Search Results (203)

Personal mobility (PM) devices have expanded rapidly as an urban transport mode supporting short-distance travel and first- and last-mile access to public transport, while conflicts involving PM users, pedestrians, and other road users have emerged as an important traffic-safety concern. This study reconstructed police-reported PM crash records from Seoul for 2017–2024 into a crash-level dataset of 2398 crashes and examined changes in crash composition and monthly crash-count trajectories around the strengthening of PM safety regulations on 13 May 2021. Grouped-binomial models using monthly outcome and non-outcome counts were treated as the primary analysis. The descriptive PM–pedestrian share increased from 42.9% before the regulation to 49.7% after the regulation. However, the grouped-binomial models did not identify statistically significant PM–pedestrian or severe-or-fatal level or slope changes. The only statistically significant post-regulation composition term in the full-period model was a declining late-night slope; this term was not significant under the conservative month-level HC3 covariance check in the January 2019 sensitivity window. Segmented Poisson models were retained as secondary, descriptive, and exploratory analyses. Observed post-regulation counts were lower than the trajectory obtained by extrapolating the pre-regulation trend; however, the counterfactual became implausibly large within two to three years, so the model could not distinguish a regulatory discontinuity from the natural deceleration or saturation of PM diffusion and other concurrent temporal changes. Among crashes with known helmet-use status, no statistically detectable pre/post change was observed, and license type was not recorded before the regulation. The evidence therefore does not establish either a beneficial or adverse causal effect of the regulation. PM safety policy should combine user-oriented enforcement with multi-indicator monitoring, pedestrian-conflict management, continuous and clearly separated PM travel space, and targeted monitoring of late-night single-vehicle crashes. Full article

The upcoming proliferation of Automated Vehicles (AVs) above Level 3 fundamentally disrupts established communicative conventions between road users and vehicles, and external Human–Machine Interface (eHMI) emerges as the primary channel for conveying vehicle intent to pedestrians, cyclists, and other drivers. Despite the standardisation of the marker lamp of AVs and the growing research activity since 2016, no internationally harmonized eHMI standard has been established as of early 2026. This Systematic Literature Review (SLR), following the PRISMA 2020 protocol, synthesizes empirical and technical findings from 149 peer-reviewed English journal articles spanning the decade from 2016 to 2026, from Web of Science, Scopus, IEEE Xplore, and Google Scholar. The review covers scenario design, test methodologies, participant demographics, eHMI modalities, and performance evaluation measures by mapping, coding, and presenting in a structured framework the reviewed papers. The key findings are as follows: (1) 13 use cases (50%, out of 26) still need to be further studied; (2) vehicle kinematics remain a primary determinant of crossing decisions, with eHMI signals serving as confirmatory rather than primary cues in many scenarios; and (3) cross-cultural and demographic diversity in empirical studies remains critically insufficient for global regulatory purposes. We synthesize these findings into a structured framework of evidence-based recommendations for international eHMI standardisation organizations and identify critical research gaps requiring resolution before comprehensive standards can be enacted. Full article

The growing deployment of right-turn warning zones (RTWZs) at urban intersections in China aims to enhance the operational safety of urban traffic systems, yet encroachment upon these zones by pedestrians and non-motorized vehicle (NMV) users remains a practical concern. This study combined a field observation at two intersections in Hefei, China, to examine these vulnerable road users’ actual unsafe RTWZ encroachment behaviors, with an extended theory of planned behavior (TPB) questionnaire survey to explore the cognitive mechanisms underlying intentions of such encroachment. Observational results showed that NMV riders encroached on RTWZs significantly more frequently than pedestrians, and such encroachments occurred more often during morning peak hours. The questionnaire survey on 402 respondents revealed that attitude, perceived behavioral control (PBC), and past behavior showed the strongest positive effect on encroachment intention, both directly and indirectly through PBC. In contrast, perceived risk and safety knowledge negatively predicted intention indirectly, while also showing significant indirect effects on intention impacting attitude. Subjective norm, sociodemographic characteristics, and travel patterns were not significant predictors. These findings not only provide a deeper understanding of the real-world effectiveness of RTWZs, but also offer an evidence-based basis for developing countermeasures to improve operational safety of urban slow traffic systems. Full article

Pedestrian safety in urban areas remains a critical concern, especially for vehicles with large blind spots. This study examined associations between surround view monitoring (SVM)-defined risk zones and safety-related indicators using real-world driving data from school zones and pedestrian crash-prone areas in Seoul, South Korea. Vehicle on-board diagnostics, RTK positioning, SVM object-detection outputs, and video data were collected, and vehicle–vulnerable road user interactions were reconstructed as events. Detection-to-braking time (DBT), time to collision (TTC), time-exposed TTC (TET), time-integrated TTC (TIT), TIT/TET, and jerk were used to compare danger and warning zones. The danger zone showed lower estimated TTC and higher TIT/TET values than the warning zone, indicating shorter time margins and higher average risk intensity during below-threshold TTC intervals. The warning zone showed higher longitudinal jerk, suggesting repeated vehicle-control adjustments during longer events. In the four-day repeated-driving dataset, the 95th percentile total jerk in the danger zone decreased over time; however, this uncontrolled trend was interpreted as exploratory rather than causal. These findings suggest that SVM-defined risk zones are associated with differences in event-level risk and vehicle dynamic indicators, supporting their potential use in event-based safety assessment. Full article

Pedestrian safety represents a critical priority for the development of sustainable urban mobility systems. This study proposes an innovative methodological framework integrating supervised and unsupervised learning techniques with econometric modeling to identify and interpret risk scenarios. Using the Italian national dataset from 2010 to 2023, an XGBoost model has been initially trained and tested. Then, SHapley Additive exPlanations (SHAPs) have been applied to highlight contributing factors. Using the resulting SHAP values, a K-Means clustering algorithm was finally employed to segment crashes into homogeneous clusters. For each cluster, a Generalized Linear Mixed Model incorporating geographic random intercepts and temporal random slopes was calibrated. Through this hybrid-stratified approach, three risk scenarios have been identified, primarily driven by demographic vulnerability. For elderly pedestrians, the involvement of heavy vehicles nearly doubles the odds of a fatal outcome. Crash dynamics varied significantly: heavy vehicles and speeding nearly double the fatality risk for elderly pedestrians; nighttime represents a severe hazard for adults (OR = 3.87) and youths (OR = 7.99), with the latter also highly penalized by unsafe road behaviors (OR = 3.12). From a spatio-temporal perspective, random effects revealed that the Islands (Sicily and Sardinia) are the most critical macro-areas (+55.2% baseline risk for adults) and the North-West the safest. Furthermore, the COVID-19 pandemic mitigated fatal risk for young pedestrians nationwide, had a neutral impact on the elderly, and for adults was protective in Southern regions but corresponded to higher odds of mortality in the North, reflecting altered traffic dynamics. Full article

Climate change poses increasing threats to urban populations, with rising temperatures exacerbating heat-related health risks and reducing outdoor thermal comfort. Although cities occupy only a small fraction of the Earth’s surface, they function as complex ecosystems where human transformations significantly influence environmental quality and public health. This research introduces a trans-scalar methodological framework to assess the impacts of climate change on human well-being in urban areas, integrating qualitative and quantitative approaches. The methodology combines direct field observations, instrumental surveys using infrared thermography, quantitative land cover analysis (Runoff and RIE indices), and predictive microclimatic simulations using ENVI-met 5.8. The framework is applied to two case studies in Italy, the municipalities of Bibbiena and Borgo San Lorenzo, focusing on pedestrian accessibility to community healthcare facilities, known as Community Houses. Results reveal critical environmental vulnerabilities, including surface temperatures reaching up to 60 °C on impervious materials, runoff coefficients exceeding 80%, and Physiological Equivalent Temperature (PET) values exceeding 56 °C during summer peak hours, indicating extreme heat stress conditions. The proposed framework enables the identification of priority intervention areas for climate adaptation strategies, supporting evidence-based urban design that prioritises public health and thermal comfort. The methodology offers replicability for other Mediterranean cities facing similar climate challenges, providing a decision-support tool for planners and public administrations. 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

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

Pedestrian detection is a fundamental component of Advanced Driver Assistance Systems (ADAS) and plays a key role in collision avoidance and the safety of vulnerable road users. This paper presents a structured review of pedestrian detection methodologies developed between 2000 and 2025, spanning classical vision techniques and modern deep learning architectures. We organize the review into two phases. First, we examine classical methods, including Histogram of Oriented Gradients (HOG)+Support Vector Machine (SVM), Viola–Jones, Deformable Part Models, and Integral Channel Features, which established the conceptual foundations of the field. Then, we analyze state-of-the-art deep learning architectures, categorized by detector stage (one-stage vs. two-stage), localization strategy (anchor-based vs. anchor-free), feature extraction paradigm (Convolutional Neural Network (CNN)-based vs. transformer-based), output representation (bounding box vs. instance segmentation), and computational profile (lightweight vs. heavyweight). Several design principles introduced by classical methods remain visible in modern architectures, indicating that they were not fully superseded. The review also examines publicly available benchmark datasets and compares the strengths and limitations of camera-, Light Detection And Ranging (LiDAR)-, radar-, and multi-sensor-fusion-based systems for ADAS deployment. We close by identifying six open problems for the field: adversarial robustness, real-time inference under embedded constraints, detection under adverse weather, dataset bias and demographic fairness, the deployment of Bird’s-Eye View (BEV) and unified perception on automotive hardware, and explainability for safety-critical use. Full article

Road traffic accidents continue to be a major global cause of fatalities, disproportionately affecting pedestrians and other vulnerable road users. While deep reinforcement learning has proven effective in handling complex navigation tasks, providing formal stability and safety guarantees during both training and deployment remains a significant challenge. This paper introduces a dual-layer safety-aware framework for pedestrian avoidance in occluded urban driving. During training, a first-order Control Lyapunov–Barrier Function is integrated with Proximal Policy Optimization to promote goal-reaching stability and obstacle avoidance: the analytic Lie derivatives of the Lyapunov and barrier functions are embedded as a modifier in the advantage estimate, providing explicit stability and safety signals that accelerate convergence toward safe, goal-reaching behavior without disrupting the standard policy update. At deployment, a higher-order Control Lyapunov–Barrier Function, realized through a quadratic programming safety filter, acts as a safety shield that projects the nominal acceleration onto the intersection of the second-order Lyapunov and barrier feasibility sets; the barrier function is further extended with relative velocity terms to account for dynamic pedestrian motion. Experiments with a four-wheeled vehicle in the Webots simulator show that the framework reliably reaches the goal, avoids an occluded pedestrian across a range of crossing speeds, and improves task success rates and safety-constraint adherence relative to Proximal Policy Optimization and a conventional higher-order safety filter baseline, particularly during emergency braking maneuvers. Full article

With the expansion of metro networks and the increasing integration of underground retail and transit facilities, metro-connected underground commercial spaces have become a common yet safety-sensitive urban form. In fire scenarios, evacuation in such environments is constrained not only by enclosure and limited egress capacity, but also by the interaction between smoke spread and strongly coupled pedestrian flows across connected zones. Existing studies have examined smoke propagation or evacuation performance in underground spaces, but fewer have explicitly addressed how smoke constraints reshape node-level safety and the relative effectiveness of different intervention strategies in metro-connected commercial environments. This study investigates smoke-constrained evacuation dynamics in a representative metro-connected underground commercial space in Nanjing, China. A coupled simulation framework integrating PyroSim and Pathfinder is employed to examine multiple fire-source scenarios. Available safe egress time (ASET) at critical evacuation nodes is assessed using tenability criteria including visibility, temperature, and CO concentration, and is then compared with evacuation performance to diagnose hazardous routes and node-level failures. On this basis, three intervention strategies—corridor widening, stair widening, and pedestrian diversion—are comparatively evaluated. The results show that, within the modeled case, visibility most frequently becomes the controlling tenability criterion, and stairway nodes tend to lose safety margins earlier than final exits. This indicates that smoke constraints in connected underground commercial environments can trigger an early node-failure process before overall exit capacity is exhausted. The comparison further shows that behavior-oriented pedestrian diversion is more effective than geometric enlargement alone in reducing critical-node pressure and improving system-level evacuation performance under the modeled conditions. Rather than proposing universally transferable design rules, this study provides case-grounded evidence on how smoke propagation and pedestrian convergence jointly shape evacuation vulnerability in metro-connected underground commercial spaces, and offers a structured basis for critical-node diagnosis and intervention comparison in similarly configured environments. Full article

Background/Objectives: Traffic accidents are a significant public health issue. Pedestrians are considered the most vulnerable victims, showing the highest mortality rates. Thus, mortality rate indicators reflect the effectiveness of policies and safety measures applied to urban mobility. Therefore, the study objective is to identify trends in pedestrian mortality to foster an understanding of the local reality and to propose effective interventions for the safety and mobility of this population. Methods: This ecological time-series study used secondary data from the Unified Health System Information System (DATASUS) on all traffic accident-related deaths in Espírito Santo, Brazil, from 2009 to 2020. Data were classified according to the 10th Revision of the International Classification of Diseases (ICD-10). Variables included sex (male; female), age group (in years: 0 to 80+), and victim type (pedestrian). Mortality rates were logarithmically transformed (base 10), and the Prais–Winsten regression model was employed using STATA 13.0. Results: A total of 1969 traffic accident-related deaths were recorded. Males accounted for 75% of deaths, individuals of mixed race (pardo) represented 57%, and 72% were unmarried. A significant reduction in mortality rates was observed across age groups, especially among individuals aged 0–24 years. Mortality trends remained stationary only among individuals aged 80 years and older. Overall, the mortality rate decreased throughout the study period, from 5.51 to 1.69 deaths per 100,000 inhabitants. Conclusion: Pedestrian mortality rates from traffic accidents in Espírito Santo showed a decreasing trend, particularly among children and young people, while remaining stable among the elderly. Full article

Background: Urban road-traffic fatalities are a public health burden and a governance challenge because protection is uneven across urban space and time. Methods: We analyzed 702,540 administrative road-incident records from Medellín, Colombia (2008–2025), identified 2762 fatal cases, standardized incident categories, and harmonized time and coordinate fields. Spatial analyses were based on 2507 geocoded fatalities. We combined descriptive profiling, chi-square tests, logistic regression comparing pedestrian-strike and collision fatalities, sensitivity analyses using grouped time periods and a pandemic-period indicator, and spatial autocorrelation measures using Moran’s I and Getis–Ord $G_i^{*}$. Results: Incident type composition did not differ significantly between daytime and nighttime, but it varied across districts (comunas). Each later hour was associated with slightly higher odds that a fatality would be classified as a pedestrian strike rather than a collision (OR = 1.033), and fatalities in the urban core had nearly threefold higher odds of being classified as pedestrian strikes (OR = 2.953). Sensitivity analyses did not materially alter these associations. Spatial statistics showed strong clustering among the dominant fatality classes and identified 129 significant hotspot cells. Conclusions: Fatal road-traffic harm in Medellín is spatially concentrated and varies by incident mechanism, with pedestrian fatalities disproportionately concentrated in central areas of intense pedestrian–vehicle interaction. These findings show that transparent surveillance analytics can inform governance prioritization while also underscoring the need to improve data completeness, incorporate exposure measures, and interpret pandemic-period patterns with caution. Full article

In the context of subtropical cities, the slow-moving environment of HOD (Hospital-Oriented Development) faces the dual challenges of spatial fragmentation and an extreme hot and humid climate, which also restricts the outdoor space’s thermal environment performance. Taking the Macau Light Rapid Transit (LRT) Union Hospital Station as an example, this study constructs a “topology-climate” dual quantitative assessment framework that integrates space syntax and parametric universal thermal climate index (UTCI) simulation. In response to the current problems of mixed pedestrian and vehicular traffic and high-intensity heat radiation, a comprehensive intervention strategy combining three-dimensional stitching and spatial optimization is proposed. The results show that: (1) The implantation of three-dimensional corridors improved the spatial integration of the core area of the site by 67.0%, significantly optimizing network connectivity. (2) During the extreme high-temperature period of daytime (9:00–18:00) in summer and autumn, the intervention strategy precisely opened up a continuous low-heat-stress linear shade zone through the synergistic mechanism of building projection shadows, physical shading of connecting corridors, (landscape shading effect, original evaporation removed). (3) The study confirms that landscape-coupled shading layout is the most effective method, reducing potential pedestrian heat exposure across the entire area, while the three-dimensional connecting corridors precisely control the thermal environment of core walkways. Together, these two elements construct a “topology-climate” optimization framework, achieving a synergistic improvement in spatial accessibility and simulated thermal comfort performance under standard meteorological input and quantitatively verifying the optimization effectiveness of the tiered intervention scheme. This study provides a data-driven decision-making basis for optimizing potential walking thermal conditions for vulnerable groups and reshaping the space’s potential to improve microclimate via shading design of medical hub areas and also provides a scientific paradigm for TOD microclimate planning focused on shading-based thermal environment optimization. Full article

Vehicle-to-Everything (V2X) communication is a critical enabler of autonomous driving, supporting real-time information exchange among vehicles, roadside infrastructure, pedestrians, and cloud services. However, the security of current V2X systems largely relies on classical cryptographic mechanisms, which are expected to become vulnerable in the presence of large-scale quantum computers. Given the long operational lifespan and stringent safety requirements of autonomous vehicular networks, the transition toward quantum-resistant authentication and key management mechanisms has become increasingly important. This paper presents a comprehensive survey of post-quantum Authentication and Key Agreement (AKA) protocols for secure V2X communications. The survey systematically reviews V2X communication architectures, security and privacy requirements, existing authentication frameworks, and emerging post-quantum cryptographic approaches. Representative AKA schemes and NIST-standardized post-quantum algorithms are comparatively analyzed in terms of security strength, computational complexity, communication overhead, storage requirements, scalability, and deployment suitability for resource-constrained vehicular environments. The survey further examines practical implementation challenges, including latency constraints, bandwidth limitations, signature size expansion, memory consumption, and hardware resource requirements. The analysis reveals that achieving quantum-resistant security in V2X networks requires balancing strong cryptographic protection with the stringent performance demands of safety-critical vehicular applications. While recent post-quantum approaches offer promising security guarantees against quantum adversaries, their practical deployment remains constrained by computational and communication overhead. Finally, this survey identifies key research gaps and outlines future directions for the development of lightweight, scalable, and quantum-resilient AKA frameworks capable of supporting next-generation autonomous transportation systems. The findings provide researchers and practitioners with a structured understanding of the opportunities, limitations, and challenges associated with securing future V2X communications in the quantum era. Full article