Professor, Cornell University, USA

​

Andreas Malikopoulos is a Professor in the School of Civil & Environmental Engineering and the Director of the Information and Decision Science Lab at Cornell University. He received a Diploma from the National Technical University of Athens, Greece, and his M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2004 and 2008, respectively, all in Mechanical Engineering. His research interests span several fields, including analysis, optimization, and control of cyber-physical systems; decentralized stochastic systems; stochastic scheduling and resource allocation; and learning in complex systems. Dr. Malikopoulos is the recipient of several prizes and awards, including the 2007 Dare to Dream Opportunity Grant from the University of Michigan Ross School of Business, the 2007 University of Michigan Teaching Fellow, the 2010 Alvin M. Weinberg Fellowship, the 2019 IEEE Intelligent Transportation Systems Young Researcher Award, and the 2020 UD’s College of Engineering Outstanding Junior Faculty Award. He has been selected by the National Academy of Engineering to participate in the 2010 German-American Frontiers of Engineering (FOE) Symposium and organize a session on transportation at the 2016 European-American FOE Symposium. He has also been selected as a 2012 Kavli Frontiers of Science Scholar by the National Academy of Sciences. Dr. Malikopoulos has been an Associate Editor of the IEEE Transactions on Intelligent Vehicles and IEEE Transactions on Intelligent Transportation Systems from 2017 through 2020. He is an Associate Editor of Automatica and IEEE Transactions on Automatic Control, and a Senior Editor of IEEE Transactions on Intelligent Transportation Systems. He is a Senior Member of the IEEE, a Fellow of the ASME, and a member of the Board of Governors of the IEEE Intelligent Transportation Systems Society.

​

A Mobility Equity Metric for Socially Optimal Emerging Mobility Systems: In this talk, Dr. Malikopoulos  will present a mobility equity metric (MEM) to quantify the accessibility and fairness in a transportation network consisting of Connected Automated Vehicles (CAVs) and human-driven vehicles. He will then present a routing framework integrated with MEM that aims to distribute travel demand for the transportation network, resulting in a socially optimal mobility system. A “socially optimal mobility system” is defined to be a mobility system that (1) is efficient in terms of travel time, (2) improves accessibility, and (3) ensures equity in transportation. To accommodate compliant and noncompliant vehicles to the routing suggestions, the framework incorporates a cognitive hierarchy model commonly used in behavioral economics to predict human decisions in transportation systems. The proposed framework aims to bolster mobility equity by addressing transportation and resource access disparities. 

Professor, National Technical University of Athens, Greece

​

Eleni I. Vlahogianni is a Professor at the Department of Transportation Planning and Engineering of the National Technical University of Athens (NTUA). Her research interests include traffic analysis and forecasting, automatic and connected traffic management and advanced technologies and intelligent approaches to mobility and transport infrastructure management using primarily models founded on computational intelligence, deep learning, reinforcement learning, applied statistics and nonlinear dynamics. She has more than 20 years of professional and research experience as an engineer, academic and advisor in research projects and consultancies, in a national and European level. She is an author of more than 280 articles with wide international recognition. She serves as Associate Editor in many well known International peer reviewed journals. She was elected Member of the Board of Governors of the IEEE ITS Society (2019-2021). She serves as a Member of the International Committee on Artificial Intelligence and Advanced Computing Applications of the Transportation Research Board. She is a Member of the Sectoral Scientific Council on Engineering of the Greek Council of Science Technology and Innovation.

​

Theory aware actionable and trustworthy forecasting​: The state of the art in short-term traffic forecasting relies almost exclusively on advancements in deep learning and performance-driven modeling, disregarding the knowledge stemming from empirical and analytical investigations of traffic flow. This leads to forecasting constructs that are usually extremely complex, difficult to understand and hard to generalize on unseen events, and, eventually, of limited trustworthiness. It is without doubt that both empirical and analytical investigations can provide valuable insights into the mechanics of network traffic and, when combined with powerful modeling techniques, they can enhance their actionability, resilience and efficiency, as well as reduce prediction error. The primary motivation of this talk is to discuss the importance of actionable trustworthy models for predictive traffic management. We will provide a concise review of the advancements in short term traffic forecasting, and we will showcase the impact of theory driven and causal learning on the generalizability of simple deep learning models.

Professor Technion, Israel Institute of Technology, Israel

​

Jack Haddad is an Associate Professor of Transportation Engineering with the Civil and Environmental Engineering faculty, the Technion – Israel Institute of Technology, and the Head of the Technion Sustainable Mobility and Robust Transportation (T-SMART) Laboratory. He received all his degrees B.Sc. (2003), M.Sc. (2006), and Ph.D. (2010) in Transportation Engineering from the Technion. His current research interests include urban air mobility, autonomous vehicles, traffic flow modeling and control, large-scale complex networks, advanced transportation systems management, and public transportation.Dr. Haddad serves as an Associate Editor for two journals: Transportation Research Part C and IEEE Transactions on Intelligent Transportation Systems. He was a recipient of the European Union Marie Curie, Career Integration Grant (CIG), and a recipient of two Israel Science Foundation (ISF) grants. He is currently the head of the Technion Transportation Research Institute (TRI), and the Assistant to the Senior Executive Vice President for Equal Opportunities. He is also a Visiting Faculty Researcher at Google

​

Control of Advanced Air Mobility Systems: This talk will focus on traffic flow modeling and control of advanced air mobility. The imminent penetration of low-altitude passenger and delivery aircraft into the urban airspace will give rise to new urban air transport systems, which we call low-altitude air city transport (LAAT) systems. As the urban mobility revolution approaches, we must investigate (i) the individual and collective behavior of LAAT aircraft in cities, and (ii) ways of controlling LAAT systems. The future LAAT systems are a typical example of a new class of modern large scale engineering systems - networked control systems. They are spatially distributed, consist of many interconnected elements in which the control loops are closed through digital communication networks such that the system signals can be exchanged among all components (sensors, controllers, and actuators) through a common network. However, using networks introduces new great challenges, due to the network-induced uncertainties such as: signal’s sampling, varying network induced (communication) delays, information constraints due to spatial distribution, data quantization effects, packet losses, vulnerability to different types of cyberattack, etc. Because of these changes, several new concerns need to be addressed in boundary traffic flow control design of LAAT systems. We will explore how decentralized adaptive control strategies can be designed to mitigate the effect of uncertainties, disturbances and different types of time-delay for various configurations of distributed control topologies.