Search Results (121)

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

Background: Surgical site infections (SSIs) are among the most common postoperative complications. Environmental factors, including intraoperative traffic and door openings in the operating room (OR), have been identified as critical contributors to microbial air contamination. Nurses play a pivotal role in managing these factors, directly influencing infection control practices. Methods: An integrative review was conducted to synthesize current evidence on the association between intraoperative traffic, door openings, and SSIs. A structured methodology was employed to identify, assess, and analyze the existing literature, with a specific focus on the nursing role in infection prevention. Results: Findings from a single-center prospective cohort study indicate that ORs with more than 10 personnel present exhibit a threefold increase in SSI risk [Relative Risk (RR) = 3.12; 95% Confidence Interval (CI): 0.71–13.66] compared to ORs with fewer personnel. Additionally, every five door openings per procedure were associated with a significant increase in SSI incidence [Hazard Ratio (HR) = 2.00; 95% CI: 1.24–3.20, p = 0.005]. Conclusions: These findings underscore the importance of strict protocols to limit intraoperative traffic and unnecessary OR access. A multidisciplinary approach plays a crucial role in ensuring surgical safety and preventing SSIs by regulating OR access and adhering to infection control best practices. Full article

Wildlife strikes in aviation are among the most reported safety incidents. As such, strikes have become the fundamental unit of understanding of the risk posed by wildlife. However, the management of wildlife risks to aviation has shifted to a hazard management philosophy. This literature review examines the argument that current wildlife strike reporting requirements are inadequate for modern wildlife hazard management techniques. This review utilised bibliometric analysis software to identify relevant academic research sourced from the Web of Science, as well as industry materials, to compile a final catalogue (n = 542). Further filtering revealed a limited set of relevant papers (n = 42) and even fewer papers that addressed the above question. Analysis of these papers and the wider catalogue noted limitations in current reporting requirements as they relate to hazard and risk management concepts. This analysis was supplemented with a review of international standards and relevant national requirements, concluding that while academics and industry have adopted systematic safety and hazard management techniques, and international guidance material has kept pace, international standards, the foundation for many national reporting systems, remain decades behind. This paper proposes the use of robust consensus-building methodologies, such as the Delphi technique, in the industry as a means of streamlining and supporting international standards development. Full article

Digital Twins (DTs) are becoming essential tools for real-time decision-making in transportation systems. This paper presents a macroscopic traffic digital twin developed for a 50 km segment of the C-32 interurban highway in Spain. The digital twin replicates highway conditions using real-time data from roadside sensors and connected vehicles via Vehicle-to-Everything (V2X) communications. It supports intelligent decision-making for traffic management, particularly during incident situations, by recommending macroscopic strategies such as variable speed limits and re-routing. Unlike many existing DTs focused on microscopic modeling or urban contexts, our approach emphasizes a macroscopic scale suitable for interurban highways, enabling faster computation and system-wide insights. The decision-making module evaluates candidate strategies using real-time simulations and selects the most effective option based on key performance indicators (KPIs), including congestion, travel time, and emissions. The system has been validated under realistic traffic scenarios using historical data, considering both congestion and pollution use cases. Strategies are communicated back to the physical infrastructure via V2I messages (IVIM) and a mobile application using the cellular communication network, enabling a closed-loop architecture. This paper contributes a scalable, real-time, and field-integrated macroscopic DT framework for highway traffic management. Full article

In the past few decades, extensive research has been conducted on the modeling of cascading failures and their recovery processes in freeway networks. In practice, the restoration of functionality and structure in complex networks that suffer large-scale cascading failures may involve a series of repair operations. In this paper, we first propose a cascading failure model for freeway networks, which considers load redistribution by taking travelers’ choice behavior into account. Specifically, we use the Stochastic User Equilibrium (SUE) as a method for redistribution in the model. Next, we propose a recovery strategy focused on critical edges, with their importance ranked through the integration of the network’s topological features and traffic characteristics. This ranking then serves as the foundation for the edge-recovery process. This model considers the operational mechanisms of complex freeway networks. In the experiment, we used the freeway network in Hunan Province as a case study to validate the effectiveness of our model. Traffic volume data were collected from toll stations on the freeway network, and the topological structure of the network was combined with these data to construct a complex weighted freeway network. The evolution of network cascading failures was analyzed under various scenarios of attacks caused by traffic incidents. Subsequently, the failed network was recovered, and the results indicate that the proposed recovery strategy demonstrates better performance compared to other traditional methods. This research provides theoretical and methodological support for the management of freeway networks. Full article

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article

Water transportation is a critical component of the overall transportation system. However, the gradual increase in traffic density has led to a corresponding rise in accident occurrences. This study proposes a quantitative framework for analyzing the evolutionary paths of maritime traffic accident risks by integrating complex network theory and link prediction methods. First, 371 maritime accident investigation reports were analyzed to identify the underlying risk factors associated with such incidents. A risk evolution network model was then constructed, within which the importance of each risk factor node was evaluated. Subsequently, several node similarity indices based on node importance were proposed. The performance of these indices was compared, and the optimal indicator was selected. This indicator was then integrated into the risk evolution network model to assess the interdependence between risk factors and accident types, ultimately identifying the most probable evolution paths from various risk factors to specific accident outcomes. The results show that the risk evolution path shows obvious characteristics: “lookout negligence” is highly correlated with collision accidents; “improper route selection” plays a critical role in the risk evolution of grounding and stranding incidents; “improper on-duty” is closely linked to sinking accidents; and “illegal operation” show a strong association with fire and explosion events. Additionally, the average risk evolution paths for collisions, groundings, and sinking accidents are relatively short, suggesting higher frequencies of occurrence for these accident types. This research provides crucial insights for managing water transportation systems and offers practical guidance for accident prevention and mitigation. Full article

Effective incident detection is essential for emergency response and transportation management. Traditional methods relying on stationary technologies are often costly and provide limited coverage, prompting the exploration of crowdsourced data such as Waze. While Waze offers extensive coverage, its data can be unverified and unreliable. This study aims to identify factors affecting the reliability of Waze alerts and develop a predictive model to distinguish true incidents from false alerts using real-time Waze data, thereby improving emergency response times. Real crash data from the New Jersey Department of Transportation (NJDOT) and crowdsourced data from Waze were matched using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to differentiate true and false alerts. A binary logit model was constructed to reveal significant predictors such as time categories around peak hours, road type, report ratings, and crash type. Findings indicate that the likelihood of accurate Waze alerts increases during peak hours, on streets, and with higher report ratings and major crashes. Additionally, multiple machine learning-based predictive models were developed and evaluated to forecast in real time whether Waze alerts correspond to actual incidents. Among those models, the Random Forest model achieved the highest overall accuracy (82.5%) and F1-score (82.8%), and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.90, demonstrating its robustness and reliability for real-time incident detection. Gradient Boosting, with an AUC-ROC of 0.90 and Area Under the Precision–Recall Curve (AUC-PR) of 0.90, also performed strongly, particularly excelling at predicting true alerts. The analysis further emphasized the importance of key predictors such as time of day, report ratings, and road type. These findings provide actionable insights for enhancing the accuracy of incident detection and improving the reliability of crowdsourced traffic alerts, supporting more effective traffic management and emergency response systems. Full article

Maritime safety is a critical concern for the transport sector and remains a key challenge for the international shipping industry. Recognizing that maritime accidents pose significant risks to both safety and operational efficiency, this study explores the application of big data analysis techniques to understand the factors influencing maritime transport accidents (MTA). Specifically, using extensive datasets derived from vessel performance measurements, environmental conditions, and accident reports, it seeks to identify the key intrinsic and extrinsic factors contributing to maritime accidents. The research examines more than 90 thousand incidents for the period 2014–2022. Leveraging big data analytics and advanced statistical techniques, the findings reveal significant correlations between vessel size, speed, and specific environmental factors. Furthermore, the study highlights the potential of big data analytics in enhancing predictive modeling, real-time risk assessment, and decision-making processes for maritime traffic management. The integration of big data with intelligent transportation systems (ITSs) can optimize safety strategies, improve accident prevention mechanisms, and enhance the resilience of ocean-going transportation systems. By bridging the gap between big data applications and maritime safety research, this work contributes to the literature by emphasizing the importance of examining both intrinsic and extrinsic factors in predicting maritime accident risks. Additionally, it underscores the transformative role of big data in shaping safer and more efficient waterway transportation systems. Full article

Background and Objectives: Maxillofacial trauma represents a significant global health challenge with substantial physical, psychological, and socioeconomic consequences. Materials and Methods: This narrative review analyzed 112 articles published between 2000 and 2024 examining epidemiology, prevention, economics, and outcomes of maxillofacial trauma in critical care settings. Results: Road traffic accidents remain the primary cause globally, followed by interpersonal violence and occupational injuries. Effective prevention strategies include seat belt laws, helmet legislation, and violence prevention programs. Economic burden encompasses direct healthcare costs (averaging USD 55,385 per hospitalization), productivity losses (11.8 workdays lost per incident), and rehabilitation expenses (USD 3800–18,000 per patient). Surgical management has evolved toward early intervention, minimally invasive approaches, and advanced techniques using computer-aided design and 3D printing. Complications affect 3–33% of patients, with significant functional disabilities and psychological sequelae (post-traumatic stress disorder in 27%, depression/anxiety in 20–40%). Conclusion: Maxillofacial trauma management requires multidisciplinary approaches addressing both immediate treatment and long-term rehabilitation. Despite technological advances, disparities in specialized care access persist globally. Future efforts should implement evidence-based prevention strategies, reduce care disparities, and develop comprehensive approaches addressing physical, psychological, and socioeconomic dimensions through collaboration among healthcare professionals, policymakers, and community stakeholders. Full article

Today’s cities are facing the challenges of increasing traffic congestion, emissions, and the need to improve road safety. The solution to these problems is the use of artificial intelligence (AI) and the Internet of Things (IoT) in intelligent traffic management. The purpose of the article is to analyze and evaluate AI- and IoT-based solutions implemented in Polish cities and to identify innovative proposals that can improve traffic management. The study uses a mixed-method approach, including the analysis of crowdsourced mobility data (from GPS, smartphones, and municipal reports), GIS tools for mapping congestion, big data analytics, and machine learning algorithms, to evaluate trends and predict traffic scenarios. The evaluation focused on seven major Polish cities—Warsaw, Krakow, Wroclaw, Gdansk, Poznan, Katowice, and Lodz—where intelligent transportation systems such as dynamic traffic lights, intelligent pedestrian crossings, accident prediction systems, and parking space management have been implemented. The effectiveness of these solutions was assessed using the following six key indicators: waiting time at intersections, travel time, congestion level, CO₂ emissions, energy consumption, and number of traffic incidents. The article provides a comprehensive analysis of these solutions’ impacts on traffic flow, emissions, energy efficiency, and road safety. A key contribution of the paper is the presentation of new proposals for improvements, such as the inclusion of behavioral data in traffic modeling, integration with GPS navigation, and dynamic emergency and public transport priority management. The article also discusses further digitization and interoperability needs. The findings show that the implementation of intelligent transportation systems not only improves urban mobility and safety but also enhances environmental sustainability and residents’ quality of life. 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 primary purpose of video surveillance is to monitor public indoor areas or the boundaries of secure facilities to safeguard them against theft, unauthorized access, fire, and various other potential threats. Security cameras, equipped with integrated video surveillance systems, are strategically placed throughout critical locations on the premises, allowing security personnel to observe all areas for specific behaviors that may signal an emergency or a situation requiring intervention. A significant challenge arises from the fact that individuals cannot maintain focus on multiple screens simultaneously, which can result in the oversight of crucial incidents. In this regard, artificial intelligence (AI) video analytics has become increasingly prominent, driven by numerous practical applications that include object identification, detection of unusual behavior patterns, facial recognition, and traffic management. Recent advancements in this technology have led to enhanced functionality, remarkable accuracy, and reduced costs for consumers. There is a noticeable trend towards upgrading security frameworks by incorporating AI into pre-existing video surveillance systems, thus leading to modern video surveillance that leverages video analytics, enabling the detection and reporting of anomalies within mere seconds, thereby transforming it into a proactive security solution. In this context, the AiWatch system introduces digital twin (DT) technology in a modern video surveillance architecture to facilitate advanced analytics through the aggregation of data from various sources. By exploiting AI and DT to analyze the different sources, it is possible to derive deeper insights applicable at higher decision levels. This approach allows for the evaluation of the effects and outcomes of actions by examining different scenarios, hence yielding more robust decisions. Full article

This study quantified the effects of seven proposed traffic management strategies (MS) to leverage the synergy between Active Traffic Management (ATM) and connected and automated vehicles (CAV) to mitigate congestion, reduce queue lengths, and improve travel time after incident occurrence. First, three proposed MS are discussed: (a) controlling speed limit but not restricting lane changes, (b) directing CAV to change lanes earlier, and (c) restricting CAV in open lanes from lane changes near incidents. Then, combinations of these strategies are presented. At 10% CAV MP, MS1 that focuses on longitudinal control reduced travel time by 11.6% compared to 1.9% with no MS. Similarly, MS2, which directs CAV to change lanes earlier, were most effective when applied at 1-mile upstream of the incident site, achieving a notable 6.0% travel time reduction compared to 1.9% with no MS. The beneficial impact of MS3, which restricts CAV in open lanes from making lane changes near incident sites, became more pronounced with increasing CAV MP. Among the combined strategies (MS4 to MS7), some strategies proved more effective than others. Findings from Vissim simulation runs showed the importance of combining CAV and MS. Full article