The JRC Award for Excellence 2022 in the category "Excellence in Research": Unveiling the real impact of automated vehicles on motorway traffic
Best paper award of SimSub committee in the development category (TRB 2020)
TRB Joint Simulation Subcommittee AHB45
Enhanced MFC: Introducing Dynamics of Electrified Vehicles for Free Flow Microsimulation Modeling
Paper selected between 408 articles to be featured on the cover of the January 2022 Sensors journal issue:
An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation
Issue: Sensors, Volume 22, Issue 1 (January-1 2022)
ACC Webinar Series: Michail Makridis - Understanding the behavior of commercial ACC systems
The penetration rate of Adaptive Cruise Control (ACC) systems on commercial vehicles is constantly increasing. ACC systems attract a lot of research interest. They are considered a proxy to future autonomous longitudinal vehicle movement, which is expected to bring significant changes in future transport networks in sustainability, traffic flow, safety, and other dimensions. JRC conducted several experimental car-following campaigns to understand how commercial ACC systems operate and their impact on motorway traffic. This talk is about the behavior of car-platoons equipped with ACC, the similarities and differences with human drivers and the essential role of connectivity.
Research and development in traffic engineering with a focus on intelligent transportation systems, partially and fully automated vehicles, traffic flow, energy consumption and sustainability. Research activities involve modelling and simulation, observation-based analysis, analytical studies and machine learning applications.
Traffic flow, modeling and simulation
Traffic Flow: Understanding the impact of heterogeneity in longitudinal driving behaviors on freeway networks.
Traffic Estimation, Prediction: Quantifying the benefits of data fusion between models and observations for better traffic state understanding and prediction.
Traffic Control: Investigating the use of using Connected and Automated Vehicles as mobile actuators for traffic estimation and control.
Traffic Safety: Understanding the different safety perceptions between automated driver such as Adaptive Cruise Control and Humans.
Energy demand and CO2 emissions: Quantifying and comparing the energy needs of (partially or fully) automated systems and human drivers.
Modeling vehicle dynamics: Proposing modeling approaches for incorporating different powertrain behaviors in microsimulation leading to more precise reproduction of electric, hybrid and vehicles with Internal Combustion Engines dynamics.
Understanding reaction time differences between drivers: Response Time and Time Headway of commercially-available Adaptive Cruise Control (ACC) Systems.
String Stability of automated vehicles: Investigating string instability issues of automated vehicles, compare them with human driving behaviors and project such impacts on traffic flow.
Energy Demand of ACC Systems: Quantifying the energy consumption of automated vehicles through empirical observations
Machine learning applications in traffic engineering: Exploring solutions for error pattern learning between models and observations through machine learning. Identifying driver behaviors from empirical data designing ML frameworks with high generalization value.
Infer of the itinerary of cargo transported in shipping containers based on a large, heterogeneous and noisy dataset of Container Status Messages. Such itinerary information can be used to improve the risk analysis performed by authorities in their effort to secure the global trade and produce reliable anti-fraud risk indicators.
Quality Assessment of Risk Indicators
We proposed a quality assessment framework that combines quantitative and qualitative domain specific metrics to evaluate the quality of large datasets of Container Trip Information records and to provide a more complete feedback on which aspects need to be revised to improve the quality of the output data.
Automatic archaeological sherd classification
Sherds that are found in the field usually have little to no visible textual information such as symbols, graphs, or marks on them. This makes manual classification an extremely difficult and time-consuming task for conservators and archaeologists. For a bunch of sherds found in the field, an expert identifies different classes and indicates at least one representative sherd for each class (training sample). We developed an image processing technique that uses local features based on color and texture information to automatically cluster similar pottery fragments and speed up the archaeologists' labelling tasks.
Jigsaw puzzle solving
This work was on providing an automatic jigsaw puzzle solution without any initial restriction about the shape of pieces, the number of neighbor pieces, etc. The proposed technique uses both curve- and color-matching similarity features and applies a recurrent algorithm that matches puzzle pieces in pairs aiming to restore the original image.
Analysis, Segmentation, Restoration of degraded document images
We worked on global and local skew detection in complex color documents with both contextual and graphical information, segmentation of historical machine-printed documents for the detection of characters, words and sentences, as well as document binarization and color reduction.