” initiative, with an emphasis on neighborhood renewal, age-friendly design, and transit-oriented planning. In contrast, international studies show a steady output driven by technological innovation, integrating methods such as deep learning, semantic segmentation, and behavioral simulation to address climate resilience, equity, and mobility complexity. The study also classifies ABM applications into five key application domains, highlighting how Chinese and international studies differ in focus, data inputs, and implementation strategies. Despite these differences, both research streams recognize the value of ABM in transport planning, public health, and low-carbon urbanism. Key challenges identified include data scarcity, algorithmic limitations, and ethical concerns. The study concludes with future research directions, including multimodal data fusion, integration with extended reality, and the development of privacy-aware, cross-cultural modeling standards. These findings reinforce ABM’s potential as a smart urban simulation tool for advancing adaptive, human-centered, and sustainable neighborhood planning. Full article

This paper presents a novel adversarial Convolutional Neural Network (ConvNet) method for generating adversarial perturbations in 3D point clouds, enabling gradient-free robustness evaluation of object detection systems at inference time. Unlike existing iterative gradient methods, our approach embeds the ConvNet directly into the detection pipeline at the voxel feature level. The ConvNet is trained to maximize detection loss while maintaining perturbations within sensor error bounds through multi-component loss constraints (intensity, bias, and imbalance terms). Evaluation on a Sparsely Embedded Convolutional Detection (SECOND) detector with the KITTI dataset shows 8% overall mean Average Precision (mAP) degradation, while CenterPoint on NuScenes exhibits 24% weighted mAP reduction across 10 object classes. Analysis reveals an inverse relationship between object size and adversarial vulnerability: smaller objects (pedestrians: 13%, cyclists: 14%) show higher vulnerability compared to larger vehicles (cars: 0.2%) on KITTI, with similar patterns on NuScenes, where barriers (68%) and pedestrians (32%) are most affected. Despite perturbations remaining within typical sensor error margins (mean L2 norm of 0.09% for KITTI, 0.05% for NuScenes, corresponding to 0.9–2.6 cm at typical urban distances), substantial detection failures occur. The key novelty is training a ConvNet to learn effective adversarial perturbations during a one-time training phase and then using the trained network for gradient-free robustness evaluation during inference, requiring only a forward pass through the ConvNet (1.2–2.0 ms overhead) instead of iterative gradient computation, making continuous vulnerability monitoring practical for autonomous driving safety assessment. Full article

This paper proposes and evaluates two neural network-based approaches for object classification in automotive radar systems, comparing the performance impact of grid search and genetic algorithm (GA) hyperparameter optimization strategies. The task involves classifying cars, pedestrians, and cyclists using radar-derived features. The grid search–optimized model employs a compact architecture with two hidden layers and 10 neurons per layer, leveraging kinematic correlations and motion descriptors to achieve mean accuracies of 90.06% (validation) and 90.00% (test). In contrast, the GA-optimized model adopts a deeper architecture with nine hidden layers and 30 neurons per layer, integrating an expanded feature set that includes object dimensions, signal-to-noise ratio (SNR), radar cross-section (RCS), and Kalman filter–based motion descriptors, resulting in substantially higher performance at approximately 97.40% mean accuracy on both validation and test datasets. Principal Component Analysis (PCA) and SHapley Additive exPlanations (SHAP) highlight the enhanced discriminative power of the new set of features, while parallelized GA execution enables efficient exploration of a broader hyperparameter space. Although currently optimized for urban traffic scenarios, the proposed approach can be extended to highway and extra-urban environments through targeted dataset expansion and developing additional features that are less sensitive to object kinematics, thereby improving robustness across diverse motion patterns and operational contexts. Full article

Over the past decade, researchers have advanced traffic monitoring using surveillance cameras, unmanned aerial vehicles (UAVs), loop detectors, LiDAR, microwave sensors, and sensor fusion. These technologies effectively detect and track vehicles, enabling robust safety assessments. However, pedestrian detection remains challenging due to diverse motion patterns, varying clothing colors, occlusions, and positional differences. This study introduces an innovative approach that integrates multiple surveillance cameras at signalized intersections, regardless of their types or resolutions. Two distinct convolutional neural network (CNN)-based detection algorithms accurately track road users across multiple views. The resulting trajectories undergo analysis, smoothing, and integration, enabling detailed traffic scene reconstruction and precise identification of vehicle–pedestrian conflicts. The proposed framework achieved 97.73% detection precision and an average intersection over union (IoU) of 0.912 for pedestrians, compared to 68.36% and 0.743 with a single camera. For vehicles, it achieved 98.2% detection precision and an average IoU of 0.955, versus 58.78% and 0.516 with a single camera. These findings highlight significant improvements in detecting and analyzing traffic conflicts, enhancing the identification and mitigation of potential hazards. Full article

Historic districts face persistent challenges balancing heritage preservation and urban vitality due to fragmented governance and static conservation. This study develops a multi-source data-driven evaluation system coupling spatial quality and urban vitality, focusing on China’s Republican-era historic districts with Nanjing’s Yihe Road as a case study. Integrating field surveys and big data (street view imagery, POI data, heatmaps), we quantitatively assess environmental quality and vitality. Key findings reveal a distinct spatial pattern: “high-quality concentration internally” and “high-vitality concentration externally,” where core areas exhibit functional homogenization and low vitality, while peripheries show high pedestrian activity but lack spatial coherence. Clustering analysis categorizes streets into four types based on quality and vitality levels, highlighting contradictions between static conservation and adaptive reuse. The study deepens understanding of spatial differentiation mechanisms and reveals universal patterns for sustainable development strategies. A multi-tiered governance strategy is proposed: urban-level flexible governance harmonizes cross-departmental policies via adaptive planning, district-level differentiated governance activates spatial value through functional reorganization, and street-level fine-grained management prioritizes incremental micro-renewal. The research underscores the critical need to balance heritage preservation with contemporary functional demands during urban renewal, offering a practical framework to resolve spatial conflicts and reconcile conservation with regeneration. Full article

This study presents a computational fluid dynamics (CFD) modeling framework to simulate two-phase (liquid and gas) chemical agent dispersion in urban canyons. The model was validated against wind tunnel experiments, meeting statistical criteria. To assess geometric impacts on flow and dispersion, the model was applied to four idealized canyon types—Cube (CB), Short (SH), Medium (MD), and Long (LN). Results revealed that increasing building length reduced the horizontal extent but enhanced the vertical extent of wake zones, weakened roof-level wind speeds, and shifted the reattachment point farther downstream. For liquid-phase sulfur mustard (HD), CB showed active canyon exchange and rapid penetration to pedestrian level. SH and MD exhibited more gradual infiltration with weaker variability due to fewer streamwise streets. LN had no streamwise street; transport was primarily driven by canyon vortices and showed slower penetration. Gaseous HD exhibited similar patterns to liquid HD but attained higher in-canyon concentrations due to differences in evaporation and dry deposition effects, indicating prolonged persistence. Overall, canyon geometry strongly influenced pollutant retention and variability. These findings suggest that the model can support chemical hazard assessment and early response planning that considers building geometry. Full article

While autonomous vehicles (AVs) are increasingly integrated into urban mobility, little is known about how digital capability shapes elderly pedestrians’ willingness to share roads with these technologies. This is especially true in the absence of explicit vehicle–pedestrian communication mechanisms. To address this gap, we combine the Theory of Planned Behavior (TPB) with the Pedestrian Behavior Questionnaire (PBQ) and segment elderly pedestrians using Latent Class Analysis (LCA). A sample of 750 older adults in Wuhan, China, was divided into two latent groups: digitally disengaged (70.8%) and digitally engaged (29.2%). Classification was based on four indicators: smart device usage, online social interaction, online entertainment, and online economic behavior. We then applied ordered logit models to estimate group-specific determinants of AV road-sharing acceptance. Results reveal clear heterogeneity across digital capability levels. For digitally disengaged seniors, positive pedestrian behaviors significantly increased willingness (β = 0.316, p = 0.001). Prior accident experience reduced willingness (0 accident: β = 0.435, p = 0.021; 1–2 accidents: β = −0.518, p = 0.012). For digitally engaged seniors, perceived behavioral control showed a marginally positive effect (β = 0.353, p = 0.066). Errors had a significant positive effect (β = 0.540, p = 0.009). Positive behaviors had a significant negative effect (β = −0.414, p = 0.007). These patterns indicate that digital capability not only modulates the strength of TPB pathways but also reshapes behavior–intention linkages captured by PBQ dimensions. Methodologically, the study contributes an integrated TPB–PBQ–LCA–OLM framework. This framework identifies digital capability as a critical moderator of AV acceptance among elderly pedestrians. Practically, the findings suggest differentiated strategies. For digitally disengaged users, interventions should build digital literacy and reinforce safe walking norms. For digitally engaged users, strategies should prioritize transparent AV intent signaling and features that enhance perceived control. Full article

The study investigates the disconnect between formal urban planning standards and experiential walkability outcomes in Viranşehir, a planned neighborhood in Mersin, Türkiye. Although the area complies with national regulations on the provision of public services, it exhibits systemic limitations, including car-oriented street layouts, fragmented pedestrian networks, and underutilized public spaces. Employing a mixed-methods case study, the research integrates archival sources (aerial imagery, zoning plans, satellite data) with field observations to assess pedestrian environments. A light coding of sidewalk continuity, crossings, and edge conditions indicates that many streets are bounded by extensive inactive walls, protected crossings are absent along critical routes such as the school–park axis, and sidewalks are frequently narrow, obstructed, or discontinuous. These built-form features undermine safety, comfort, and social interaction despite formal regulatory compliance. The findings demonstrate how grid-pattern street systems prioritize vehicular mobility, while gated developments restrict permeability and diminish everyday encounters. In response, the study proposes a hierarchy of interventions: immediate measures such as school streets, protected crossings, and traffic calming, followed by medium- to long-term strategies including shaded seating, sidewalk widening, and participatory design guidelines. By linking statutory standards with lived experience, the paper conceptualizes walkability not only as a technical planning requirement but also as a socio-cultural right, offering transferable insights for the creation of more inclusive urban environments. Full article

This study investigates the severity of injuries sustained by over seven million participants involved in road traffic incidents in Poland between 2015 and 2024, with a view to supporting sustainable mobility and the United Nations Sustainable Development Goals. Road safety is a crucial dimension of sustainable development, directly linked to public health, urban liveability, and the socio-economic costs of transportation systems. Using a harmonised participant-level dataset, this research identifies key demographic, behavioural, and environmental factors associated with injury outcomes. A novel five-level injury severity variable was developed by integrating inconsistent records on fatalities and injuries. Descriptive analyses revealed clear seasonal and weekly patterns, as well as substantial differences by participant type and driving licence status. Pedestrians and passengers faced the highest risk, with fatality rates more than five times higher than those of drivers. An XGBoost classifier was trained to predict injury severity, and SHAP analysis was applied to interpret the model’s outputs at the feature level. Participant role emerged as the most important predictor, followed by driving licence status, vehicle type, lighting conditions, and road geometry. These findings provide actionable insights for sustainable road safety interventions, including stronger protection for pedestrians and passengers, stricter enforcement against unlicensed driving, and infrastructural improvements such as better lighting and safer road design. By combining machine learning with interpretability tools, this study offers an analytical framework that can inform evidence-based policies aimed at reducing crash-related harm and advancing sustainable transport development. Full article

This article focuses on modeling counterflows within pedestrian social groups in a corridor using the kinetic theory approach, specifically when two social groups move in opposite directions. The term social group refers to a set of pedestrians with established social relationships who stay as close as possible to one another and share a common goal or destination, such as friends or family. The model accounts for interactions both within the same social group and between pedestrians from different social groups. Numerical simulations based on a Monte Carlo particle method are performed. A key criterion for evaluating simulation models is their ability to reproduce empirically observed collective motion patterns. One of the most significant emergent behaviors in bidirectional pedestrian flows is lane formation. To analyze this phenomenon, we employ Yamori’s band index to quantify the evolution of lane structures. Full article