Search Results (86)

Autonomous driving systems have the potential to reduce traffic accidents dramatically; however, conventional modules often struggle to accurately detect risks in complex environments. This study presents a novel risk recognition system that integrates the reasoning capabilities of a large language model (LLM), specifically GPT-4, with traffic engineering domain knowledge. By incorporating surrogate safety measures such as time-to-collision (TTC) alongside traditional sensor and image data, our approach enhances the vehicle’s ability to interpret and react to potentially dangerous situations. Utilizing the realistic 3D simulation environment of CARLA, the proposed framework extracts comprehensive data—including object identification, distance, TTC, and vehicle dynamics—and reformulates this information into natural language inputs for GPT-4. The LLM then provides risk assessments with detailed justifications, guiding the autonomous vehicle to execute appropriate control commands. The experimental results demonstrate that the LLM-based module outperforms conventional systems by maintaining safer distances, achieving more stable TTC values, and delivering smoother acceleration control during dangerous scenarios. This fusion of LLM reasoning with traffic engineering principles not only improves the reliability of risk recognition but also lays a robust foundation for future real-time applications and dataset development in autonomous driving safety. Full article

This research is part of a broader investigation into innovative simulation-based approaches for improving traffic efficiency and road safety in roundabout corridors. These corridors, composed of successive roundabouts along arterials, present systemic challenges due to the dynamic interactions between adjacent intersections. While previous studies have addressed localized inefficiencies or proposed isolated interventions, this paper introduces possible replicable methodology based on a microsimulation and surrogate safety analysis to evaluate roundabout corridors as integrated systems. In this context, efficiency refers to the ability of a road corridor to maintain stable traffic conditions under a given demand scenario, with low delay times corresponding to acceptable levels of service. Safety is interpreted as the minimization of vehicle conflicts and critical interactions, evaluated through surrogate measures derived from simulated vehicle trajectories. The proposed approach—implemented through Aimsun Next and the SSAM tool—is tested on two real-world corridors: Via Aurelia Nord in Pisa (Italy) and Route de Marseille in Avignon (France), assessing multiple intersection configurations that combine roundabouts and signal-controlled junctions. Results show how certain layouts can produce unexpected performance outcomes, underlining the importance of system-wide evaluations. The proposed framework aims to support engineers and planners in identifying optimal corridor configurations under realistic operating conditions. Full article

Traffic safety assessments rely on Surrogate Safety Measures (SSMs), yet their diversity hinders understanding and selection. This paper proposes a novel conceptual framework to systematically categorize SSMs through what we term Motion Scenario Mapping, an approach inspired by queuing theory notation and the concept of short-term behavioral storytelling. The framework explicitly defines interaction stories between a following and leading vehicle to reveal hidden assumptions within each SSM, achieved through a combined coding system. Examining ten common SSMs, the research demonstrates that the framework effectively exposes underlying assumptions, enabling critical evaluation of their contextual validity. By emphasizing short-term risk dynamics, this approach offers a structured understanding of interaction mechanisms and provides a systematic foundation for comparing existing SSMs, identifying research gaps, and guiding future development. This structured ontology has the potential to enhance the analysis and design of safety measures for future transportation systems. Full article

Recent advances in high-fidelity modeling, numerical computing, and data science have spurred interest in model-data integration for nuclear reactor applications. While machine learning often prioritizes data-driven predictions, this study focuses on data assimilation (DA) to synergize physical models with measured data, aiming to enhance predictive accuracy and reduce uncertainties. We implemented deterministic DA methods—Kalman filter (KF) and three-dimensional variational (3D-VAR)—in a one-dimensional single-phase natural circulation loop and extended 3D-VAR to RELAP5, a system code for two-phase loop analysis. Unlike surrogate-based or model-reduction strategies, our approach leverages full-model propagation without relying on computationally intensive sampling. The results demonstrate that KF and 3D-VAR exhibit robustness against varied noise types, intensities, and distributions, achieving significant uncertainty reduction in state variables and parameter estimation. The framework’s adaptability is further validated under oceanic conditions, suggesting its potential to augment baseline models beyond conventional extrapolation boundaries. These findings highlight DA’s capacity to improve model calibration, safety margin quantification, and reactor field reconstruction. By integrating high-fidelity simulations with real-world data corrections, the study establishes a scalable pathway to enhance the reliability of nuclear system predictions, emphasizing DA’s role in bridging theoretical models and operational demands without compromising computational efficiency. Full article

Advanced driving technologies have the potential to transform the transportation sector. Specifically, the progress of autonomous vehicles (AVs) has caught the interest of governmental authorities, industrial groups, and academic institutions, with the goal of improving the driving experience, effectiveness, and comfort while also improving safety and flexibility and lowering vehicle emissions. Considering these facts, the purpose of this study is to assess the possible effects and advantages of AVs under diverse traffic situations in urban and rural environments. Knowledge of traffic behavior inside a certain road network is made easier by traffic microsimulation. PTV VISSIM (Verkehr In Städten—SIMulationsmodell) is among the microsimulation software programs that has attracted great interest because of its remarkable capacity to faithfully simulate traffic conditions. This review helps researchers choose the best methodological strategy for their individual study objectives and restrictions while using VISSIM. This research assesses the effect of AVs in different driving behavior and weather conditions in urban and rural situations using VISSIM and introduces traffic safety using the surrogate safety assessment model (SSAM). The study focuses on 10 parameters from the Wiedemann 99 car-following model and speed distribution to establish the correlation between weather conditions and surrogate safety measures (SSMs). The findings could lead to more accurate and authentic models of driving behavior and encourage the automotive industry to further equip AVs to operate efficiently in various environmental and driving conditions. Full article

Intersection sight distance (ISD) requirements, currently designed for driver-operated vehicles (DVs), will be affected once automated vehicles (AVs) enter the driving environment. This paper examines the ISD for intersections with a yield control on a minor road in a mixed DV-AV environment. Five potential conflict types with different ISD requirements are modeled as a minor-road vehicle proceeds to cross the intersection, turns right, or turns left. Furthermore, different models are developed for each conflict type depending on the vehicle types on the minor and major roads. These models, along with the intersection geometry, establish the system demand and supply models for ISD reliability analysis. A surrogate safety measure is developed and used to measure ISD non-compliance and is denoted by the probability of unresolved conflicts (PUC). The models are applied to a case study intersection, where PUC values are estimated using Monte Carlo Simulation and compared to an established target value relating to the DV-only traffic of 0.00674. The results show that AV-related traffic has higher overall PUC values than those of DV-only traffic. A corrective measure, reducing the AV speed limit on the minor-road approaches by 3 to 4 km/h, decreases the overall PUC to values below those of the target PUC. Full article

The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article

Background and Aims: Elevated lipoprotein(a) [Lp(a)] and familial hypercholesterolemia (FH) are both inherited dyslipidemias that are independently associated with cardiovascular disease. Surrogate markers to assess signs of atherosclerosis, such as arterial stiffness, might be useful to evaluate the cardiovascular risk in young patients. The aim of this study is to evaluate the contribution of Lp(a) to arterial stiffness, as measured by carotid pulse wave velocity (cPWV) in young adults with FH. Methods: For this cross-sectional study, 214 children with FH who participated in a randomized controlled trial between 1997 and 1999 on the efficacy and safety of pravastatin were eligible. After 20 years, these patients were invited for a hospital visit, including cPWV assessment (by 4D flow MRI) and Lp(a) measurement. Linear mixed-effects models were used to evaluate the association between Lp(a) and cPWV. Results: We included 143 patients (mean [standard deviation] age: 31.8 [3.2] years) from 108 families. Median (interquartile range) cPWV was 1.62 (1.31–2.06) m/s. Both the unadjusted (ß = −0.0014 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0052 to 0.0023, p = 0.455) and adjusted model (ß = −0.0005 m/s per 1 mg/dL increase in Lp(a), 95% CI: −0.0042 to 0.0032, p = 0.785) showed no significant association between Lp(a) and cPWV. Conclusions: Our findings indicate that Lp(a) levels are not associated with carotid arterial stiffness in young adults with FH. Possibly, High Lp(a) might cause atherosclerosis by mechanisms beyond arterial stiffness in young adults. Other surrogate markers of early signs of atherosclerosis may be more suitable to evaluate the Lp(a)-mediated contribution to atherosclerosis in young FH patients. Full article

As autonomous driving technology advances, connected and autonomous vehicles (CAVs) will coexist with human-driven vehicles (HDVs) for an extended period. The deployment of CAVs will alter traffic flow characteristics, making it crucial to investigate their impacts on mixed traffic. This study develops a hybrid control framework that integrates a platoon control strategy based on the “catch-up” mechanism with lane management for CAVs. The impacts of the proposed hybrid control framework on mixed traffic flow are evaluated through a series of macroscopic simulations, focusing on fundamental diagrams, traffic oscillations, and safety. The results illustrate a notable increase in road capacity with the rising market penetration rate (MPR) of CAVs, with significant improvements under the hybrid control framework, particularly at high MPRs. Additionally, traffic oscillations are mitigated, reducing shockwave propagation and enhancing efficiency under the hybrid control framework. Four surrogate safety measures, namely time to collision (TTC), criticality index function (CIF), deceleration rate to avoid a crash (DRAC), and total exposure time (TET), are utilized to evaluate traffic safety. The results indicate that collision risk is significantly reduced at high MPRs. The findings of this study provide valuable insights into the deployment of CAVs, using control strategies to improve mixed traffic flow operations. Full article

Traumatic brain injury (TBI) during snowboarding sports is a major concern. A robust evaluation of existing snowboarding helmets is desired. Head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and associated brain responses (brain pressure, equivalent (von Mises) stress, and maximum principal strain) of the head are a predominant cause of TBI or concussion. The conventional snowboarding helmet, which mitigates linear acceleration, is typically used in snow sports. However, the role of conventional snowboarding helmets in mitigating angular head kinematics is marginal or insignificant. In recent years, new anti-rotational technologies (e.g., MIPS, WaveCel) have been developed that seek to reduce angular kinematics (i.e., angular velocity, angular acceleration). However, investigations regarding the performance of snowboarding helmets in terms of the mitigation of head kinematics and brain responses are either extremely limited or not available. Toward this end, we have evaluated the performance of snowboarding helmets (conventional and anti-rotational technologies) against blunt impact. We also evaluated the performance of newly developed low-cost, silica-based anti-rotational pads by integrating them with conventional helmets. Helmets were mounted on a head surrogate–Hybrid III neck assembly. The head surrogate consisted of skin, skull, dura mater, and brain. The geometry of the head surrogate was based on the GHBMC head model. Substructures of the head surrogate was manufactured using additive manufacturing and/or molding. A linear impactor system was used to simulate/recreate snowfield hazards (e.g., tree stump, rock, pole) loading. Following the ASTM F2040 standard, an impact velocity of 4.6 ± 0.2 m/s was used. The head kinematics (i.e., linear acceleration, angular velocity, angular acceleration) and brain simulant pressures were measured in the head surrogate. Further, using the concurrent simulation, the brain simulant responses (i.e., pressure, von Mises stress, and maximum principal strain) were computed. The front and side orientations were considered. Our results showed that the helmets with anti-rotation technologies (i.e., MIPS, WaveCel) significantly reduced the angular kinematics and brain responses compared to the conventional helmet. Further, the performance of the silica pad-based anti-rotational helmet was comparable to the existing anti-rotational helmets. Lastly, the effect of a comfort liner on head kinematics was also investigated. The comfort liner further improved the performance of anti-rotational helmets. Overall, these results provide important data and novel insights regarding the performance of various snowboarding helmets. These data have utility in the design and development of futuristic snowboarding helmets and safety protocols. Full article

This study proposes a risk measurement approach to assess collision risks in mixed traffic flow, focusing on the integrated behavior of car-following and lane-changing. A new surrogate safety measure (SSM), denoted as $R_{\mathrm{total}}$, is developed to provide a comprehensive risk assessment. Numerical analysis is used to determine the weights of parameters within $R_{\mathrm{total}}$, and its validity is substantiated using an empirical dataset, with a risk threshold of 0.49 established when the time to collision (TTC) is set to 2 s. The study incorporates scenarios of connected and automated vehicle (CAV) degradation and evaluates the influence of penetration rates, perception–reaction time (PRT), and lane-changing modes on risk levels. Simulation results reveal that a CAV penetration rate between 0.4 and 0.6 represents a critical range where collision risks significantly increase, reflecting safety dynamics under CAV degradation. Furthermore, in scenarios involving lane-changing, the degradation of the following vehicle in the target lane poses the highest risk. At lower PRTs, the penetration rate exerts a more significant influence on collision risks. $R_{\mathrm{total}}$ has been validated across various scenarios, showing strong applicability and more sensitive trends than other SSMs, making it well-suited for assessing long-term comprehensive traffic flow risks. These findings offer practical guidance for traffic management to establish real-time risk prediction and warning systems for identifying high-risk car-following and lane-changing behaviors. Future research can explore the applicability of the proposed risk index in more complex traffic scenarios and its effectiveness across different levels of vehicle automation and connectivity. Full article

Monitoring and predicting the dynamic responses of railway bridges under moving trains, including displacement and acceleration, are vital for evaluating the safety and serviceability of the train–bridge system. Traditionally, finite element analysis methods with high computational burden are used to predict the train-induced responses according to the given train loads and, hence, cannot easily be integrated as an available structural-health-monitoring strategy. Therefore, this study develops a novel framework, combining the train–bridge coupling mechanism and deep learning algorithms to efficiently predict the train-induced bridge responses while considering train load duration. Initially, the feasibility of using neural networks to calculate the train–bridge coupling vibration is demonstrated by leveraging the nonlinear relationship between train load and bridge responses. Subsequently, the instantaneous multiple moving axial loads of the moving train are regarded as the equivalent node loads that excite adjacent predefined nodes on the bridge. Afterwards, a deep long short-term memory (LSTM) network is established as a surrogate model to predict the train-induced bridge responses. Finally, the prediction accuracy is validated using a numerical case study of a simply supported railway bridge. The factors that may affect the prediction accuracy, such as network structure, training samples, the number of structural units, and noise level, are discussed. Results show that the developed framework can efficiently predict the train-induced bridge responses. The prediction accuracy of the bridge displacement is higher than that of the acceleration. In addition, the robustness of the displacement prediction is proven to be better than that of the acceleration with the variation of carriage number, riding speed, and measurement noise. Full article

Due to the transition of vehicle fleets from conventional driver-operated vehicles (DVs) to connected vehicles (CVs) and/or automated vehicles (AVs), vehicles with different technologies will soon operate on the same roads in a mixed-vehicle environment. Although a major goal of vehicle connectivity and automation is to improve traffic safety, negative safety impacts may persist in the mixed-vehicle environment. Speed disparity measures have been shown in the literature to be related to safety performance. Therefore, speed disparity measures are derived from the expected speed distributions of different vehicle technologies and are used as surrogate measures to assess the safety of mixed-vehicle environments and identify the efficacy of prospective countermeasures. This paper builds on speed models in the literature to predict the speed behavior of CVs, AVs, and DVs on horizontal curves on freeways and major arterials. The paper first proposes a methodology to determine speed disparity measures on horizontal curves without any control in terms of speed limit. The impact of speed limit or advisory speed, as a safety countermeasure, is modeled and assessed using different strategies to set the speed limit. The results indicated that the standard deviation of the speeds of all vehicles (${\sigma }_c$) in a mixed environment would increase on arterial roads under no control compared to the case of DV-only traffic. This speed disparity can be reduced using an advisory speed as a safety countermeasure to decrease the adverse safety impacts in this environment. Moreover, it was shown that compared to the practice of a constant speed limit based on road classification, the advisory speed is more effective when it is based on the speed behavior of various vehicle types. Full article

The use of dedicated lanes, known as managed lanes (MLs), on freeways is an established traffic management strategy to reduce congestion. Allowing automated vehicles (AVs) in existing MLs or dedicating MLs for AVs, referred to as AVMLs, has been suggested in the literature as a tool to improve traffic operation and safety performance as AVs and driver-operated vehicles (DVs) coexist in a mixed-vehicle environment. This paper focuses on investigating the safety impacts of deploying AVMLs on freeways by repurposing general-purpose lanes (GPLs). Four ML strategies considering different lane positions and access controls were implemented in a traffic microsimulation under different AV market adoption rates (MARs) and traffic demand levels, and trajectories were used to extract rear-end and lane change conflicts. The time-to-collision (TTC) surrogate safety measure was used to identify critical conflicts using a time threshold dependent on the type of following vehicle. Rates of conflicts involving different vehicle types for all ML strategies were compared to the case of heterogeneous traffic. The results indicated that the rates of rear-end conflicts involving the same vehicle type as the lead and following vehicle, namely DV-DV and AV-AV conflicts, increased with ML implementation as more vehicles of the same type traveled in the same lane(s). By comparing the aggregated conflict rates, the design options that were deemed to negatively impact traffic efficiency and capacity were also found to negatively impact traffic safety. However, other ML options were found to be feasible in terms of traffic operation and safety performance, especially at traffic demand levels below capacity. Specifically, one left-side AVML with continuous access was found to have lower or comparable aggregated conflict rates compared to heterogenous traffic at 25% and 50% MARs, and, thus, it is expected to have positive or neutral safety impacts. Full article

The quality and reliability of wind turbine blades, as core components of wind turbines, are crucial for the operational safety of the entire system. Carbon fiber is the primary material for wind turbine blades. However, during the manufacturing process, manual intervention inevitably introduces minor defects, which can lead to crack propagation under complex working conditions. Due to limited understanding and measurement capabilities of the input variables of structural systems, the distribution parameters of these variables often exhibit uncertainty. Therefore, it is essential to assess the impact of distribution parameter uncertainty on the fatigue performance of carbon-fiber structures with initial cracks and quickly identify the key distribution parameters affecting their reliability through global sensitivity analysis. This paper proposes a sensitivity analysis method based on surrogate sampling and the Kriging model to address the computational challenges and engineering application difficulties in distribution parameter sensitivity analysis. First, fatigue tests were conducted on carbon-fiber structures with initial cracks to study the dispersion of their fatigue life under different initial crack lengths. Next, based on the Hashin fatigue failure criterion, a simulation analysis method for the fatigue cumulative damage life of cracked carbon-fiber structures was proposed. By introducing uncertainty parameters into the simulation model, a training sample set was obtained, and a Kriging model describing the relationship between distribution parameters and fatigue life was established. Finally, an efficient input variable sampling method using the surrogate sampling probability density function was introduced, and a Sobol sensitivity analysis method based on surrogate sampling and the Kriging model was proposed. The results show that this method significantly reduces the computational burden of distribution parameter sensitivity analysis while ensuring computational accuracy. Full article