A5071915866- Traffic and Road Safety
- Traffic Prediction and Management Techniques
- Human-Automation Interaction and Safety
- Imbalanced Data Classification Techniques
- Currency Recognition and Detection
- Vehicle emissions and performance
- Vehicular Ad Hoc Networks (VANETs)
- Air Quality and Health Impacts
- Autonomous Vehicle Technology and Safety
- Transportation Planning and Optimization
- IoT and GPS-based Vehicle Safety Systems
- Transportation and Mobility Innovations
- Urban Transport and Accessibility
- Vehicle License Plate Recognition
National Technical University of Athens
2022-2025
The i-DREAMS project has a core objective: to establish comprehensive framework that defines, develops, and validates context-aware ‘Safety Tolerance Zone’ (STZ). This zone is crucial for maintaining drivers within safe operational boundaries. primary focus of this research conduct detailed comparison between two machine learning approaches: long short-term memory networks shallow neural networks. goal evaluate the safety levels participants as they engage in natural driving experiences...
The present study aims to investigate the benefits of eco-driving in urban areas and on highways through an experiment conducted a driving simulator. Within group 39 participants aged 18–30, multiple scenarios were conducted, both without with guides, assess impact behavior environmental sustainability safety outcomes. Data pollutant emissions, including carbon dioxide (CO2), monoxide (CO), nitrogen oxides (NOx), as well crash probability, collected during experiment. relationships between...
Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched the past years. Although in-vehicle interventions gamification features post-trip dashboards have emerged, connection between prediction triggering of such yet to be realized. This focus European Horizon2020 project "i-DREAMS", which aims at defining, developing, testing validating 'Safety Tolerance Zone' (STZ) order prevent drivers from risky behaviors using both post-trip. However,...
Human behavior significantly contributes to severe road injuries, underscoring a critical safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through comprehensive analysis over 356,000 trips, enhancing existing knowledge in field and promoting sustainability safety. The research uses advanced machine learning algorithms (e.g., Random Forest, Gradient Boosting, Extreme Multilayer Perceptron, K-Nearest Neighbors) categorize into ‘Dangerous’...
Abstract Road safety is a subject of significant concern and substantially affects individuals across the globe. Thus, real-time, post-trip interventions have gained importance in past few years. This study aimed to analyze different classification techniques examine their ability identify dangerous driving behavior based on dual-approach study. The analysis was investigation important risk factors such as average speed, harsh acceleration, braking, headway, overtaking, distraction (i.e.,...
The increasing penetration of autonomous vehicles (AVs) presents new challenges and opportunities for road safety. This study aims to evaluate the impact AV rates on traffic safety through use microscopic simulation scenarios based Villaverde network in Madrid. Eleven were simulated with SAE Level 5 market (MPRs) ranging from 0% 100% 10% increments. Vehicle conflicts, defined as instances where time collision was less than 1.5 s, analyzed along composition roadway characteristics. Multiple...
The i-DREAMS project established a 'Safety Tolerance Zone (STZ)' to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of human element in driving behavior. This paper aims model inter-relationship among task complexity, operator vehicle coping capacity, crash risk. Towards that aim, data from 80 drivers, who participated naturalistic experiment carried out three countries (i.e., Belgium, Germany, Portugal), resulting dataset...
Human behavior has a major role in severe road injuries, making safety enhancement crucial. Recent studies have focused on driving analysis through the development of machine and deep learning models to detect quantify relations between different features. This research aims contribute field's background knowledge by employing various techniques detect, classify, predict dangerous harsh events using algorithms designed for imbalanced datasets. study evaluates influence specific features...