Search Results (1,019)

The metropolitan agglomeration in and around Bucharest, Romania’s capital and largest city, has experienced significant growth in recent decades, both economically and demographically. With over two million residents in its metropolitan area, Bucharest faces urban mobility challenges characterized by congested roads, overcrowded public transport routes, limited parking, and air pollution. This study evaluates the potential of AI-driven adaptive traffic signal control to address these challenges using an agent-based simulation approach. The authors focus on Bucharest’s north-western part, a critical congestion area. A detailed road network was derived from OpenStreetMap and calibrated with empirical traffic data from TomTom Junction Analytics and Route Monitoring (corridor-level speeds and junction-level turn ratios). Using the MATSim framework, the authors implemented and compared fixed-time and adaptive signal control scenarios. The adaptive approach uses a decentralized, demand-responsive algorithm to minimize delays and queue spillback in real time. Simulation results indicate that adaptive signal control significantly improves network-wide average speeds, reduces congestion peaks, and flattens the number of en-route agents throughout the day, compared to fixed-time plans. While simplifications remain in the model, such as generalized signal timings and the exclusion of pedestrian movements, these findings suggest that deploying adaptive traffic management systems could deliver substantial operational benefits in Bucharest’s urban context. This work demonstrates a scalable methodology combining open geospatial data, commercial traffic analytics, and agent-based simulation to rigorously evaluate AI-based traffic management strategies, offering evidence-based guidance for urban mobility planning and policy decisions. Full article

Traffic violations, the primary cause of road accidents, threaten public safety by disrupting traffic flow and causing substantial casualties and economic losses. Accurate spatiotemporal prediction of violations offers critical insights for proactive traffic management. While Graph Attention Network (GAT) methods excel in spatiotemporal forecasting, their practical deployment is hindered by prohibitive computational costs when handling dynamic large-scale data. To address this issue, we propose a Lightweight Traffic Violation Prediction with Graph Attention Distillation (LTVPGA) model, transferring spatial topology comprehension from a complex GAT to an efficient multilayer perceptron (MLP) via knowledge distillation. Our core contribution lies in topology-invariant knowledge transfer, where spatial relation priors distilled from the teacher’s attention heads enable the MLP student to bypass explicit graph computation. This approach achieves significant efficiency gains for large-scale data—notably accelerated inference time and reduced memory overhead—while preserving modeling capability. We conducted a performance comparison between LTVPGA, Conv-LSTM, and GATR (teacher model). LTVPGA achieved revolutionary efficiency: consuming merely 15% memory and 0.6% training time of GATR while preserving nearly the same accuracy. This capacity enables practical deployment without sacrificing fidelity, providing a scalable solution for intelligent transportation governance. Full article

Traffic flow prediction is a critical component of intelligent transportation systems, playing a vital role in alleviating congestion, improving road resource utilization, and supporting traffic management decisions. Although deep learning methods have made remarkable progress in this field in recent years, current studies still face challenges in modeling complex spatio-temporal dependencies, adapting to anomalous events, and generalizing to large-scale real-world scenarios. To address these issues, this paper proposes a novel traffic flow prediction model. The proposed approach simultaneously leverages temporal and frequency domain information and introduces adaptive graph convolutional layers to replace traditional graph convolutions, enabling dynamic capture of traffic network structural features. Furthermore, we design a frequency–temporal multi-head attention mechanism for effective multi-scale spatio-temporal feature extraction and develop a cross-multi-scale graph fusion strategy to enhance predictive performance. Extensive experiments on real-world datasets, PeMS and Beijing, demonstrate that our method significantly outperforms state-of-the-art (SOTA) baselines. For example, on the PeMS20 dataset, our model achieves a 53.6% lower MAE, a 12.3% lower NRMSE, and a 3.2% lower MAPE than the best existing method (STFGNN). Moreover, the proposed model achieves competitive computational efficiency and inference speed, making it well-suited for practical deployment. Full article

Predicting driver injury severity is critical for enhancing road safety, but it is complicated because fatal accidents inherently create class imbalance within datasets. This study conducts a comparative analysis of machine-learning (ML) and deep-learning (DL) models for multi-class driver injury severity prediction using a comprehensive dataset of 107,195 traffic accidents from the Adana, Mersin, and Antalya provinces in Turkey (2018–2023). To address the significant imbalance between fatal, injury, and non-injury classes, the hybrid SMOTE-ENN algorithm was employed for data balancing. Subsequently, feature selection techniques, including Relief-F, Extra Trees, and Recursive Feature Elimination (RFE), were utilized to identify the most influential predictors. Various ML models (K-Nearest Neighbors (KNN), XGBoost, Random Forest) and DL architectures (Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN)) were developed and rigorously evaluated. The findings demonstrate that traditional ML models, particularly KNN (0.95 accuracy, 0.95 F1-macro) and XGBoost (0.92 accuracy, 0.92 F1-macro), significantly outperformed DL models. The SMOTE-ENN technique proved effective in managing class imbalance, and RFE identified a critical 25-feature subset including driver fault, speed limit, and road conditions. This research highlights the efficacy of well-preprocessed ML approaches for tabular crash data, offering valuable insights for developing robust predictive tools to improve traffic safety outcomes. Full article

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion evolution focus on a single, fixed scale, which risks overlooking clearer causal patterns at other scales and thus limiting predictive power and practical applicability. To address this, we develop a multiscale congestion evolution modeling framework grounded in causal emergence theory. Within this framework we build dynamical models at multiple spatiotemporal scales using dynamic Bayesian networks (DBNs) and quantify the causal strength of these models using effective information (EI) and singular value decomposition (SVD)-based diagnostics. Using road networks from three central Kunshan regions, we validate the proposed framework across 24 spatiotemporal scales and five demand scenarios. Across all three regions and the tested scales, we observe evidence of causal emergence in congestion evolution dynamics. When results are pooled across regions and scenarios, models built at the 10 min/150 m scale exhibit stronger and more coherent causal structure than models at other scales. These findings demonstrate that the proposed framework can identify and help build dynamical models of congestion evolution at appropriate spatiotemporal scales, thereby supporting the development of proactive traffic management and effective resilience enhancement strategies for urban transport systems. Full article

Skid resistance, or friction, on a road surface is a critical parameter in functional highway assessments, given its direct relationships with safety and accident frequency. Therefore, road administrations must collect friction data across their road networks to ensure safe roads for users. In addition, having a predictive model of skid resistance for each road section is essential for an efficient pavement management system (PMS). Traditionally, road authorities disregard rural roads, since they are more focused on freeways and traffic-intense roads. This study develops a model for predicting minimum-available skid resistance, which occurs in summer, measured using the Sideway-force Coefficient Routine Investigation Machine (SCRIM), on bituminous pavements in the single-carriageway road network of the Province of Gipuzkoa, Spain. To this end, traffic volume data available in the PMS of the Provincial Council of Gipuzkoa, such as the annual average daily traffic (AADT) and the AADT of heavy vehicles (AADT.HV), were uniquely used to forecast skid-resistance values collected in summer. Additionally, a methodology for eliminating outliers is proposed. Despite the simplicity of the model, which does not include information about the materials at the surface layer, a coefficient of determination (R²) of 0.439 was achieved. This model can help road authorities identify the roads for which lower skid-resistance values are most likely to occur, allowing them to focus their attention and efforts on these roads, which are key infrastructure in rural areas. Full article

With the accelerated commercialization of autonomous vehicles, new accident types and complex risk factors have emerged beyond the scope of existing traffic safety management systems. This study aims to contribute to sustainable safety by establishing a quantitative basis for early recognition and response to high-risk situations in urban traffic environments where autonomous and conventional vehicles coexist. To this end, high-risk factors were identified through a combination of literature meta-analysis, accident history and image analysis, autonomous driving video review, and expert seminars. For analytical structuring, the six-layer scenario framework from the PEGASUS project was redefined. Using the analytic hierarchy process (AHP), 28 high-risk factors were identified. A risk prediction model framework was then developed, incorporating observational indicators derived from expert rankings. These indicators were structured as input variables for both road segments and autonomous vehicles, enabling spatial risk assessment through agent-based strategies. This space–object integration-based prediction model supports the early detection of high-risk situations, the designation of high-enforcement zones, and the development of preventive safety systems, infrastructure improvements, and policy measures. Ultimately, the findings offer a pathway toward achieving sustainable safety in mixed traffic environments during the initial deployment phase of autonomous vehicles. Full article

Purpose: To evaluate the prevalence of undiagnosed ligamentous knee injuries in patients surgically treated for high-energy pelvic ring or acetabular fractures and propose a mechanism to diagnose these briefly post-hospital discharge. Methods: A retrospective case series (level of evidence IV) was conducted at Italy’s largest trauma center. Medical records from 2018 to 2023 were reviewed to identify patients who underwent surgical treatment for pelvic or acetabular fractures. Eligible patients were contacted for a structured telephone interview, which included a questionnaire on knee symptoms and the International Knee Documentation Committee (IKDC) score. Associations between demographic factors, trauma mechanism, and knee outcomes were statistically analyzed. Results: Fifty-nine patients (mean age 55 years, 72.9% male) were enrolled. Undiagnosed knee ligament injuries were present in 11.9%, with an additional 8.5% reporting persistent knee symptoms. The average time to diagnosis was 6.4 months post-discharge. Patients involved in road traffic accidents showed a significantly higher incidence of knee injuries (34.8%) compared to those who fell from a height (3.9%) (p = 0.049). Patients who had undergone ligament reconstruction had significantly lower IKDC scores (62.0 ± 8.2) than non-surgical cases (82.4 ± 12.1, p = 0.0002). No association was found with age or sex. Conclusions: Ligamentous knee injuries are frequently overlooked in the acute management of high-energy pelvic fractures, particularly in road traffic accidents. A systematic knee assessment before discharge or early outpatient imaging should be considered to improve detection and outcomes. Full article

In the V2X environment, intelligent connected vehicles can obtain multi-dimensional traffic flow data in real time through the vehicle–road collaborative cyber–physical fusion system. Based on this, this study proposes a multi-lane traffic flow lattice model integrating optimal traffic flow difference estimation information to effectively suppress traffic congestion. The linear stability criterion of the system is derived through linear stability analysis, proving that the optimal traffic flow difference estimation can significantly expand the stable region and suppress traffic fluctuations caused by small disturbances. Furthermore, the perturbation method is used to derive the mKdV equation near the critical stability point of the system, revealing the nonlinear characteristics of traffic congestion propagating in the form of kink solitary waves, and indicating that the new consideration effect can effectively slow down the congestion propagation speed by adjusting the parameters of solitary waves (such as wave speed and amplitude). The numerical simulation results show that compared to the traditional model, the improved model exhibits enhanced traffic flow stability and robustness. Meanwhile, it reveals the nonlinear relationship between the increase of the number of lanes and the alleviation of congestion, and there is an optimal lane configuration threshold. The research results not only provide theoretical support for the optimization of traffic flow efficiency in intelligent transportation systems, but also provide a decision-making basis for dynamic lane management strategies in the V2X environment. Full article

Background and Objectives: Interpersonal violence (IPV) has overtaken road traffic collisions as a leading cause of facial fractures, yet regional data from Southern Europe are limited. Materials and Methods: We retrospectively reviewed all adults (≥18 y) treated between 1 January 2011 and 31 December 2022 for radiologically confirmed IPV-related facial fractures. Recorded variables were demographics, AO-CMF (Arbeitsgemeinschaft für Osteosynthesefragen—Craniomaxillofacial) fracture site, Facial Injury Severity Score (FISS), presence of facial soft-tissue wounds, treatment modality, and length of stay; associations between variables were explored. Results: A total of 224 victims were identified; 94% were men (median age 26 y, IQR 22–34). The mandible was the most frequently involved bone (42%), followed by the orbit (25%); 14% sustained fractures at multiple sites. Facial soft-tissue wounds occurred in 9% of cases, three-quarters of which were associated with mandibular injury (p = 0.005). The median FISS was 2 and was higher in males, patients > 34 y, those with multiple fractures, and those with wounds (all p < 0.05). FISS showed a weak positive correlation with hospital stay (r = 0.23), which averaged 4.1 ± 1.6 days. Open reduction and internal fixation were required in 78% of patients, most often 24–72 h after admission. Annual IPV-related admissions remained stable throughout the 12-year period. Conclusions: IPV in this region consistently injures young men, with the mandible and orbit most at risk. FISS is a practical bedside indicator of resource use. The unchanging incidence—likely underestimated because isolated nasal fractures and minor injuries are often managed outside maxillofacial services or never reported—highlights the urgency of targeted prevention programs, routine screening, and streamlined multidisciplinary pathways. Full article

Vehicle load is crucial for road design, maintenance, and expansion, while vehicle speed and lateral position are essential for traffic management and driving safety. This paper introduces a method for collecting vehicle speed, lateral position, and load information using roadside Micro-Electromechanical Systems (MEMS) accelerometers located on the pavement. Firstly, this research analyzes the distribution of pavement vibration responses in both lateral and vertical directions based on the Finite Element Method (FEM) data provided in the literature. Then, pavement vibration data is collected by roadside sensors with a Full-scale Accelerated Loading Tester, considering varying vehicle speeds, loads, and lateral positions. The results reveal that the vertical peak acceleration increases linearly with vehicle speed within a range of 5–22 km/h, decreases following a power law as the lateral distance between the wheel center and sensor increases from 0.4 to 0.9 m, which is consistent with the trends observed in the literature’s FEM data. The vibration energy of the vertical acceleration exhibits a positive linear correlation with the total vehicle load, with a correlation coefficient of 0.885. This approach offers a practical method for vehicle load estimation, optimal sensor deployment, and enhancement of pavement performance monitoring systems. Full article

This study explores the impact of driving behaviors, specifically cautious and aggressive, on the performance of human-driven vehicles (HDVs) and autonomous vehicles (AVs) in traffic flow dynamics. It focuses on various metrics, including level of service (LOS), average speed, traffic volume, queue delays, carbon emissions, and fuel consumption, to assess their effects on overall performance. The findings reveal significant differences between cautious and aggressive AVs, particularly at varying market penetration rates (MPRs). Aggressive autonomous vehicles demonstrate greater traffic efficiency compared to their cautious counterparts. They achieve higher levels of service, improving from poor performance at low MPRs to significantly better performance at higher MPRs and in fully autonomous scenarios. In contrast, cautious AVs often experience poor service ratings at low MPRs, with an improvement in performance only at higher MPRs. Regarding environmental performance, aggressive AVs outperform cautious ones in terms of reduced emissions and fuel consumption. The emissions produced by aggressive AVs are significantly lower than those from cautious AVs, and they further decrease as the MPRs increases. Additionally, aggressive AVs show a considerable reduction in fuel usage compared to cautious AVs. While cautious AVs improve slightly at higher MPRs, they continue to generate higher emissions and consume more fuel than their aggressive counterparts. In conclusion, aggressive AVs offer better traffic efficiency and environmental performance than both cautious AVs. Their ability to improve road efficiency and reduce congestion positions them as a valuable asset for sustainable transportation. Strategically incorporating aggressive AVs into transportation systems could lead to significant advancements in traffic management and environmental sustainability. Full article

This paper presents the design and implementation of a high-precision vehicle detection and classification system for toll stations on national highways in Colombia, leveraging LiDAR-based 3D point cloud processing and supervised machine learning. The system integrates a multi-sensor architecture, including a LiDAR scanner, high-resolution cameras, and Doppler radars, with an embedded computing platform for real-time processing and on-site inference. The methodology covers data preprocessing, feature extraction, descriptor encoding, and classification using Support Vector Machines. The system supports eight vehicular categories established by national regulations, which present significant challenges due to the need to differentiate categories by axle count, the presence of lifted axles, and vehicle usage. These distinctions affect toll fees and require a classification strategy beyond geometric profiling. The system achieves $89.9\%$ overall classification accuracy, including $96.2\%$ for light vehicles and $99.0\%$ for vehicles with three or more axles. It also incorporates license plate recognition for complete vehicle traceability. The system was deployed at an operational toll station and has run continuously under real traffic and environmental conditions for over eighteen months. This framework represents a robust, scalable, and strategic technological component within Intelligent Transportation Systems and contributes to data-driven decision-making for road management and toll operations. Full article

Road markings, as critical components of transportation infrastructure, are crucial for ensuring traffic safety. Accurate quantification of their damage severity is vital for effective maintenance prioritization. However, existing methods are limited to detecting the presence of damage without assessing its extent. To address this limitation, we propose a novel segmentation-based framework for estimating the degree of road marking damage. The method comprises two stages: segmentation of residual pixels from the damaged markings and segmentation of the intact markings region. This dual-segmentation strategy enables precise reconstruction and comparison for severity estimation. To enhance segmentation performance, we proposed two key modules: the Asymmetric Large Field-of-View Contextual (ALFVC) module, which captures rich multi-scale contextual features, and the supervised Boundary Feature Enhancement (BFE) module, which strengthens shape representation and boundary accuracy. The experimental results demonstrate that our method achieved an average segmentation accuracy of 89.44%, outperforming the baseline by 5.86 percentage points. Moreover, the damage quantification achieved a minimum error rate of just 0.22% on the proprietary dataset. The proposed approach was both effective and lightweight, providing valuable support for automated maintenance planning, and significantly improving the efficiency and precision of road marking management. Full article

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article