Search Results (641)

This study investigates the relationship between cardiorespiratory fitness and driving behavior in a highway environment using a driving simulator. A total of 46 young drivers aged 19 to 27 years participated in the experiment. Cardiorespiratory fitness was assessed through the Queen’s College Step Test and heart rate monitoring, allowing participants to be classified into high-fitness and low-fitness groups based on estimated maximum oxygen consumption. Each participant completed three simulated highway driving scenarios under varying traffic and lighting conditions. Driving performance data were continuously recorded, while additional individual and behavioral characteristics were collected through a structured questionnaire. The analysis focused on key performance indicators, including headway distance variability, average speed, and time to collision. Statistical analysis was conducted using regression models. The results indicate that higher physical fitness is associated with greater adaptability in driving behavior, reflected in increased headway variability and slightly higher driving speeds. At the same time, high-fitness drivers exhibited longer time to collision, suggesting improved anticipation and more effective management of traffic conditions. Environmental factors, particularly traffic volume and lighting conditions, remained dominant in shaping driving behavior. Overall, the findings suggest that physical fitness contributes to a more adaptive driving style on highways. By integrating physiological condition into the analysis of driver behavior, this study highlights the importance of considering health-related factors in road safety research and provides insights for developing preventive strategies targeting young drivers. Full article

This paper presents a novel approach to design an attitude motion control strategy of a vehicle to mitigate lateral or longitudinal inertial forces acting on the passenger during cornering, braking, and acceleration maneuvers. The collaboration of active suspension system and active airfoil substantially enhances the attitude motion of a vehicle. By incorporating integral control action for both the desired body attitude roll or pitch angle and zero dynamic tire deflection within the performance index, the optimal controller maintains the ideal roll or pitch angle while preserving the road holding capability. The computer simulations were conducted to evaluate the dynamic performance of the proposed system in comparison with various other suspension systems based on a 4-degree-of-freedom half-car model. Four scenarios for rolling and pitching motions were simulated as follows: the first case examines the rolling response to a one-sided bump input applied to a lateral half-car model during straight-line driving. The second case investigates the rolling performance during a cornering maneuver. The third and fourth cases analyze the pitching responses to braking and acceleration using a longitudinal half-car model. The simulation results demonstrate that the proposed system maintains the ideal body attitude, attenuates the effect of the lateral or longitudinal inertial forces and keeps an ideal road holding capability. As a result, the proposed control system substantially improves ride comfort while enhancing the dynamic safety of the vehicle. Full article

Autonomous navigation on unknown uneven terrain remains a critical challenge for unmanned ground vehicle (UGV) deployed in unstructured environments such as disaster relief, wilderness exploration, and off-road logistics. Existing motion planning methods for such environments suffer from three key limitations: under-utilization of the solution space due to discretized terrain assessment, difficulty in transforming complex terrain safety constraints into optimization-compatible forms, and the inherent trade-off between environmental modeling accuracy and real-time performance. This paper presents a hierarchical motion planning framework that enables safe and fast navigation of UGV on unknown uneven terrain. We first construct a traversability map based on terrain slope, roughness, and sparsity extracted from ground point cloud clusters. Non-traversable points are then transformed via spherical inversion and inverse mapping to generate terrain safety corridors composed of a series of convex polygons. The geometric containment relationship between the vehicle’s convex hull and the corridor is reformulated as continuously differentiable Control Barrier Function (CBF) constraints to ensure driving safety. The front-end employs a kinodynamic Hybrid A* algorithm with a traversability-aware node pruning strategy, while the back-end trajectory optimization embeds the CBF constraints as hard constraints within the optimization loop to guarantee forward invariance of the safety set under the linearized dynamics. The proposed framework achieves full-shape collision avoidance without sacrificing the solution space, while maintaining real-time performance for autonomous navigation on complex terrain. Full article

This paper presents a robust dual-loop control strategy for the lateral motion and heading-angle regulation of an autonomous vehicle equipped with a Steer-By-Wire (SBW) system under unknown time-varying disturbances. The proposed framework comprises a fuzzy PID controller in the inner loop to generate the motor torque and track the front-wheel steering angle, and an optimal backstepping controller in the outer loop—integrated with a finite-time disturbance observer—to ensure lateral trajectory tracking and wind-disturbance rejection. The PID gains are tuned online by a Mamdani-type fuzzy inference system, while the backstepping parameters are optimized offline via a genetic algorithm. Beyond the bicycle-model-based design, the controller is evaluated through supplementary simulations using a 6-degree-of-freedom (6-DOF) vehicle model, as well as through a detailed robustness analysis that includes measurement noise and increasing lateral disturbance forces. The results demonstrate that the closed-loop system achieves precise path tracking, finite-time convergence of both tracking and estimation errors, and effective compensation of road vibrations and wind disturbances. Furthermore, the controller maintains stable performance under significant measurement noise and tolerates lateral disturbance forces up to at least 10,000 N without violating safety constraints. The effectiveness of the proposed method is consistently confirmed across both the reduced-order bicycle model and the higher-fidelity 6-DOF validation environment. Full article

Vulnerable Road Users (VRUs) are involved in a significant proportion of traffic fatalities, and they are highly exposed to severe injuries in urban traffic environments. For detecting and tracking VRUs, LiDAR technology offers precise 3D perception capabilities, overcoming challenges posed by their small size, dynamic behavior, and frequent presence in occluded or congested areas. This work aims to conduct a scoping review of LiDAR-based solutions for preventing and reducing accidents involving VRUs, synthesizing current methodologies, evaluating detection and tracking approaches, and identifying strategies to improve urban safety through data-driven interventions. An analysis of 49 publications indicates that effective monitoring of VRUs depends on a strategic balance between technological performance and practical limitations, such as system costs, calibration complexity, and hardware constraints. Privacy-preserving techniques, such as anonymization and LiDAR-based sensing, are essential to enable ethically responsible large-scale data collection. Full article

Investment in infrastructure is considered the foundation for economic growth. However, traditional construction and maintenance methods in Egypt are carbon-intensive, which conflicts with sustainability strategies. Therefore, there was a need to develop a model for evaluating highway maintenance methods to facilitate decision-making on the best ones, economically, environmentally, and socially. This study included a model for evaluating sustainability in road maintenance. It integrated carbon management and value engineering to facilitate the selection of the best alternatives for achieving sustainability. The literature on sustainability criteria covering the project life cycle was consulted, and 27 key factors across the three sustainability criteria were selected. A questionnaire was conducted to determine the weights of the criteria using the Analytic Hierarchy Process (AHP). Road maintenance scenarios were then developed, and the carbon emissions for each were calculated. The cost of carbon disposal was added to the project life cycle cost using CCS technology. This model was named SRMVE because it ultimately combines economic and environmental challenges into a single factor to facilitate a comparison of the proposed alternatives and achieve the best degree of sustainability. The model results were compared with the sustainability scores generated by the AHP to assess the extent of agreement. This model provides decision-makers with a way to sort through maintenance alternatives and identify those with the lowest lifecycle emissions while maintaining the service and safety levels. Full article

Road Traffic Accidents (RTAs) represent a significant public safety issue in rapidly urbanising nations, resulting in considerable fatalities, injuries, and economic losses. This research investigates the spatio-temporal distribution and hotspot dynamics of RTAs in Siliguri City, India, a principal transnational transport corridor connecting northeastern India with adjacent countries. A geocoded dataset comprising RTA incidents from 2021 to 2023 was analysed using integrated GIS-based machine learning and statistical methods. Temporal clusters were identified through Kulldorff’s purely temporal scan statistics, while Kernel Density Estimation (KDE) quantified accident density during morning peak, midday/off-peak, evening peak, and lean/night-time intervals. Spatial clustering was further assessed using LISA-Moran’s I, purely spatial scan statistics, and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). Emerging Hotspot Analysis (EHA) was employed to detect evolving hotspot patterns over time. The findings indicate that major accident hotspots are concentrated at key intersections and transport corridors, such as Hill Cart Road, Darjeeling More, Sevoke Road, Eastern Bypass, and Burdwan Road. Moran’s I (0.157; p = 0.007) demonstrates significant but moderate spatial autocorrelation, and spatial scan statistics identified three principal high-risk zones. HDBSCAN classified 81.90% of incidents within clustered areas. Lean/night-time periods exhibited the highest accident densities, reaching 14.21 accidents/km² at critical intersections. These results underscore the utility of integrating GIS and machine learning techniques for urban traffic safety planning and hotspot-focused intervention strategies. Full article

This paper presents a wearable system based on low-cost inertial sensors for the continuous monitoring of driver motion and behavior under controlled urban driving conditions. The system consists of distributed wearable units placed on the head, neck, and torso, each equipped with an inertial measurement unit (IMU) that measures linear acceleration and angular velocity. The acquired data are processed in real time to characterize the driver’s kinematic response during vehicle operation. The main contribution of this work is the definition of a novel Driving Aggressiveness Index (DAI) for quantitative driving style assessment. The proposed index integrates motion-derived features based on acceleration and angular velocity and combines information from multiple body segments through a normalization and weighting strategy, enabling a compact and interpretable representation of driver behavior. Experimental validation was conducted in an urban driving scenario under controlled traffic-free conditions, including typical maneuvers such as straight driving, braking, roundabout navigation, lane changes, and yielding, performed under both normal and aggressive driving styles. The results demonstrate that the monitoring system captures distinct kinematic patterns and that the proposed index provides a clear and consistent separation between driving behaviors. A data-driven threshold is also defined, enabling the quantitative classification of driving styles. Overall, the proposed approach offers an interpretable, scalable, and real-time solution for driver monitoring, with potential applications in road safety and sustainable mobility. Full article

Digital Twin (DT) technologies are increasingly adopted in the automotive domain to support real-time monitoring, predictive analytics, and connected decision-making across vehicles, drivers, and road infrastructure. However, research on Vehicle, Driver, and Road Digital Twins (VDTs, DrDTs, and RDTs) remains fragmented, with heterogeneous definitions, architectural assumptions, and integration strategies. This paper presents a critical review of seventy-six studies published between 2008 and 2025, examining how these three DT domains are modeled, evaluated, and connected within intelligent mobility scenarios. The review synthesizes recurring architectural patterns, communication and computing choices, and the role of interoperability and standardization in multi-twin systems. It also highlights open challenges involving distributed coordination, semantic alignment, real-time operation, and driver-aware adaptation. Based on this analysis, the paper presents a unified conceptual framework for connected automotive digital twins and discusses key directions for building scalable and safety-aware mobility services. Full article

Road disasters such as subsidence and bridge failures pose severe threats to traffic safety. Existing warning distance calculation methods typically assume homogeneous traffic flow and overlook the spatial heterogeneity of vehicle responses across different vehicle types, limiting their applicability for geospatial early warning systems. This paper proposes a dynamic warning distance model that integrates mixed-traffic flow composition—comprising human-driven vehicles (HDVs), Level 2 advanced driver-assistance system vehicles (ADASVs), and automated vehicles (AVs) of Level 3 and above—within a geospatial risk propagation framework. The model introduces vehicle-type weighting coefficients to quantify response differences, incorporates interaction delays calibrated through SUMO microsimulations, and accounts for cascading reaction delays caused by abrupt HDV braking. The methodology is illustrated using a counterfactual reconstruction of the 2024 Meizhou–Dapu Expressway collapse in China (52 fatalities). Based on reconstructed traffic conditions (80% HDVs, 15% ADASVs, 5% AVs; average speed 27.5 m/s; flow 1800 veh/h), the calculated dynamic warning distance is 153 m, which is 12% shorter than the speed-matched conventional stopping sight distance of 174 m (computed under consistent wet-pavement assumptions). Sensitivity analyses reveal that warning distance decreases substantially with increasing AV penetration (to 42 m in AV-dominated scenarios, a potential reduction of up to 74% compared with the HDV-dominated baseline, provided that residual HDVs are supported by V2X-based alerting) and varies monotonically with traffic flow, demonstrating the model’s adaptive capability. The proposed framework provides a theoretical foundation for adaptive geospatial disaster warning strategies and offers practical guidance for infrastructure development in the era of mixed-traffic automation. Full article

The paper analyses the economic impact of the reduction in road traffic accidents in Slovakia between 2000 and 2024 and quantifies both direct and indirect costs of road crashes. Over this period, annual crashes declined from more than 50,000 to approximately 11,500 and fatalities from over 600 to 262, demonstrating the effectiveness of national road safety strategies. The methodology is based on the national road accident database, complemented by macroeconomic and demographic indicators, and follows European recommendations for the valuation of external costs of transport. The study applies the value of a statistical life, the value of a statistical life year, the relative severity index and the critical accident rate, with particular emphasis on comparing the value of a statistical life and the relative severity index. The total VSL-based economic costs of road traffic crashes in 2024 are estimated at approximately €1.25 billion, underscoring the scale of the socioeconomic burden. Building on the forecasted values for 2025, the paper further tests and compares these methodologies on a specific road section, illustrating their practical implications for project appraisal and safety management. The results confirm that VSL-based estimates systematically exceed RSI-based estimates by 21–45% per year, reflecting the broader societal costs captured by the VSL concept. The study shows that investments in safety measures are economically worthwhile and reduce the burden on public finances, while also highlighting the need to harmonize methodologies and improve data quality. Full article

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment. Full article

Traffic accidents pose a persistent challenge for urban mobility, public safety, and sustainable development in smart cities, particularly in rapidly growing urban environments. This study presents a data-driven spatiotemporal analysis of traffic accidents in Ecuador, aimed at supporting evidence-based urban traffic management and road safety planning. Using large-scale historical accident records, the proposed approach combines spatial clustering and temporal forecasting techniques to characterize accident concentration patterns and temporal dynamics at national and metropolitan scales. Spatial accident hotspots are identified using Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling the detection of high-risk zones without imposing assumptions on cluster shape or size. This analysis reveals strong spatial concentration of accidents, with a limited number of clusters accounting for a substantial proportion of fatalities and injuries. Complementary temporal analysis is conducted using a multi-model ensemble framework to examine accident trends and seasonal patterns. This approach integrates SARIMA for linear stochastic modeling and Prophet for additive trend analysis, alongside two Long Short-Term Memory (LSTM) architectures: a direct 12-month vector output and a recursive horizon-3 model. By synthesizing these statistical and neural network-based methods through inverse-RMSE weighting, the study captures both stable seasonal cycles and non-linear, short-to-medium-term variations in accident frequency. Results show that traffic accidents in Ecuador exhibit stable diurnal and seasonal structures, alongside pronounced spatial heterogeneity across urban regions. The combined spatial and temporal insights provide a coherent representation of accident risk patterns, facilitating the prioritization of critical zones and high-risk periods. The resulting hotspot maps and multi-model forecasting horizons offer actionable information for smart city stakeholders, supporting targeted infrastructure interventions, adaptive enforcement strategies, and data-informed urban mobility policies. This work contributes to the broader understanding of traffic safety analytics as a core component of smart city decision-support systems. Full article

For autonomous vehicles, high-speed cornering can easily lead to degraded handling stability and increased risks of sideslip or even rollover. Therefore, vehicle phase-plane stability-region analysis has become an important topic in active safety-control research. However, most existing studies still construct phase-plane stability regions mainly based on simplified vehicle models, without sufficiently considering the influence of vertical load transfer during cornering on tire lateral forces and stability boundaries. To address this issue, this paper proposes a hierarchical control strategy based on phase-plane analysis for active inward tilt vehicles. This method adopts a three-degree-of-freedom vehicle dynamics model and a tire model. By carefully comparing the phase-plane stability regions of active inward tilt and passive roll vehicles and by further analyzing the state-trajectory convergence characteristics of active inward tilt vehicles under different longitudinal speeds, front wheel steering angles, and road adhesion coefficients, the effects of active inward tilt on stability-region expansion and vehicle-state convergence are revealed. Subsequently, a hierarchical control strategy is proposed as an integrated solution to improve vehicle handling stability. The upper-level controller dynamically adjusts the reference values and objective weights according to whether the vehicle state is located in the stable, critical, or dangerous region. The lower-level NMPC controller optimizes the front wheel steering angle and active suspension forces to achieve coordinated trajectory tracking and stability control. Double lane-change simulation results show that active inward tilt can improve the left–right vertical load distribution and expand the lateral stability region. Compared with passive roll and conventional active inward tilt control, the proposed strategy reduces the phase-plane state convergence area by 68% and 75%, respectively, thereby improving vehicle handling stability and active safety under extreme conditions. Full article

Urban fire risk in high-density cities is characterized by complex spatial heterogeneity and nonlinear relationships with the built environment, population distribution, and climatic conditions. However, most existing studies rely on linear assumptions and offer limited interpretability. To address this gap, we developed an interpretable analytical framework that integrates the CatBoost model with SHAP (SHapley Additive exPlanations), using Futian District in Shenzhen as a case study. We constructed a fire risk surface from historical fire incident data using kernel density estimation (KDE) and incorporated multiple urban environmental factors—including points of interest (POIs), road networks, and meteorological variables—as explanatory variables. The CatBoost model captured nonlinear relationships, while SHAP quantified feature importance and revealed interaction effects. The results show that urban fire risk is strongly associated with the spatial agglomeration of population-related facilities, especially high-density commercial and residential areas, as well as thermal conditions. Several variables exhibit clear nonlinear threshold effects, with their influence on fire risk varying markedly across different intensity ranges. Interaction analysis further indicates that combinations of built-environment characteristics and climatic factors jointly shape the spatial pattern of fire risk. These findings provide empirical insights into the spatial mechanisms underlying urban fire risk and highlight the value of interpretable machine learning in urban safety research. The proposed framework offers a practical tool for developing more targeted, evidence-based fire risk management strategies in high-density urban areas. Full article