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Energy Efficient Mobility Analytics At Scale For Transportation Hubs

Dr. Satish Ukkusuri
Professor Lyles School of Civil Engineering Purdue University


Given that the transportation sector is a major energy consumer, how do we develop mobility solutions that can optimize the energy flows in urban areas? This talk will first characterize the energy inefficiencies in transportation networks and then develop efficient operational algorithms to reduce energy consumption. Using data-driven methods, the algorithms are integrated into scalable simulation platforms to reduce energy flows. Recent work on electric vehicle demand estimation and charging corridor identification from Indiana will also be discussed using large-scale geolocation data. Energy equity issues will be discussed along with implications to policymakers and transferability to other locations in the US.

Biographical Profile

Satish Ukkusuri is a Reilly Professor in the Lyles School of Civil Engineering at Purdue University and the Director of the Urban Mobility Networks and Intelligence (UMNI) Lab. His research is in the area of interdisciplinary transportation networks with current interests in data-driven mobility solutions, disaster management, the resilience of interdependent networks, connected and autonomous traffic systems, shared mobility platforms, and smart logistics. He is a University Faculty Scholar (2017-present), ASCE Fellow, Fulbright Fellow, a selectee of the National Academy of Engineering (NAE) JAFOE conference (2016), a selectee of the National Academy of Science (NAS) Arab American Frontiers of Science, Engineering and Medicine in 2017 and a CUTC/ARTBA Faculty Award (2011) among other awards. He has published more than 350 peer-reviewed papers in journals such as PNAS, Nature Communications, and Transportation Research Part B. He is the Editor in Chief of the Journal of Big Data Analytics in Transportation and co-Editor in Chief of the ACM Journal of Autonomous Transportation.

Networking Event 4:00 p.m. – 5:00 p.m.    Eng II, Room 103

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