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An image of a car underneath a wifi symbol, indicating mobility options with smart technology.
The image of a person, a vehicle, and a shield, meant to evoke safety.
The image of a smart phone with people on it, meant to be evocative of shared transportation modes.
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The FloodSense project, first seeded by C2SMART in 2020, developed a publicly-accessible platform that provides real-time flood information through deployed sensors capable of detecting the presence of water. Designed to help cities respond to emergencies by providing real-time information on flood depth, frequency, and duration, the project has since grown into the NYC FloodNet consortium, a partnership with CUNY, the NYC Mayor’s Offices of Climate Resiliency, and of the CTO, Science and Resilience Institute, Jamaica Bay, and New York Sea Grant– and plans to scale sensor deployment all over New York City. The research team will share the development and impact of the project, and its role in the future of NYC’s emergency response and resiliency, detailing how FloodNet sensors were able to detect and generate warnings from flooding resulting from Hurricane Ida, the challenges and opportunities of scale, and the future of smart urban resilience.
Research and Practice: Implementing a Flood Sensor Network for a Resilient NYC
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_6MYIWE450P0
The FloodSense project, first seeded by C2SMART in 2020, developed a publicly-accessible platform that provides real-time flood information through deployed sensors capable of detecting the presence of water. Designed to help cities respond to emergencies by providing real-time information on flood depth, frequency, and duration, the project has since grown into the NYC FloodNet consortium, a partnership with CUNY, the NYC Mayor’s Offices of Climate Resiliency, and of the CTO, Science and Resilience Institute, Jamaica Bay, and New York Sea Grant– and plans to scale sensor deployment all over New York City. The research team will share the development and impact of the project, and its role in the future of NYC’s emergency response and resiliency, detailing how FloodNet sensors were able to detect and generate warnings from flooding resulting from Hurricane Ida, the challenges and opportunities of scale, and the future of smart urban resilience.
Research and Practice: Implementing a Flood Sensor Network for a Resilient NYC
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_HN8dFakc1Pw
Robust, reliable perception systems are the “eyes” of autonomous vehicles (AVs). LiDAR is a widely applied perception sensor in AVs for capturing 3D geometry information from the environment. But LiDAR-based perception faces challenges such as data sparsity, occlusions, and motion distortion. Yiming Li’s research works to design novel 3D deep learning algorithms from both collaborative and adversarial aspects to improve the robustness of LiDAR-based 3D perception. For effective and efficient collaborative perception, he proposes DiscoNet, which uses dynamic directed graph with matrix-valued edge weight for an ego-vehicle to adaptively retrieve the most important complementary information from its neighboring vehicles, which could improve its own perception performance and robustness. Looking at the adversarial robustness of LiDAR-based perception Yiming will reveal an often-overlooked vulnerability that lies in the LiDAR motion correction process, and show that spoofing a vehicle’s trajectory estimation with small adversarial perturbations can jeopardize LiDAR perception. Yiming’s aim is for this collaborative and adversarial 3D perception research to help improve the robustness and safety of autonomous driving systems everywhere.
Collaborative and Adversarial 3D Perception for Autonomous Driving
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_W-AONQMfGi0
On May 18, 2022, transportation and smart cities researchers and colleagues across the three global NYU campuses met to hold the NYU Transportation Joint Campus Research Summit. Faculty from each campus (NYU-New York, -Shanghai, and -Abu Dhabi) brought a unique set of perspectives, experiences, and expertise to a conversation not just about how transportation research and education operates at NYU, but also how mobility underpins many aspects of daily life in our cities, from hyperlocal - and even individual - levels, to global and societal mechanisms. They discussed cities as ecosystems for connection, for crisis response, and for vital innovation that will not just keep us moving, but keep us moving forward.
NYU Joint Campus Transportation Research Summit
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_2oD9mOFFHD8
In this presentation, experience from three Louisiana projects involving SHM will be shared with the audience. The first two projects were employed to assist the Louisiana DOTD in updating their standard continuity detail between simply supported prestressed concrete girders. The first detail was a full-continuity detail following NCHRP Report 519 recommendations, whereas the second was a partial continuity detail using a link slab. Results from live load tests and long-term monitoring will be presented. These results assisted in updating the standard continuity detail in the new Louisiana Bridge Design and Evaluation Manual. The third project addresses load rating of cast-in-place (CIP) reinforced concrete (RC) box culverts with low fill heights, which are known to produce lower load rating factors if simplified AASHTO-LRFD procedures for live load distribution are used;even though these culverts rarely show signs of distress. Eight CIP-RC culverts were instrumented and load tested in the field. Refined three-dimensional (3D) finite element (FE) models were calibrated using measured field data and used to load rate the tested culverts. Recommendations for future load rating were also made. Finally, an overview of a project sponsored by the National Science Foundation to introduce SHM in undergraduate CE curricula will be presented.
Structural Health Monitoring Applications in Bridge Projects Lessons from Louisiana
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_eI9_HPVYLBo
The increasing popularity of smart cities and recent developments in sensing, edge computing and vehicle technologies and the availability of “big data” combined with sophisticated AI techniques offer an opportunity to substantially advance and fundamentally transform the road safety profession—enabling continuous, real-time, proactive safety evaluation and optimization. The application of innovations that are both progressing and disrupting the status quo represents an opportunity for improved transportation safety. However, with the introduction of new modes of mobility and the complex interactions created by these different technologies within the transportation system, governments will need to rely on advanced research and analysis techniques to support policies towards the transition to these new forms of mobility and technologies. These issues are discussed and several methods and techniques developed in this area are described with example projects from several agencies worldwide.
Real-Time Proactive Road Safety Management Techniques
YouTube Video UC-JzRVBnWGJ3_Jv5iYmpo6A_0bV-30OvSuE
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