Search Results (395)

This article presents a geospatial model for identifying and assessing the risk of hazardous locations in the road network, developed to predict traffic safety hazards in areas with complex infrastructure where traditional methods, such as the Highway Safety Manual, are insufficient. The objective of the study was to develop a model that classifies road segments into five risk categories based on environmental and infrastructural characteristics, without using accident or traffic volume data. The model accounts for speed limits, road geometry, and the proximity of facilities that generate pedestrian traffic (schools, preschools, stores) and infrastructure elements (crosswalks, intersections). A hybrid approach was used, combining proprietary methods for determining distances from objects: vector-based (geodetic distance), route-based (road graph), and geometric (classification of a road segment’s shape), using QGIS, OpenStreetMap, and custom Python scripts. The results enabled assigning a risk category to each road segment, and validation was performed by comparing them with the locations of actual accidents resulting in serious injuries or fatalities. The developed model for identifying hazardous locations is a scalable tool that supports sensor-network-based area-based speed control systems, infrastructure planning, and safety management in regions with diverse road networks. Full article

Rapid urbanization and increasing traffic density have increased the crash risk of vulnerable road users (VRUs), particularly pedestrians and cyclists. Meanwhile, the conventional statistical models have difficulties in capturing the nonlinear and complex nature of crash data, limiting their safety analysis effectiveness. This study utilized advanced Gradient Boosting machine learning and integrated it with SHapley Additive exPlanations (SHAP) using five years of crash data from Michigan, USA, employing a two-tiered modeling design consisting of a 4-class joint structure and binary subset frameworks. Rigorously evaluated using 10-fold stratified cross-validation to predict crash severity for VRUs, the CatBoost model had better predictive performance (AUC = 0.917) than LightGBM, Random Forest and the traditional Logistic Regression models. The analysis further indicated that prior crash actions, particularly risky crossing behaviors, are the most significant determinants of injury severity for both user groups. However, the pedestrian crash severity is strongly associated with lighting conditions and speed limits, while cyclist crash severity is more heavily influenced by intersection involvement and roadway geometry. Moreover, SHAP interaction analysis showed that the speed effect on severity significantly increases when it interacts with hazardous actions or poor visibility. The findings provide a critical insight into the implementation of effective measures and infrastructure improvements to increase the safety of VRUs. Full article

Understanding the built environment is essential for explaining road safety outcomes, particularly through individuals’ perceptions of their surroundings, which are linked to perceived travel risk and play an important role in pedestrian decision-making. However, the heterogeneous effects of the built environment across contexts remain insufficiently understood and require further investigation. Thus, this study aims to investigate how perceived built environment attributes are associated with perceived travel risk and whether these associations differ across urban contexts, with specific variation in land-use contexts. Data were collected using questionnaires administered at pedestrian crossings in Bangkok, and ordinal regression was employed for the analysis. The results indicate that some factors are consistently significant across contexts, while others show significance in specific contexts. In particular, land use mix as diversity and traffic speed are among the variables that are consistently significant across multiple contexts. Overall, the findings suggest variation in the patterns of association between built environment attributes and perceived travel risk across contexts. These results provide useful insights for urban and transport planners regarding the role of environmental design and allocation in promoting safer pedestrian environments, thereby contributing to the development of a sustainable transport system by enhancing pedestrian safety and prioritizing pedestrians in planning. Full article

Pedestrian safety at urban intersections is influenced by complex interactions among environmental conditions, social cues, and boundedly rational behavioral responses, making intervention planning under uncertainty particularly challenging. Existing studies have largely emphasized descriptive analysis or predictive modeling, with limited attention to prescriptive decision support under data scarcity. This study proposes a Bayesian-network-based robust optimization framework for pedestrian safety planning, complemented by a large language model (LLM)-assisted parameter-elicitation process. We first construct a three-layer Bayesian network based on the stimulus–organism–response paradigm to represent the propagation of risk from environmental cues through latent psychological mechanisms to behavioral violations and accident risk. To support model initialization when site-specific behavioral data are limited, we introduce an LLM-assisted elicitation protocol that maps literature-based qualitative evidence to intervention mechanisms, effect directions, and qualitative strength classes. Numerical parameter ranges are subsequently assigned through explicit mapping rules rather than generated directly by the LLM. We then formulate a bi-objective robust optimization model that distinguishes between physical interventions, which alter root-node distributions, and cognitive interventions, which modify conditional probability tables. Using the $ϵ$-constraint method, the framework generates Pareto-optimal intervention portfolios and evaluates cost–risk trade-offs under behavioral uncertainty and adverse operating scenarios. Full article

Construction dust is a major contributor to urban inhalable particulate matter (PM₁₀) pollution, posing severe respiratory and cardiovascular health risks to construction workers and nearby residents, severely undermining urban environmental sustainability. Construction fences are widely adopted as a primary dust mitigation measure, yet their underlying dispersion mechanisms and comprehensive impacts on vertical air quality remain poorly understood due to the limitations of traditional field monitoring and empirical models, creating critical barriers to site-level pollution control and long-term urban sustainability. In this study, a reliable computational fluid dynamics (CFD) method was developed to investigate the spatial distribution of construction dust and quantify the dust suppression performance of fences with heights ranging from 0 to 3 m. Three mainstream k-ε turbulence models (Standard, RNG, and Realizable) were evaluated using on-site measurement data, and the RNG k-ε model was found to provide the best agreement with field observations, with statistical metrics of q = 1, FB = 0.052, and NMSE = 0.028. The results show that construction fences effectively reduce dust dispersion into the surrounding environment, particularly in the pedestrian breathing zone (z < 1.5 m). Increasing the fence height from 1.5 m to 3 m improves the breathing-zone dust reduction rate from 39% to 55%, with the most significant mitigation effect observed within 50 m downwind of the fence. However, a critical dual effect was identified: while fences suppress near-ground pollution, they induce strong upward airflow and turbulence, leading to elevated dust concentrations in the upper part of the near-ground region (z = 1.5–9 m), a phenomenon absent in the no-fence scenario. These findings provide practical implications for urban construction site management, suggesting that fence height and configuration should be carefully designed not only to reduce pedestrian-level exposure but also to avoid unintended pollutant accumulation aloft, thereby improving overall air quality control strategies and delivering balanced, long-term environmental sustainability at construction sites. Full article

This study presents a comprehensive multidimensional analysis of pedestrian accidents in Amman, Jordan, from 2014 to 2023. By integrating spatial, temporal, and statistical techniques, the research identifies critical risk patterns to inform evidence-based safety interventions. Characterizing a decade-long database of 14,821 cases, the study utilizes radar graphs, Kernel Density Estimation (KDE), and DBSCAN cluster analysis to delineate high-risk zones and temporal peaks. Temporal findings indicate that Thursdays recorded the highest accident frequency (2382 cases), with peak occurrences between 17:00 and 23:00. Spatial clustering identified five significant high-risk zones, with Central Amman emerging as the primary critical area. The study’s novelty lies in being the first in the Jordanian context to bridge accident frequency with severity mechanisms by integrating advanced spatial clustering and KDE with a robust Ordered Logit Model. Severity analysis reveals that while 59.34% of incidents resulted in minimal injuries, fatalities accounted for 5.02%. The model demonstrates that injury outcomes are systematically associated with traffic dynamics and behavior rather than environmental factors. Speed-related driver error was identified as the strongest predictor of severe outcomes (OR = 81.3). Significant dependencies were confirmed between vehicle category and road type (χ² = 2182.20, p < 0.001), lighting and road surface (χ² = 76.21, p < 0.001), and vehicle type and lighting (χ² = 148.52, p < 0.001). The study proposes a multi-layered framework combining site-specific nodal improvements with corridor-level strategies to enhance urban safety in Amman City. Full article

The rapid surge of Electric Two-Wheelers (E2Ws) in high-density urban villages imposes severe strain on low-voltage residential distribution networks. Unlike formal Electric Vehicles, E2W charging is decentralized and highly constrained by short pedestrian walking thresholds, frequently forcing users to adopt non-compliant “fly-wire charging” when public facilities are scarce. Traditional top-down load models fail to capture these localized, micro-behavioral single-phase grid impacts. To address this deficit, this study proposes a GIS-integrated “Digital Sandbox” simulation framework that projects individual behavioral mutations directly onto feeder networks via a hyper-granular “particle tracking” mechanism, treating each E2W as an autonomous agent. As a case study focused on a representative urban-village area, we validate the model using field data from the site. A 30-day simulation reveals that unmanaged fly-wire charging generates a peak load of 17.64 kW (nearly double the public station peak) and accounts for 38.9% of aggregate energy consumption—concentrated within the top 10 buildings and coinciding with evening peaks, inducing severe phase imbalance. While the numerical results are case-specific, the framework itself is transferable to other service areas through re-calibration against local data. This foundational digital twin blueprint shifts E2W planning from guesswork to particle-level risk prediction. Full article

Pedestrian Spatial Experience PSE in urban spaces is a multi-faceted topic that requires the thematization of assessment methods due to their fragmentation across studies. Accordingly, this systematic review followed an inductive approach to define a framework of PSE assessment themes reflecting their evaluation in public and university campus spaces. This systematic review included open-access, accessible, peer-reviewed sources based on assessment-focused English research that followed defined frameworks on the effects of urban environments on adult PSE. Studies were excluded if they focused on non-pedestrians or vulnerable user groups, examined non-pedestrian-scale contexts, explored pedestrian experience in virtual environments, assessed interior spaces, lacked a structured attribute-based assessment framework, were review articles, did not specify how urban environments shape pedestrian experience, investigated non-urban or rural areas, or examined urban settings without clearly defined street or square infrastructure. The review relied on querying PSE-related bibliography from the Scopus and Web of Science databases on 12 October 2025; results were processed through a screening procedure according to the inclusion and exclusion criteria. The final sets of sources reviewed included 83 and 24 sources related to PSE assessment in public and university campus spaces, respectively. Risk of Bias (RoB) tools included the Joanna Briggs Institute (JBI) tool for cross-sectional studies, tailored for urban spatial studies, and the Prediction model Risk Of Bias Assessment Tool (PROBAST+AI), tailored for ABM studies. Using a data extraction sheet and codebook to identify the prominent codes in the included sources, in addition to reviewing frequent words and the methods of the included sources, clarified the main conceptual framework of PSE assessment themes. The thematic categorization of PSE studies was followed by analyses of the frequencies of the themes, the prevalence of themes across countries and cities, and the theoretical explorations within the themes over the years in both reviewed contexts. Subsequently, synthesizing both sets clarified the interrelations between themes, methods, and tools as an attempt to address gaps in PSE assessment methods. The main results of this review are the 11 themes of PSE assessment that were identified from the reviewed sources. Data analyses and syntheses indicated a high prevalence of quantitative methods relying on visual aspects, signifying the dominance of the Cognitive and Navigational Experience theme due to its frequent assessment by numerous and diverse sets of methods in both reviewed sets. Nevertheless, the Temporal Experience theme emerged as the least considered. The key limitations of this systematic review include its reliance on accessible articles from bibliographic databases, as well as its focus on adult populations as the common users of public and university campus spaces. This review decodes PSE in terms of its assessment themes through the methods followed and the applied tools within real environments. As an application of the introduced conceptual framework, this systematic review clarifies the comparison of the themes examined between public and university campus spaces. The findings of this systematic review provide a foundation for a comprehensive understanding of PSE, thereby informing the design of more user-centered environments. Full article

Background and Objectives: Courier-operated motorcycles and mopeds have become increasingly visible in urban traffic, raising concerns about injuries among both couriers and pedestrians struck by courier-operated vehicles. The study period coincided with the COVID-19 pandemic; however, this study was not designed to evaluate the effect of the pandemic on courier-related injuries. This multicenter retrospective study aimed to describe hospital-based injury patterns, injury severity, treatment requirements, complications, and mortality among selected courier-related casualties, including motorcycle couriers and pedestrians struck by courier-operated motorcycles or mopeds. Materials and Methods: This retrospective multicenter observational study included courier-related traffic casualties identified between 1 March 2020, and 1 June 2022, through nine hospitals in İstanbul and Ankara and, for fatal courier cases, linked emergency medical service and hospital records. The primary clinical cohort consisted of injured couriers and pedestrians evaluated or treated at participating hospitals. Linked prehospital and early fatal courier cases were analyzed separately for mortality-related and mechanism-specific fatality analyses. Injury severity was assessed using the New Injury Severity Score. Treatment was classified as conservative or surgical, including both fracture fixation and soft-tissue procedures. Results: A total of 857 courier-related traffic casualties were identified, including 491 couriers and 366 pedestrians. Among couriers, 111 fatal cases were verified through linked records. Clinical treatment and follow-up analyses were restricted to 380 hospital-treated surviving couriers and 366 pedestrians with available clinical records. In this selected hospital-treated courier cohort, multiple fractures, open fractures, higher injury severity scores, surgical treatment, and complications were more frequent than among pedestrians. All hospital-treated surviving couriers included in the orthopedic trauma cohort underwent surgery, reflecting the orthopedic trauma-based case-identification process, whereas most pedestrians were treated conservatively. Conclusions: Among this selected hospital-based cohort, courier casualties identified through hospital, trauma referral, emergency, forensic, and linked fatal-event records showed a more severe clinical profile than pedestrians evaluated after being struck by courier-operated motorcycles or mopeds. These findings should be interpreted as hospital-based injury-severity patterns rather than population-level estimates of accident incidence, relative injury risk, or public health burden. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Pedestrian safety at signalized intersections is a key component of sustainable urban mobility, as safer walking environments support active transportation, reduce crash risk, and improve the inclusiveness of urban transport systems. This study presents a structured review of pedestrian–vehicle conflicts based on a systematic PRISMA-guided literature search, synthesizing 60 studies with emphasis on operational conditions, behavioral factors, infrastructural characteristics, and surrogate safety measures. The review examines the application of surrogate safety measures (SSMs), including Time-to-Collision (TTC), Post-Encroachment Time (PET), Pedestrian Path Deviation (PPD), and Deceleration-to-Safety Time (DST). The findings reveal significant variability in threshold definitions and methodological approaches, which limits the comparability and transferability of results across different traffic contexts. Building on this synthesis, the paper proposes an integrated conceptual framework linking behavioral, operational, and infrastructural determinants to conflict occurrence and severity. The analysis shows that existing studies often treat these factors in isolation, reducing the generalizability of current models. Overall, this review identifies key methodological inconsistencies in surrogate safety indicators and emphasizes the need for standardized yet context-sensitive thresholds and locally validated conflict models to improve the comparability and transferability of pedestrian–vehicle conflict assessments. Full article

Urban freight distribution is essential for supplying commercial activities, but it also increases pressure on curb space, vehicular circulation, pedestrian movement, and public space management, especially in medium-sized cities where dedicated loading and unloading infrastructure is often limited. Although recent literature emphasizes the need for data-driven urban logistics planning, empirical evidence from intermediate Latin American cities remains scarce. This study develops and applies a spatial-operational framework to characterize urban freight distribution, identify patterns of conflict and informality, estimate loading and unloading bay requirements, and prioritize intervention areas in a medium-sized city. A quantitative, observational, exploratory–descriptive, and correlational design was applied, based on 642 georeferenced loading and unloading operations recorded through a digital field survey. The analysis integrated data cleaning, descriptive and inferential statistics, logistic models, an operational sustainability risk/pressure index, DBSCAN spatial clustering, logistics pressure and sustainable transport priority indices, and a capacity model based on average daily operations. The results revealed spatial concentration of logistics activity, a predominance of light trucks, frequent use of paid parking areas and roadways, and a high presence of operational conflicts. The study provides a replicable and planning-oriented framework for prioritizing curbside management interventions for sustainable urban freight distribution in medium-sized Latin American cities. Full article

Modern fulfillment centers share floor space with human workers, making warehouse multi-robot task allocation a safety-critical problem. We propose SHARP (Safe Heterogeneous Allocation with Risk Prediction), a Transformer-based constrained reinforcement-learning framework with a closed-form deployment-time safety mask. Under a Gaussian pedestrian belief and fixed closest-approach directions, the mask uses Bonferroni-allocated per-pair CVaR scores; a nonnegative mask score implies a conservative trajectory-level chance constraint under the stated assumptions. We also present an idealized primal–dual surrogate analysis, without claiming global convergence for the nonconvex Transformer/PPO implementation. Expanded experiments use ten training seeds per learned method and deterministic final-checkpoint evaluation on twenty independently generated held-out instances. No statistically significant difference between SHARP and Lagrangian-PPO was detected in any of the four scenarios. The held-out analysis further reveals late-training instability and severe over-conservatism in the dense S40_high scenario. These findings position SHARP as an auditable geometric filtering mechanism, while identifying conservatism and training stability as important limitations for deployment. Full article

Walking plays a crucial role in supporting sustainable transportation systems and is closely linked to the built environment, which influences risky pedestrian crossing behavior. However, prior research has largely focused on constructs from the Theory of Planned Behavior (TPB) with limited integration of environmental factors. This research addresses this gap by employing an extended TPB framework to investigate behavioral intentions and their relationship with risky pedestrian crossing behavior. The model incorporates additional variables, including habit, personal norms, past behavior, perceived risk across behavioral and built environmental dimensions, and perceived built environment. An on-site questionnaire survey was conducted at signalised pedestrian crossings in Bangkok, Thailand, and analyzed using Structural Equation Modeling (SEM). The findings demonstrate a significant predictive effect of the core TPB constructs on behavioral intentions. Among the extended variables, habit positively influences intentions toward risky behavior, whereas personal norms and perceived risk have negative effects. Additionally, perceptions of built environment characteristics contribute to perceived physical risk. Overall, the research emphasizes the importance of integrating psychological and perceived built environment dimensions in explaining risky pedestrian crossing behavior. Policy implications emphasize the need to improve the physical environment to support safe mobility, alongside promoting positive attitudes and norms toward safe travel. The findings can further be used to guide the planning of safer pedestrian crossing facilities in urban contexts and to inform interventions that may help reduce risky pedestrian crossing behavior. Full article

In this paper, we introduce an agent-based pedestrian simulation with aerosol modeling, which we use for analyzing the risk of infection with airborne diseases, with special attention to indoor scenarios and the corresponding geometry. For our analysis, we simulate a realistic supermarket scenario, and analyze the influence of geometric factors for the risk of infection regarding aerosol concentration. Using such a defined set of geometry allows for a targeted analysis of risk factors. Specifically, we examine if angular structures bear higher viral loads than flat structures, which is confirmed by our experiments. An artificial neural network (ANN) specifically trained on simulation data is able to identify adjacent geometric structures based on aerosol concentration with up to 94% accuracy. Full article