Search Results (26)

Over 35% of bridges in the United States are currently rated in fair or poor condition, highlighting ongoing challenges in maintaining safety and performance amid aging infrastructure, limited budgets, and extended repair timelines. While Accelerated Bridge Construction (ABC) offers a faster solution, its adoption requires comprehensive decision frameworks. This paper presents a multi-criteria decision support tool (DST) that builds on the Connecticut Department of Transportation (CTDOT) ABC decision matrix. This DST quantifies the benefits of ABC for road and work zone safety, social equity, and environmental justice (SEEJ) and integrates them with structural, traffic, and construction factors to provide a comprehensive approach for determining the suitability of ABC techniques in bridge construction projects. Crash costs and corresponding safety benefits are quantified based on crash severity and frequency. While the tool incorporates both safety and SEEJ criteria, it also allows decision makers to consider either criterion individually based on their preferences. To demonstrate the applicability and benefits of the tool, it was applied to case studies in Connecticut. The results demonstrated how the considerations of safety and SEEJ can affect ABC decision-making. The presented DST is simple (Excel-based) and offers a practical and flexible tool that utilizes readily available data from national databases, making it applicable to all state DOTs across the United States. Full article

Road infrastructure is the foundation of the predominant modes of transport, and its effective management is crucial to meet mobility needs. Although necessary for reconstruction, maintenance, and expansion projects, roadworks produce negative impacts, resulting in further risk for workers and drivers and failing to ensure sustainable development. The objective of this paper is twofold: Firstly, investigate the contributing factors to the occurrence of crashes in roadworks. Secondly, develop a model to estimate crash numbers in these areas. The results, which could support municipalities at the planning stage and implement policies for safe and sustainable development, are achieved by examining 121 sites, where 549 crashes occurred, and 25 contributing factors. The variables are divided into three categories: technical characteristics of the site, infrastructural, and environmental. Besides the conventional variables, a risk-increasing factor is calibrated. It assesses the impact of roadworks according to the manoeuvres imposed and the number of lanes. Consistent with previous findings, several variables related to the work zone layout, traffic conditions, infrastructure, and surrounding environment are correlated with the crash number. After performing a further statistical analysis, a multiple linear regression model, statistically significant (0.000) and suitable for accurately estimating the possible number of crashes (R²_adj = 0.41), is determined. Full article

Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. This study presents an AI-assisted framework for auditing crash data integrity by detecting potentially mislabeled records related to construction zone (czone) involvement. A GPT-3.5 model was fine-tuned using a fusion of structured crash attributes and unstructured narrative text (i.e., multimodal input) to predict work zone involvement. The model was applied to 6400 crash reports to flag discrepancies between predicted and recorded labels. Among 80 flagged mismatches, expert review confirmed four records as genuine misclassifications, demonstrating the framework’s capacity to surface high-confidence labeling errors. The model achieved strong overall accuracy (98.75%) and precision (86.67%) for the minority class, but showed low recall (14.29%), reflecting its conservative design that minimizes false positives in an imbalanced dataset. This precision-focused approach supports its use as a semi-automated auditing tool, capable of narrowing the scope for expert review and improving the reliability of large-scale traffic safety datasets. The framework is also adaptable to other misclassified crash attributes or domains where structured and unstructured data can be fused for data quality assurance. Full article

This study proposes an AI-powered digital twin (DT) platform designed to support real-time traffic risk prediction, decision-making, and sustainable mobility in smart cities. The system integrates multi-source data—including static infrastructure maps, historical traffic records, telematics data, and camera feeds—into a unified cyber–physical platform. AI models are employed for data fusion, anomaly detection, and predictive analytics. In particular, the platform incorporates telematics–video fusion for enhanced trajectory accuracy and LiDAR–camera fusion for high-definition work-zone mapping. These capabilities support dynamic safety heatmaps, congestion forecasts, and scenario-based decision support. A pilot deployment on Madison’s Flex Lane corridor demonstrates real-time data processing, traffic incident reconstruction, crash-risk forecasting, and eco-driving control using a validated Vehicle-in-the-Loop setup. The modular API design enables integration with existing Advanced Traffic Management Systems (ATMSs) and supports scalable implementation. By combining predictive analytics with real-world deployment, this research offers a practical approach to improving urban traffic safety, resilience, and sustainability. Full article

The automotive industry is currently in a paradigm shift transferring the fleet over from internal combustion vehicles to battery electric vehicles (BEV). This introduces new challenges when designing the body-in-white (BIW) due to the sensitive and energy-dense battery that needs to be protected in a crash scenario. Press-hardening steels (PHS) have emerged as an excellent choice when designing crash safety parts due to their ability to be manufactured to complex parts with ultra-high strength. It is, however, crucial to evaluate the crash performance of the selected materials before producing parts. Component testing is cumbersome and expensive, often geometry dependent, and it is difficult to separate the bulk material behaviour from other influences such as spot welds. Fracture toughness measured using the essential work of fracture method is a material property which has shown to be able to rationalise crash resistance of advanced high-strength steel (AHSS) grades and is thereby an interesting parameter in classifying steel grades for automotive applications. However, most of the published studies have been performed at quasi-static loading rates, which are vastly different from the strain rates involved in a crash. These higher strain rates may also lead to adiabatic self-heating which might influence the fracture toughness of the material. In this work, two PHS grades, high strength and very high strength, intended for automotive applications were investigated at lower and higher strain rates to determine the rate-dependence on the conventional tensile properties as well as the fracture toughness. Both PHS grades showed a small increase in conventional mechanical properties with increasing strain rate, while only the high-strength PHS grade showed a significant increase in fracture toughness with increasing loading rate. The adiabatic heating in the fracture process zone was estimated with a high-speed thermal camera showing a significant temperature increase up to 300 °C. Full article

Background: The rise in work zone crashes due to distracted and aggressive driving calls for improved safety measures. While Truck-Mounted Attenuators (TMAs) have helped reduce crash severity, the increasing number of crashes involving TMAs shows the need for improved warning systems. Methods: This study proposes an AI-enabled vision system to automatically alert drivers on collision courses with TMAs, addressing the limitations of manual alert systems. The system uses multi-task learning (MTL) to detect and classify vehicles, estimate distance zones (danger, warning, and safe), and perform lane and road segmentation. MTL improves efficiency and accuracy, making it ideal for devices with limited resources. Using a Generalized Efficient Layer Aggregation Network (GELAN) backbone, the system enhances stability and performance. Additionally, an alert module triggers alarms based on speed, acceleration, and time to collision. Results: The model achieves a recall of 90.5%, an mAP of 0.792 for vehicle detection, an mIOU of 0.948 for road segmentation, an accuracy of 81.5% for lane segmentation, and 83.8% accuracy for distance classification. Conclusions: The results show the system accurately detects vehicles, classifies distances, and provides real-time alerts, reducing TMA collision risks and enhancing work zone safety. Full article

The presence of highway work zones has a major effect on safety, mobility, and project expenses. The objective of this study is to develop a multi-objective optimization model to address these challenges by considering all three factors simultaneously. The model employs a Genetic Algorithm to identify the Pareto front and elucidate the trade-offs between safety, mobility, and cost. It evaluates various decision variables related to site geometry, work management, and temporary traffic control measures, exploring numerous potential combinations and offering decision-makers a comprehensive array of solutions. A case study demonstrates the model’s efficacy. Initially, approximately 829,440 feasible solutions were identified, which were effectively reduced to 263 by imposing additional constraints such as specific safety levels, maximum project costs, or traffic delay thresholds. The findings highlight significant cost variations: crash costs ranged from saving USD 973,473 to increasing costs by USD 1,328,322; mobility costs ranged from USD 184,491 to USD 3,854,212; and project costs ranged from USD 1,424,634 to USD 1,574,894. These variations underscore the substantial influence of crash costs and the benefits of location-based scheduling, which improves cost estimate reliability by capturing the effects of working hours and project duration. This research builds upon previous studies by incorporating three distinct objectives rather than focusing on a singular solution. By addressing safety, mobility, and project cost separately, the framework yields multiple solutions, each impacting the objectives differently. This multifaceted approach enhances its utility as a robust decision-making tool for stakeholders involved in highway work zone management and planning. This study concludes that multi-objective optimization is crucial for providing realistic and diverse solutions, ultimately improving decision-making processes in highway work zone operations. Full article

Road crashes, although random events, frequently occur on roads. However, certain characteristic infrastructural locations require detailed analysis regarding the frequency of road crashes. This study examines the dynamics of road crashes at characteristic infrastructural locations in Serbia from 2018 to 2022, focusing on bridges, tunnels, railroad crossings, and road work zones. Using data on road crashes from official reports, the analysis includes trends in crash rates, fatalities, injuries, and material damage during the above-mentioned time frame. In addition to the data analysis, 22 experts from the fields of traffic engineering ranked the mentioned characteristic infrastructural locations in terms of road safety. The same questions were asked to six different artificial intelligence software programs. The findings reveal significant variations in crash rates across different infrastructures, with bridges and road work zones having the highest number of crashes. Expert assessment is in line with the analysis of the results, while artificial intelligence gives a completely opposite assessment. Full article

Examining the factors contributing to work zone crashes and implementing measures to reduce their occurrence can significantly improve road safety. In this research, we utilized the explainable boosting machine (EBM), a modern glass-box machine learning (ML) model, to categorize and predict work zone-related crashes and to interpret the various contributing factors. The issue of data imbalance was also addressed by utilizing work zone crash data from the state of New Jersey, comprising data collected over the course of two years (2017 and 2018) and applying data augmentation strategies such synthetic minority over-sampling technique (SMOTE), borderline-SMOTE, and SVM-SMOTE. The EBM model was trained using augmented data and Bayesian optimization for hyperparameter tuning. The performance of the EBM model was evaluated and compared to black-box ML models such as combined kernel and tree boosting (KTBoost, python 3.7.1 and KTboost package version 0.2.2), light gradient boosting machine (LightGBM version 3.2.1), and extreme gradient boosting (XGBoost version 1.7.6). The EBM model, using borderline-SMOTE-treated data, demonstrated greater efficacy with respect to precision (81.37%), recall (82.53%), geometric mean (75.39%), and Matthews correlation coefficient (0.43). The EBM model also allows for an in-depth evaluation of single and pairwise factor interactions in predicting work zone-related crash severity. It examines both global and local perspectives, and assists in assessing the influence of various factors. Full article

The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting. Full article

This study investigates factors contributing to the injury severity of truck-involved work zones crashes in South Carolina (SC). The outcome of interest is injury or property damage only crashes, and the explanatory factors examined include the occupant, vehicle, collision, roadway, temporal, and environmental characteristics. Two mixed (random parameter) logit models are developed, one for non-interstates with speed limits less than 60 miles per hour (mph) and one for interstates with speed limits greater than or equal to 60 mph, using South Carolina statewide truck-involved work zone crash data from 2014 to 2020. Results of log-likelihood ratio tests indicate that separate speed models are warranted. The factors that were found to contribute to injury at the 90% confidence level in both models (interstate and non-interstate) are (1) dark lighting conditions, (2) female (at-fault) drivers, and (3) driving too fast for roadway conditions. Significant factors that apply only to non-interstates are SC or US primary roadways, activity area of the work zone, at-fault drivers under 35, sideswipe collision, presence of workers in the work zone, and collision with fixed objects. Significant factors that apply only to interstates are three or more vehicles, rear-end collision, location before the first work zone sign, and weekdays. Full article

Using motorcycle crash data from 2016 to 2019, this paper aims to uncover and compare the risk factors that influence the severity of motorcyclist injuries sustained in daytime and nighttime motorcycle crashes in Thailand. Mixed-ordered probit models with means and variances in heterogeneity were used to take into consideration unobserved heterogeneity. The temporal instability of risk factors was also extensively explored. The results show that male motorcyclists, speeding, fatigue, crashes in work zones, crashes on raised median roads, intersection-related crashes, crashes on wet roads, and crashes on unlit roads are all factors that are positively associated with the risk of death and serious injury in nighttime crashes. The presence of pillions, crashes on two-lane roads, crashes on depressed/flush median roads, crashes in rural areas, U-turn-related crashes, weekend crashes involving heavy vehicles, and head-on crashes are factors that were positively associated with risk of death and serious injury for both daytime and nighttime crashes. This study’s findings provide evidence that factors that influence motorcycle accidents during the daytime and nighttime vary significantly. Additionally, nighttime crashes typically carried a higher risk of fatalities or serious injuries compared to daytime crashes. A discussion of policy recommendations is also provided. Full article

The sustainable development goals “Good health and well-being” and “Sustainable cities and communities” of the United Nations and World Health Organization, alert governments and researchers and raise awareness about road safety problems and the need to mitigate them. In Portugal, after the economic crisis of 2008–2013, a significant amount of road assets demand investment in maintenance and rehabilitation. The areas where these actions take place are called work zones. Considering the particularities of these areas, the proposed work aims to identify the main factors that impact the occurrence of work zones crashes. It uses the statistical technique of multinomial logistic regression, applied to official data on road crashes occurred in mainland Portugal, during the period of 2010–2015. Usually, multinomial logistic regression models are developed for crash and injury severity. In this work, the feasibility of developing predictive models for crash nature (collision, run off road and running over pedestrians) and for type of person involved in the crash (driver, passenger and pedestrian), considering only one covariate (the number of persons involved in the crash), was studied. For the two predictive models obtained, the variables road environment (urban/rural), horizontal geometric design (straight/curve), pavement grip conditions (good/bad), heavy vehicle involvement, and injury severity (fatalities, serious and slightly injuries), were identified as the preponderant factors in a universe of 230 investigated variables. Results point to an increase of work zone crash probability due to driver actions such as running straight and excessive speed for the prevailing conditions. Full article

Work zone areas are commonly known as crash-prone areas. Thus, they usually receive high priority by road operators as drivers and workers have higher chances of being involved in road crashes. The paper aims to investigate driving behavior in work zones using unsupervised machine learning and vehicle kinematic data. A dataset of 67 participants was gathered through an experiment using a driving simulator located at the Qatar Transportation and Traffic Safety Center (QTTSC). The study considered two different work zone scenarios where the leftmost lane was closed for maintenance. In the first scenario, drivers drove on the leftmost lane (Drive 1), while in the second, they drove on the second leftmost lane (Drive 2). The results show that the number of aggressive and conservative drivers was surprisingly more than normal drivers, as most participants either cautiously drove through or failed to drive without being aggressive. The results also show that drivers acted more aggressively in the leftmost lane rather than in the second leftmost lane. We also found that female drivers and drivers with relatively little driving experience were more likely to be aggressive as they drove through a work zone. The framework was found to be promising and can help policymakers take optimal safety countermeasures in work zones during construction. Full article

An electric heavy quadricycle, categorized as an L7e vehicle, is an alternative solution for sustainable mobility with a lower carbon footprint and high energy consumption efficiency. However, accidental crashes of quadricycles with larger vehicle opponents can cause extensive damage to their structures and fatal injury to the occupants due to their geometry drawback in limited space in the front crumple zone. This work investigates the crashworthiness performance and safety assessment of the L7e vehicle under rigid wall crash tests and crash compatibility in car-to-car collisions with a sedan and an SUV. Crash scenarios are simulated using a nonlinear finite element analysis via LS-DYNA to evaluate structural crashworthiness and occupant injuries of a hybrid III 50th percentile male dummy. The compatible vertical alignment of the primary energy-absorbing structure substantially affects the safety of the quadricycle under a frontal crash. A secondary energy-absorbing component should be adapted to the L7e vehicle to achieve vertical alignment with different vehicle sizes. In addition, the typical rigid-wall frontal crash test at 50 kph considerably underestimates the structural damage and occupant injury of the L7e vehicle compared to car-to-car collisions. Thus, additional crash tests representing car-to-car collisions that account for the car’s smaller size and lighter mass should be included in the safety regulation for the L7e vehicle. Full article