Search Results (34)

Background: Renal trauma accounts for approximately 3–5% of all trauma cases, predominantly affecting young males. The most common etiology is blunt trauma, particularly due to road traffic accidents, and it frequently occurs as part of polytrauma involving multiple organ systems. Management strategies are primarily dictated by hemodynamic stability, overall clinical condition, comorbidities, and injury severity graded according to the AAST classification. This study aimed to evaluate the effectiveness of non-operative management (NOM) in high-grade renal trauma (AAST grades III–V), beyond its established role in low-grade injuries (grades I–II). Secondary endpoints included the identification of independent prognostic factors for NOM failure and in-hospital mortality. Methods: We conducted a retrospective observational study including patients diagnosed with blunt renal trauma who presented to the Emergency Department of Policlinico Umberto I in Rome between 1 January 2013 and 30 April 2024. Collected data comprised demographics, trauma mechanism, vital signs, hemodynamic status (shock index), laboratory tests, blood gas analysis, hematuria, number of transfused RBC units in the first 24 h, AAST renal injury grade, ISS, associated injuries, treatment approach, hospital length of stay, and mortality. Statistical analyses, including multivariable logistic regression, were performed using SPSS v28.0. Results: A total of 244 patients were included. Low-grade injuries (AAST I–II) accounted for 43% (n = 105), while high-grade injuries (AAST III–V) represented 57% (n = 139). All patients with low-grade injuries were managed non-operatively. Among high-grade injuries, 124 patients (89%) were treated with NOM, including observation, angiography ± angioembolization, stenting, or nephrostomy. Only 15 patients (11%) required nephrectomy, primarily due to persistent hemodynamic instability. The overall mortality rate was 13.5% (33 patients) and was more closely associated with the overall injury burden than with renal injury severity. Multivariable analysis identified shock index and active bleeding on CT as independent predictors of NOM failure, whereas ISS and age were significant predictors of in-hospital mortality. Notably, AAST grade did not independently predict either outcome. Conclusions: In line with the current international literature, our study confirms that NOM is the treatment of choice not only for low-grade renal injuries but also for carefully selected hemodynamically stable patients with high-grade trauma. Our findings highlight the critical role of physiological parameters and overall ISS in guiding management decisions and underscore the need for individualized assessment to minimize unnecessary nephrectomies and optimize patient outcomes. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of “dormant” ETC data contains rich traffic information that urgently needs to be deeply mined and effectively utilized. This paper reviews the research status, key technologies, and development trends of urban traffic state sensing and analysis technologies based on ETC data. In terms of technological development, ETC systems have evolved from simple toll collection tools to comprehensive traffic management platforms, featuring unique advantages such as accurate vehicle identification, extensive spatiotemporal coverage, and stable data quality. ETC data-based traffic sensing technologies encompass traffic state representation at microscopic, mesoscopic, and macroscopic levels, enabling comprehensive sensing from individual vehicle behavior to overall network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. In practical applications, ETC data has demonstrated enormous potential in real-time monitoring and signal control optimization, traffic prediction and artificial intelligence technologies, environmental impact assessment, and other fields. Meanwhile, ETC data-based urban traffic management is transitioning from passive responses to proactive prediction, from single functions to comprehensive services, and from isolated systems to integrated platforms. Looking toward the future, the deep integration of emerging technologies, such as vehicle–road networking, edge computing, and artificial intelligence, with ETC systems will further promote the intelligent, refined, and precise development of urban traffic management. Full article

With the rapid development of intelligent transportation systems, obtaining vehicle status information across large-scale road networks is essential for the coordinated management and control of traffic conditions. Distributed Acoustic Sensing (DAS) demonstrates considerable potential in vehicle status perception due to its characteristics such as high spatial resolution and robustness in complex sensing environments. This study first reviews the limitations of conventional vehicle detection technologies and introduces the operating principles and technical features of DAS. Secondly, it investigates the correlations between DAS sensing characteristics, deployment process, and driving behavior characteristics. The results indicate that both the intensity of driving behavior and the degree of deployment–process coupling are positively associated with DAS signal sensing characteristics. This study further examines the principles, advantages, limitations, and application scenarios of various DAS signal processing algorithms. Traditional methods are becoming less effective in handling massive data generated by numerous distributed nodes. Although deep learning achieves high classification accuracy and low latency, its generalization capability remains limited. Finally, this study discusses DAS-based traffic status perception frameworks and outlines key research frontiers in vehicle status monitoring using DAS technology. Full article

In response to the challenges of imbalanced economic efficiency of charging stations caused by disorderly charging of large-scale electric vehicles (EVs), rising electricity expenditure of users, and increased risk of stable operation of the power grid, this study designs a user-side vehicle pile resource interaction strategy considering source load clustering to enhance the economy and safety of electric vehicle energy management. Firstly, by constructing a dynamic traffic flow distribution network coupling architecture, a bidirectional interaction model between charging facilities and transportation/power systems is established to analyze the dynamic correlation between charging demand and road network status. Next, an EV charging and discharging electricity price response model is established to quantify the load regulation potential under different scenarios. Secondly, by combining urban transportation big data and prediction networks, high-precision inference of the spatiotemporal distribution of charging loads can be achieved. Then, a multidimensional optimization objective function covering operator revenue, user economy, and grid power quality is constructed, and a collaborative decision-making model is established. Finally, the IEEE69 node system is validated through joint simulation with actual urban areas, and the non-dominated sorting genetic algorithm II (NSGA-II) based on reference points is used for the solution. The results show that the optimization strategy proposed by NSGA-II can increase the operating revenue of charging stations by 33.43% while reducing user energy costs and grid voltage deviations by 18.9% and 68.89%, respectively. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

Buses constitute a crucial component of public transportation systems in numerous urban centers. Integrating autonomous driving technology into the bus transportation ecosystem has the potential to enhance overall urban mobility. The management of mixed traffic at intersections, involving both private vehicles and buses, particularly in the presence of bus lanes, presents several formidable challenges. This study proposes a preemptive-level-based cooperative autonomous vehicle (AV) trajectory optimization for intersections with mixed traffic. It takes into account dynamic changes in the intersection’s passing sequence, trajectory selection, and adherence to traffic regulations, including the different status of bus lanes. Based on the spatio–temporal coupling constraints of each vehicle trajectory at intersections, a preemptive-level-based AV passing order optimization method is proposed. Subsequently, a speed control mechanism is introduced to decouple these constraints, thereby preventing vehicle conflicts and reducing unnecessary braking. Ultimately, trajectory routes for multi-exit roads are selected, prioritizing traffic efficiency. In simulated validations, two representative types of intersections from the actual road network were selected, and eight typical scenarios established, including the operation status of bus lanes and different percentages of buses. The results indicate that the proposed method improves intersection traffic efficiency by a minimum of 12.55%, accompanied significantly by reduction of fuel consumption by 8.93%. This study verified that the proposed method significantly enhances intersection efficiency and reduces energy consumption while ensuring safety. Full article

Rainstorms and flooding are among the most common natural disasters, which have a number of impacts on the transport system. This reality highlights the importance of understanding resilience—the ability of a system to resist disruptions and quickly recover to operational status after damage. However, current resilience assessments often overlook transport network functions and lack dynamic spatiotemporal analysis, posing challenges for comprehensive disaster impact evaluations. This study proposes an SR-PR-FR comprehensive resilience evaluation model from three dimensions: structure resilience (SR), performance resilience (PR), and function resilience (FR). Moreover, a simulation model based on Geographic Information System (GIS) and Simulation of Urban MObility (SUMO) is developed to analyze the dynamic spatial–temporal effects of a rainstorm on traffic during Xi’an’s evening rush hour. The results reveal that the southwest part of Xi’an is most prone to being congested and slower to recover, while downtown flooding is the deepest, severely affecting emergency services’ efficiency. In addition, the road network resilience returns to 70% of the normal values only before the morning rush the next day. These research results are presented across both temporal and spatial dimensions, which can help managers propose more targeted recommendations for strengthening urban risk management. Full article

With the rapid development of electric vehicles, the infrastructure for charging stations is also expanding quickly, and the failure rate of charging piles is increasing. To address the effective operation and maintenance of charging stations, a method based on the XGBoost algorithm for electric vehicle DC charging stations is proposed. An operation and maintenance system is constructed based on state analysis, considering the operational status of the charging stations and users’ charging habits. Factors such as driving and charging habits, road traffic, and charging station equipment are taken into account. The training sample data are established using historical data, online monitoring data, and external environmental data, and the charging station status evaluation model is trained using the XGBoost algorithm. Based on the condition assessment results, a risk assessment model is established in combination with fault parameters. Risk tracking of the charging stations is conducted using the energy not charged (ENC), evaluating the risk level of each station and determining the operation and maintenance order. The optimal operation and maintenance model for DC charging stations, aimed at achieving both economic and reliability goals, is constructed to determine the operation and maintenance schedule for each station. The results of the case study demonstrate that the state evaluation and operation and maintenance strategy can significantly improve the reliability of the system and the overall benefits of operation and maintenance while meeting the required standards. Full article

As an important component of road traffic facilities, bridges play a crucial role in daily traffic operations, and changes in their status can have an impact on traffic operation. The existing research mainly focuses on monitoring the status of bridges themselves or analyzing the operation status of road traffic, and rarely considers the changes in traffic operation status caused by changes in bridge status. Therefore, in order to evaluate the impact relationship between the two, this article designs an algorithm that combines the Analytic Hierarchy Process (AHP), the Delphi method, and Singular Value Decomposition (SVD) based on the traditional evaluation of bridges and road traffic operation status, and establishes a bridge traffic operation status impact assessment model. Then, simulation analysis and actual data verification will be conducted based on the specific situation of Ma’anshi Bridge on the Chongqing Wuhan Expressway. The experimental results show that the evaluation model established in this paper conforms to the characteristics of traffic operation, can reflect the impact of bridge state changes on traffic operation status well, effectively promote the automation level of bridge traffic impact management, and has high reliability and accuracy. Full article

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications. Full article

Satellite remote sensing technology significantly aids road traffic monitoring through its broad observational scope and data richness. However, accurately detecting micro-vehicle targets in satellite imagery is challenging due to complex backgrounds and limited semantic information hindering traditional object detection models. To overcome these issues, this paper presents the NanoSight–YOLO model, a specialized adaptation of YOLOv8, to boost micro-vehicle detection. This model features an advanced feature extraction network, incorporates a transformer-based attention mechanism to emphasize critical features, and improves the loss function and BBox regression for enhanced accuracy. A unique micro-target detection layer tailored for satellite imagery granularity is also introduced. Empirical evaluations show improvements of 12.4% in precision and 11.5% in both recall and mean average precision (mAP) in standard tests. Further validation of the DOTA dataset highlights the model’s adaptability and generalization across various satellite scenarios, with increases of 3.6% in precision, 6.5% in recall, and 4.3% in mAP. These enhancements confirm NanoSight–YOLO’s efficacy in complex satellite imaging environments, representing a significant leap in satellite-based traffic monitoring. Full article

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network’s operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model’s correlation. Full article

To improve the simulation accuracy and efficiency of microscopic urban traffic, a unified modeling method considering the behavioral characteristics of vehicle drivers is proposed by considering the lane-changing vehicles on the inlet lanes of signalized intersections and their approach following vehicles on the target lanes as research objects. Based on the driver’s multidirectional, multi-vehicle anticipation ability and introducing lateral vehicle influence coefficients, the full velocity difference car-following model was extended to microscopic traffic models that consider the driver’s capacity for multi-directional, multi-vehicle anticipation. The extended model can describe longitudinal movements of lane changing and car followers using lateral vehicle influential parameters. The influences of traffic control signals and the type of lane change on drivers’ decisions were integrated into the model by reformulating the optimal velocity function of the basic car following the model. Similar modeling methods and components were applied to formulate four groups of experimental models and one group of test models. Vehicle trajectory data and manual observations were collected on urban arteries to calibrate and evaluate the research models, experimental models, and test models. The results show that the car-following behavior is more sensitive to the variation in the status of the lateral moving vehicle and change of lane-changing type compared to lane-changing behavior during the lane-changing process. In addition, when lane changing gradually encroaches on the target lane, the vehicle observes the driving conditions and adjusts its driving behaviors differently. This research helps to analyze travel characteristics and influence mechanisms of vehicles on urban roads, which is a guide for the future development of sustainable transportation and self-driving vehicles and promoting the efficient operation of urban transportation systems. Full article

As online shopping is becoming mainstream, driven by the social impact of Coronavirus disease-2019 (COVID-19) as well as the development of Internet services, the demand for autonomous delivery mobile robots is rapidly increasing. This trend has brought the autonomous mobile robot market to a new turning point, with expectations that numerous mobile robots will be driving on roads with traffic. To achieve these expectations, autonomous mobile robots should precisely perceive the situation on roads with traffic. In this paper, we revisit and implement a real-time traffic light recognition system with a proposed lightweight state recognition network and ratio-preserving zero padding, which is a two-stage system consisting of a traffic light detection (TLD) module and a traffic light status recognition (TLSR) module. For the TLSR module, this work proposes a lightweight state recognition network with a small number of weight parameters, because the TLD module needs more weight parameters to find the exact location of traffic lights. Then, the proposed effective and lightweight network architecture is constructed by using skip connection, multifeature maps with different sizes, and kernels of appropriately tuned sizes. Therefore, the network has a negligible impact on the overall processing time and minimal weight parameters while maintaining high performance. We also propose to utilize a ratio-preserving zero padding method for data preprocessing for the TLSR module to enhance recognition accuracy. For the TLD module, extensive evaluations with varying input sizes and backbone network types are conducted, and then appropriate values for those factors are determined, which strikes a balance between detection performance and processing time. Finally, we demonstrate that our traffic light recognition system, utilizing the TLD module’s determined parameters, the proposed network architecture for the TLSR module, and the ratio-preserving zero padding method can reliably detect the location and state of traffic lights in real-world videos recorded in Gumi and Deagu, Korea, while maintaining at least 30 frames per second for real-time operation. Full article