Emmanouil Manos Barmpounakis

Scientist
manos.barmpounakis@epfl.ch 41 21 693 53 97 open-traffic.epfl.ch
Citizenship : Greek
Birth date : 17.11.1988
EPFL ENAC IIC LUTS
GC C2 390 (Bâtiment GC)
Station 18
CH-1015 Lausanne
Web site: Web site: https://luts.epfl.ch
EHE > ASSOCIATIONS > AGEPOLY-CE > MUSICAL
Fields of expertise
- Traffic Monitoring
- Traffic Operations
- Unmanned Aerial Systems
- Data Science
Biography
My primary research field is Traffic Operations, Unmanned Aerial Systems (UAS) for traffic operations and Data Science. My professional experience includes participation in projects and traffic studies in Greece.
Until now I have published 1 book chapter, 15 papers in International Journals, over 30 papers in International Conferences, 1 Doctoral thesis and 1 Diploma thesis.
Current work
pNEUMA intends to revolutionize how emerging technologies reshape our understanding of traffic congestion mechanisms, by putting the emphasis on urban networks with disturbances generated by interactions among different types of vehicles.The project targets to better explain the mechanism of congestion formation and propagation in congested multimodal urban environments through massive data from aerial footage and fundamental research prospective.
Education
Ph.D.
Traffic Operations
National Technical University of Athens, Greece
July 2013 - July 2017
Integrated Master
Civil & Transportation Engineering
National Technical University of Athens, Greece
September 2006 - October 2012
Publications
Selected publications
E. Barmpounakis and N. Geroliminis Transportation Research Part C: Emerging Technologies |
On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment |
E. Barmpounakis, E. Vlahogianni and J. Golias International Journal of Transportation Science and Technology |
Unmanned Aerial Aircraft Systems for transportation engineering: Current practice and future challenges |
E. Barmpounakis, G.M. Sauvin and N. Geroliminis Transportation Research Record |
Lane Detection and lane-changing identification with high-resolution data from a swarm of drones (2020 Greenshields Prize) |