Bio:Stanislav Sobolevsky is an Associate Professor of Practice And Director Of Urban Complexity Lab at the Center for Urban Science and Progress at New York University and a Research Affiliate at the MIT Senseable City Lab. Holds PhD (1999) and Doctor of Science habilitation degree (2009) in Mathematics. Dr. Sobolevsky teaches data science, machine learning and networks analysis and studies human behavior in urban context through its digital traces: spatio-temporal big data created by various aspects of human activity – social media, cell phone records, vehicle/vessel GPS traces, public service requests, credit card transactions and other. Authored over a 100 of research publications in top-tier journals and conferences, including Nature’s Scientific Reports, PNAS, Physical Review E, PLOS ONE and others. His applied projects on transportation modeling, trajectory mining, anomaly, pattern and vulnerability detection in temporal urban networks are supported by industrial partners and foundations including US Department Of Energy, US Department Of Transportation, National Geospatial Intelligence Agency, Lockheed Martin, Future Cities Catapult, Arcadis. His prior research at MIT was supported by Ericsson, BBVA, Orange, British Telecom, Liberty Mutual and other industrial partners.
Title: Big data for predictive modeling of urban mobility and forecasting impacts of urban solutions
Abstract: The past few decades saw a technological revolution that resulted in the broad penetration of digital technologies in everyday life. More and more aspects of human activity now leave digital traces behind them, thus increasing production of big data related to human activity and mobility. Various datasets from the last 5-7 years - landline and cell phone call records, public transportation records, vehicle GPS traces, credit card transactions, geo-tagged social media, WiFi/Bluetooth connections and many others – are now available for research purposes, creating tremendous opportunities for urban mobility research. In this talk I discuss the applications of this data for understanding urban mobility and review some common approaches for its descriptive and predictive modeling. And I’ll further dive into several specific cases, such as prediction and anomaly detection of the for-hire-vehicle ridership originating at major transportation hubs in New York City and predicting the mode-shift resulting from transportation innovations and policy change in order to assess their economic, social and environmental impacts.
Bio:Satish V. Ukkusuri is a Professor in the Lyles School of Civil Engineering, Director of the Urban Mobility, Networks and Intelligence Lab, Director of the Centre for Smart Mobility and Co-lead of the Sustainable Communities Cluster at Purdue University. His research is in the area of interdisciplinary transportation networks with current interests in data driven mobility solutions, disaster management, resilience of interdependent networks, connected and autonomous traffic systems, shared mobility platforms, dynamic traffic networks and smart logistics. He is a University Faculty Scholar (2017-present), Fulbright Fellow (2015-16), Discovery Park Fellow (2013-15), a selectee of the NAS Arab American Frontiers (2017), a selectee of NAE Japan-US Frontiers of Engineering conference (2016) and a CUTC/ARTBA Faculty Award (2011) among other awards. He has published more than 350 peer reviewed journal and conference articles. He is an Area Editor of Networks and Spatial Economics, Associate Editor of Transpormetrica Part B, Associate Editor of Frontiers of Future Transportation and an Academic Editor of PLOS One among other editorial duties. He is a member of several national and international committees, including the TRB’s Freight Logistics and Planning Committee, Emergency Evacuation Modeling Committee and a past member of the Network Modeling Committee.
Title: Mobility Analytics in an Era of Accelerated Technological Change
Abstract: Over the last few years, we are seeing a convergence of technology, data, analytics and new modeling approaches in transportation systems leading to efficient and sustainable cities. This trend is expected to continue with unprecedented advances in artificial intelligence, telematics, growth of connected and automated technologies and availability of high velocity spatio-temporal data. These advances promise significant benefits in the realization of smart mobility and sustainability; however, many challenges exist on the research frontier. This talk with present an overview of Prof. Ukkusuri’s research in the areas of: (1) smart mobility in information rich transportation environments; (2) innovations in connected/autonomous vehicles and (3) resilience of coupled socio-technical networks. Open questions and insights from these research areas will be discussed.
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