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DTSTART;TZID=America/New_York:20251112T130000
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UID:89965-1762952400-1762956000@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Inverse Learning and Intervention of Transportation Network Equilibrium
DESCRIPTION:Abstract:\nBy 2035\, nearly half of all new vehicles in the United States will be connected\, generating unprecedented volumes of mobility data. Leveraging emerging connected mobility data\, this talk establishes an AI-enabled inverse learning framework to transform the transportation network equilibrium modeling paradigm\, which has been the foundation of system planning and management for over seventy years. \nTraditional transportation network equilibrium models are time-consuming and costly to calibrate. This talk presents the inverse learning of user equilibrium as a novel framework for constructing nonparametric\, context-dependent network equilibrium models directly from empirical travel patterns. We compare nonparametric and parametric approaches\, mathematically clarifying the trade-offs among behavioral realism\, data availability\, and computational cost. The proposed neural-network-based nonparametric framework can automatically discover any well-posed network equilibrium model given\nsufficient data\, without relying on predefined behavioral assumptions. In contrast\, the semi-parametric approach is more computationally tractable\, as it simplifies the inverse learning problem into a sequence of convex optimizations. \nFinally\, we apply the inverse learning framework to a long-term network design problem for the city of Ann Arbor\, Michigan. Using real-world crowdsourced data\, we learned a context-dependent equilibrium model and introduce a certified\, auto-differentiation-accelerated algorithm to solve the resulting distributionally robust bi-level network design problem under contextual uncertainty. \nBio:\nDr. Zhichen Liu is an Assistant Professor in the Stony Brook University Department of Civil Engineering. Her research focuses on innovating next-generation modeling and computational tools for mobility and logistics systems\, with an emphasis on connectivity\, electrification\, and automation. She received her Ph.D. in Civil Engineering and M.S. in Industrial and Operational Engineering from the University of Michigan\, and previously served as a Visiting Scientist at General Motors. Dr. Liu is a recipient of the Rackham Predoctoral Fellowship and was honored as the sole global awardee of the prestigious Helene M. Overly Memorial Scholarship by the WTS International Foundation.
URL:https://c2smart.engineering.nyu.edu/event/89965/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Student Events,Virtual Events,Webinars
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251229T150000
DTEND;TZID=America/New_York:20251229T160000
DTSTAMP:20260620T105919
CREATED:20251219T185245Z
LAST-MODIFIED:20251219T185245Z
UID:90481-1767020400-1767024000@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 1
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-1/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260105T150000
DTEND;TZID=America/New_York:20260105T160000
DTSTAMP:20260620T105919
CREATED:20251219T185405Z
LAST-MODIFIED:20251219T185405Z
UID:90484-1767625200-1767628800@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 2
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-2/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260106T120000
DTEND;TZID=America/New_York:20260106T130000
DTSTAMP:20260620T105919
CREATED:20251219T185543Z
LAST-MODIFIED:20251219T185543Z
UID:90487-1767700800-1767704400@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 3
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-3/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260107T090000
DTEND;TZID=America/New_York:20260107T100000
DTSTAMP:20260620T105919
CREATED:20251219T185637Z
LAST-MODIFIED:20251219T185637Z
UID:90489-1767776400-1767780000@c2smart.engineering.nyu.edu
SUMMARY:NYU MS Open House Series\, Session 4- Last one!
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/nyu-ms-open-house-series-session-4-last-one/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260611T150000
DTEND;TZID=America/New_York:20260611T160000
DTSTAMP:20260620T105919
CREATED:20260529T171814Z
LAST-MODIFIED:20260529T203638Z
UID:90887-1781190000-1781193600@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Physical Attribute-Driven NFD Approach for Bicycle Network Design
DESCRIPTION:Abstract: Urban street networks are increasingly expected to serve multiple modes simultaneously\, yet tools for understanding how physical design choices affect aggregate traffic performance\, translating those predictions into infrastructure investment decisions\, and quantifying the equity trade-offs among those decisions remains limited. This work addresses these gaps through the estimation of an NFD functional form based on physical attributes of active mobility infrastructure which is then employed to develop network designs informed by varying equity objectives. The physical-infrastructure-informed NFD functional form links traffic performance\, such as capacity\, jam density\, and flow dynamics\, to spatial design characteristics including the among of area allocated to each mode\, space for interaction between modes\, and type of separation between modes. This form was calibrated using high-resolution simulation outputs from multimodal SUMO networks which allowed for differences in flow behavior across modes and designs to be captured in high density scenarios. A two-stage optimization framework was implemented to determine an optimal assignment of bike lane types across network links based on this functional form. The first stage maximizes the developed functional form to return optimal values of the spatial design characteristics. The second step makes use of a genetic algorithm (GA) to generate networks optimizing for flow\, accessibility\, and adherence to ethical frameworks. By evaluating candidate solutions using the estimated NFD\, the algorithm selects infrastructure configurations that maximize network efficiency while also reducing congestion impacts on disadvantaged communities. This framework contributes an integration of macroscopic traffic modeling into network design for multimodal systems with additional equity-focused optimization.
URL:https://c2smart.engineering.nyu.edu/event/seminar-physical-attribute-driven-nfd-approach-for-bicycle-network-design/
LOCATION:NY
CATEGORIES:Student Events,Virtual Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260623T120000
DTEND;TZID=America/New_York:20260623T130000
DTSTAMP:20260620T105919
CREATED:20260526T184820Z
LAST-MODIFIED:20260526T185142Z
UID:90875-1782216000-1782219600@c2smart.engineering.nyu.edu
SUMMARY:Bridge Resource Program Rime Webinar: Fusion of SHM and WIM Data for Minimizing the Cracking Potential of Concrete Bridge Decks
DESCRIPTION:This webinar focuses on the fusion of Structural Health Monitoring (SHM) data and Weigh-In-Motion (WIM) data to predict strain responses from live truck loads and support early-age crack mitigation strategies. A digital twin of a multi-span bridge was developed using a Finite Element Model (FEM) and calibrated based on the SHM data. The webinar will present how truck traffic and load data from WIM and structural responses from SHM can be used in the FEM under realistic loading scenarios during construction to support decision-making for the pouring sequence. The fusion of SHM\, WIM and FEM provides advanced predictions of structural behavior allowing for optimization of the concrete placement schedules by selecting a window of time to mitigate cracking while minimizing traffic restrictions. \nThis webinar will cover innovative methods for obtaining and combining analysis for\nNondestructive Material testing – Testing Concrete Samples and temperature over time for early age nondestructive strength evaluation of the structural element\nDeveloping a Truck live load profile – Onsite Weigh-In-Motion (WIM) System profiles truck traffic\nBridge superstructure behavior under known truck load – Bridge Instrumentation using strain gauges\, accelerometers\, and deflectometers\nLong term concrete shrinkage and thermal strain of bridge deck – Embedded sensor assessment of bridge deck & girder elements\nDigital twin – Finite Element Model for identifying strains that can produce cracking\nPost install evaluation – Crack Mapping and Analysis \nPresented by Rutgers’s Prof. Hani Nassif\, Dr. Chaekuk Na\, and Michael Ruszala
URL:https://c2smart.engineering.nyu.edu/event/bridge-resource-program-rime-webinar-fusion-of-shm-and-wim-data-for-minimizing-the-cracking-potential-of-concrete-bridge-decks/
LOCATION:NY
CATEGORIES:Seminars,Student Events,Virtual Events,Webinars
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