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DTSTART;TZID=America/New_York:20220420T120000
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UID:75460-1650456000-1650459600@c2smart.engineering.nyu.edu
SUMMARY:State-of-the-Field: Structural Health Monitoring (SHM) Application to Bridge Projects: Experience from Louisiana
DESCRIPTION:In the State-of-the-Field event series\, C2SMART leverages its consortium of researchers and experts to share a vision of the future of mobility and transportation systems. They’ll share advances\, opportunities\, predictions\, research bottlenecks\, and what perspectives and skills are needed from researchers and the workforce of tomorrow towards tackling one area of today’s most pressing problems. \nProfessor Ayman M. Okeil\, LSU will share his experience applying structural health monitoring (SHM) methods to three Louisiana projects\, employed to assist the Louisiana DOTD in updating their standard continuity detail between simply supported prestressed concrete girders\, and to address load rating of cast-in-place (CIP) reinforced concrete (RC) box culverts with low fill heights. He’ll share the impact this applied research made on the Louisiana Bridge Design and Evaluation Manual\, and recommendations for future load ratings. Professor Okeil will also share his efforts to develop the transportation workforce of tomorrow by introducing SHM to civil engineering curriculum\, sponsored by the National Science Foundation. \nPresenter \n\n\nDr. Ayman M. Okeil is Roy P. Daniels Professor of Engineering in the Department of Civil and Environmental Engineering\, Louisiana State University. Dr. Okeil’s experience in the field of bridge engineering includes strengthening of concrete girders using composite materials\, behavior of box girder bridges\, structural health monitoring and reliability calibration of LRFD codes. Dr. Okeil is a voting member / associate member in several national committees (ACI 440 and 444\, TRB AKB10 AND AKB30\, ASCE-ACI 343) and serves as Associate Editor for the ASCE J. of Composites for Construction. He is the recipient of several awards in recognition of his teaching and research contributions including “Michael R. Mangham Memorial Undergraduate Teaching Award” from LSU Tiger Athletic Foundation\, the “Outstanding Teaching Award” at NCSU\, the “Outstanding Achievement Award” at LSU (twice)\, and the “Educator of the Year Award” from the Baton Rouge Branch of ASCE (twice). He has also consulted on various projects related to both buildings and bridges. His PhD dissertation investigated seismic design of secondary systems in nuclear power plants.
URL:https://c2smart.engineering.nyu.edu/event/state-of-the-field-structural-health-monitoring-shm-application-to-bridge-projects-experience-from-louisiana/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Infrastructure Resiliency,Virtual Events,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220328T120000
DTEND;TZID=America/New_York:20220328T170000
DTSTAMP:20260610T090131
CREATED:20220309T223338Z
LAST-MODIFIED:20220315T182808Z
UID:74589-1648468800-1648486800@c2smart.engineering.nyu.edu
SUMMARY:Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach
DESCRIPTION:The emergence of connected and autonomous vehicles (CAVs) presents increased opportunities to mitigate traffic congestion\, improve safety and reduce accidents. Professor Zhong-Ping Jiang\, and researchers Leilei Cui and Sayan Chakraborty are applying innovative reinforcement learning control methods to one challenging aspect of CAV control: lane changing in mixed traffic. The team takes a novel approach by reducing the trajectory planning and tracking problem down to the minimization of a cost function that depends on a target way-point in the lane a CAV is targeting. They’ll discuss the integration of reinforcement learning and adaptive/approximate dynamic programming methods without assuming exact knowledge of surrounding vehicles\, while avoiding the curses of dimensionality and modeling of conventional dynamic programming\, and they’ll share simulation and validation results of this promising method towards minimizing fuel consumption and improve safety of the whole traffic stream. \nPresenters \nProfessor Zhong-Ping Jiang is known for his contributions to stability and control of interconnected nonlinear systems\, and is a key contributor to the nonlinear small-gain theory. His recent research focuses on robust adaptive dynamic programming\, learning-based optimal control\, nonlinear control\, distributed control and optimization\, and their applications to computational and systems neuroscience\, connected and autonomous vehicles\, and cyber-physical systems. \nProfessor Jiang is a Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica and of the Journal of Decision and Control and has served as Senior Editor for the IEEE Control Systems Letters (L-CSS) and Systems & Control Letters\, Subject Editor\, Associate Editor and/or Guest Editor for several journals including International Journal of Robust and Nonlinear Control\, Mathematics of Control\, Signals and Systems\, IEEE Transactions on Automatic Control\, European Journal of Control\, and Science China: Information Sciences. \nLeilei Cui is a third-year PhD student at the Department of Electrical and Computer Engineering\, New York University\, under the supervision of Professor Zhong-Ping Jiang. He received a B.S. Degree in Automation from Northwestern Polytechnical University\, Xian\, China\, in 2016\, and the M.S. degree in Control Science and Engineer from Shanghai Jiao Tong University\, Shanghai\, China\, in 2019. His research interests are reinforcement learning\, adaptive dynamic programming\, control theory\, and their applications to robotics and intelligent transportation. \nSayan Chakraborty is a first year PhD candidate at the Department of Electrical and Computer Engineering\, New York University\, under the supervision of Professor Zhong-Ping Jiang. He obtained a B.Tech. degree in Electrical Engineering from National Institute of Technology\, Silchar\, India in 2017\, and an M.Tech. degree in Electrical Engineering with specialization in Systems and Control from Indian Institute of Technology Hyderabad\, India in 2021. His research interests are data-driven control\, adaptive dynamic programming\, and their application to autonomous vehicles.
URL:https://c2smart.engineering.nyu.edu/event/lane-changing-of-autonomous-vehicles-in-mixed-traffic-environments-a-reinforcement-learning-approach/
LOCATION:
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T120000
DTEND;TZID=America/New_York:20220224T130000
DTSTAMP:20260610T090131
CREATED:20220217T175204Z
LAST-MODIFIED:20220217T175204Z
UID:73325-1645704000-1645707600@c2smart.engineering.nyu.edu
SUMMARY:State-of-the-Field: New Fiber Optic Structural Health Monitoring (SHM) Technologies and Recent Case Studies
DESCRIPTION:SMS provides specialized\, fiber-optic structural health monitoring (SHM) systems for bridges and tunnels and world-wide has worked with many agencies and engineering firms. Terry Tamutus\, Founder and CEO of SMS\, will discuss solutions to new construction problems and deteriorating bridges. He’ll share successful case studies\, lessons-learned\, critical safety issues\, O&M improvement\, deterioration models\, and asset management. This webinar will provide engineers with a high-level SHM overview\, types of RFPs used by different agencies\, and a check-list for SHM project oversight. \nIn the State-of-the-Field event series\, C2SMART leverages its consortium of researchers and experts to share a vision of the future of mobility and transportation systems. They’ll share advances\, opportunities\, predictions\, research bottlenecks\, and what perspectives and skills are needed from researchers and the workforce of tomorrow towards tackling one area of today’s most pressing problems. \n Terry Tamutus of SMS\, is a Mechanical Engineer and has over 30 years of SHM expertise. Terry has published over 20 peer-reviewed papers on Acoustic SHM. He provides SHM design\, application support\, installation\, training\, and product development. Worldwide\, he has provided hundreds of papers and presentations to universities\, engineering societies (NACE\, PTI\, ASNT\, TRB)\, government agencies (DoD\, DOTs\, NIST\, FAA\, NASA\, FHWA)\, and companies including Boeing\, Lockheed\, PowerGen\, and refineries.\n 
URL:https://c2smart.engineering.nyu.edu/event/state-of-the-field-new-fiber-optic-structural-health-monitoring-shm-technologies-and-recent-case-studies/
LOCATION:https://c2smart.engineering.nyu.edu/event/state-of-the-field-new-fiber-optic-structural-health-monitoring-shm-technologies-and-recent-case-studies/
CATEGORIES:Infrastructure Resiliency,Virtual Events,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T120000
DTEND;TZID=America/New_York:20211207T133000
DTSTAMP:20260610T090131
CREATED:20211201T193405Z
LAST-MODIFIED:20211201T200034Z
UID:70355-1638878400-1638883800@c2smart.engineering.nyu.edu
SUMMARY:Cooperative Perception of Smart Roadside Unit with Edge AI for Driving Assistance
DESCRIPTION:3D object detection is essential for building autonomous driving perception systems that can detect 3D objects from sensor information and safely plan movement accordingly. Stereo cameras\, light detection\, and liDAR in existing CAV systems can be heavy and expensive\, and have suffered from computing resource limitation\, resulting in unavoidable calculation errors or delays that can lead to severe consequences. To address this challenge and provide more reliable real-time localization services for CAVs\, C2SMART Center researchers Wei Sun and Chenxi Liu have developed a smart roadside unit (SRSU) with advanced computer vision technologies for driving and parking assistance. Developed by the STAR Lab at UW\, the SRSU sensor is a multi-source traffic sensing roadside unit that can transmit data through 4G/5G data plan or Long Range (LoRa) and Narrow Band Internet of Things (NB-loT) data communication protocols. Wei and Chenxi will discuss the development and impact of the smart roadside unit\, along with a Mobile Unit for Traffic Sensing (MUST) employed for data analysis and for reliable\, efficient communication with surrounding vehicles. \n  \nDr. Wei Sun is a research associate in transportation engineering in the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He has a Ph.D. in transportation engineering from the University of Florida (2019) and a bachelor’s degree in transportation engineering from South China University of Technology (2014). Dr. Sun’s active research fields include Intelligent Transportation Systems (ITS)\, transportation data analytics\, traffic operations and safety\, and traffic simulation and software development. Dr. Sun has worked on research projects funded by Federal Highway Administration (FHWA)\, National Cooperative Highway Research Program (NCHRP)\, Washington State Department of Transportation (WSDOT)\, Center for Safety Equity in Transportation (CSET)\, and Pacific Northwest Transportation Consortium (PacTrans). Dr. Sun serves as reviewers for the Journal of Intelligent Transportation Systems: Technology\, Planning\, and Operations\, American Society of Civil Engineers (ASCE) Journal of Transportation Engineering\, Institute of Electrical and Electronics Engineers (IEEE) International Smart Cities Conference\, and Transportation Research Record (TRR). \n  \nChenxi Liu is a Ph.D. student in the Smart Transportation Applications and Research Laboratory (STAR Lab) at the University of Washington (UW). He received his master’s degree in Civil Engineering from University of Washington (2020) and bachelor’s degree in Civil Engineering from Tsinghua University (2017) in Beijing\, China. He came to University of Washington\, Seattle\, WA\, US. in 2017\, he has been a Research Assistant in Smart Transportation Research and Application Lab (STARLab). His research interest includes computer vision\, deep learning\, neural network\, and smart transportation facilities. \n 
URL:https://c2smart.engineering.nyu.edu/event/cooperative-perception-of-smart-roadside-unit-with-edge-ai-for-driving-assistance/
LOCATION:NY
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211026T120000
DTEND;TZID=America/New_York:20211026T130000
DTSTAMP:20260610T090131
CREATED:20211015T153100Z
LAST-MODIFIED:20211018T190550Z
UID:69120-1635249600-1635253200@c2smart.engineering.nyu.edu
SUMMARY:State of the Field: Structural Health Monitoring (SHM) towards Infrastructure Resiliency
DESCRIPTION:In this event series\, C2SMART leverages its consortium of researchers and experts to share a vision of the future of mobility and transportation systems. They’ll share advances\, opportunities\, predictions\, research bottlenecks\, and what perspectives and skills are needed from researchers and the workforce of tomorrow towards tackling one area of today’s most pressing problems. \nState of the Field: Structural Health Monitoring (SHM) towards Infrastructure Resiliency \nWhat does the best of transportation engineering research have to say about maintenance\, rehabilitation\, and replacement of critical infrastructure? How are the latest advances in technology being applied to extend lifespans of structures\, and how can this technology be scaled to infrastructure projects around the United States? Structural Health Monitoring provides critical insight into answering these questions using new technologies that are changing the ways we tackle maintenance and rehabilitation of structures. \nThe first event in this series will be a webinar delivered by Erik Zuker\, PE\, of HNTB\, on the state of SHM applications and case studies from the Mario M. Cuomo Bridge\, Verrazzano Narrows Bridge\, and the Marine Parkway Bridge\, in the New York City area.
URL:https://c2smart.engineering.nyu.edu/event/state-of-the-field-structural-health-monitoring-shm-towards-infrastructure-resiliency/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Infrastructure Resiliency,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211019T150000
DTEND;TZID=America/New_York:20211019T160000
DTSTAMP:20260610T090131
CREATED:20211015T152545Z
LAST-MODIFIED:20211015T153257Z
UID:69113-1634655600-1634659200@c2smart.engineering.nyu.edu
SUMMARY:Proactive Safety Management Empowered by Big Data
DESCRIPTION:In the last few decades\, data-driven methods have been used to assist with key tasks of road safety management like hotspot identification\, countermeasure development\, and before-after evaluation. These methods have traditionally relied heavily on historical crash data for safety assessment\, which can take a long time to collect. Professor Kun Xie will share a more proactive and time-efficient approach based on surrogate safety measures (SSMs)\, which can assess safety by capturing the more frequent “near-crash” situations. Massive amounts of data from emerging sources like GPS devices\, smartphone apps\, traffic cameras\, naturalistic driving\, and connected vehicles (CV) can be leveraged to extract SSMs on a large scale\, presenting new opportunities for proactive traffic safety management. Results will show that risk status is a reliable criterion for safety assessment\, and promisingly point towards the use CV data for proactive traffic safety management.
URL:https://c2smart.engineering.nyu.edu/event/proactive-safety-management-empowered-by-big-data/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Safety in Transportation Systems,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20210908T150000
DTEND;TZID=UTC:20210908T160000
DTSTAMP:20260610T090131
CREATED:20210902T204704Z
LAST-MODIFIED:20210902T204704Z
UID:1480-1631113200-1631116800@c2smart.engineering.nyu.edu
SUMMARY:Incentive Design for Promoting Ridesharing
DESCRIPTION:Traffic congestion has become a serious issue around the globe\, partly owing to single-occupancy commuter trips. Ridesharing can present a suitable alternative for serving commuter trips. However\, there are several important obstacles that impede ridesharing systems from becoming a viable mode of transportation\, including the lack of a guarantee for a ride back home as well as the difficulty of obtaining a critical mass of participants. At this event\, Neda Masoud will discuss a study which addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters. The TIP program allocates incentives to (1) directly subsidize a select set of ridesharing rides\, and (2) encourage a few\, carefully selected set of travelers to change their travel behavior. We formulate the underlying ride-matching problem as a budget-constrained min-cost flow problem and present a Lagrangian Relaxation-based algorithm with worst-case optimality bound to solve large-scale instances of this problem in polynomial time. We further propose a polynomial-time budget-balanced version of the problem. Numerical experiments suggest that allocating subsidies to change travel behavior is significantly more beneficial than directly subsidizing rides. Furthermore\, using a flat tax rate as low as 1% can double the system’s social welfare in the budget-balanced variant of the incentive program. \nBio: Neda Masoud is an Assistant Professor of Civil and Environmental Engineering at the University of Michigan. She holds a Bachelor’s of Science Degree in Industrial Engineering and a Master’s of Science degree in Physics. She received her Ph.D. in Civil and Environmental Engineering from the University of California Irvine. Her research focuses on devising operational and planning tools to facilitate the transition into the next generation of mobility systems\, which are envisioned to be connected\, automated\, electrified\, and shared.
URL:https://c2smart.engineering.nyu.edu/event/incentive-design-for-promoting-ridesharing/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Big Data & Planning for Smart Cities,Virtual Events,Webinars
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