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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260327T123000
DTEND;TZID=America/New_York:20260327T133000
DTSTAMP:20260416T120659
CREATED:20260304T152522Z
LAST-MODIFIED:20260304T152522Z
UID:90559-1774614600-1774618200@c2smart.engineering.nyu.edu
SUMMARY:SLH: Introduction to Bayesian Optimization and Its Applications in Transportation and AI
DESCRIPTION:This talk introduces Bayesian Optimization (BO)\, a sample-efficient framework for optimizing expensive\, noisy black-box functions. We will cover the core ideas behind surrogate modeling (e.g.\, Gaussian processes) and acquisition functions (such as EI and UCB) that balance exploration and exploitation to find high-performing solutions with limited evaluations. The applications focus on two representative directions: (1) parameter calibration for transportation simulation—tuning behavioral and network parameters so simulated traffic patterns match real observations; and (2) hyperparameter optimization in machine learning—automatically selecting model and training settings to improve accuracy\, robustness\, and efficiency. We will also highlight practical considerations such as constraints\, multi-objective trade-offs\, and scalable implementations.\n\nPresented by NYU’s Yu Tang
URL:https://c2smart.engineering.nyu.edu/event/slh-introduction-to-bayesian-optimization-and-its-applications-in-transportation-and-ai/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260313T150000
DTEND;TZID=America/New_York:20260313T160000
DTSTAMP:20260416T120659
CREATED:20260309T151845Z
LAST-MODIFIED:20260309T152634Z
UID:90578-1773414000-1773417600@c2smart.engineering.nyu.edu
SUMMARY:MTA x NYU: Demystifying the Application Process
DESCRIPTION:Kawanza Williams serves as Senior Manager of Emerging Talent at the Metropolitan Transportation Authority (MTA)\, where she leads initiatives focused on talent development\, retention\, and strategic workforce partnerships. In her role as Employee Manager of Retention & External Partnerships\, she oversees the full lifecycle of the MTA’s internship programs\, guiding interns from onboarding through professional development and\, when applicable\, transition into permanent roles. Kawanza works closely with senior leadership\, external partners\, and vendors to ensure that talent pipelines align with organizational priorities and operational needs. Her leadership emphasizes structured career pathways\, sustainable workforce development\, and cost-conscious program design that supports long-term institutional growth. Through her work\, she plays a central role in shaping how emerging professionals enter\, navigate\, and advance within one of the largest public transportation systems in North America.
URL:https://c2smart.engineering.nyu.edu/event/mta-x-nyu-demystifying-the-application-process/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20260311T120000
DTEND;TZID=America/New_York:20260311T130000
DTSTAMP:20260416T120659
CREATED:20260304T145623Z
LAST-MODIFIED:20260304T145623Z
UID:90556-1773230400-1773234000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: AI Agent 101 and Vibe Coding Demo
DESCRIPTION:This talk introduces the core concepts behind modern AI agents—from (Large Language Models) LLMs and memory to tool integration\, reusable agent skills\, and autonomous workflows. Participants will gain a clear understanding of how prompting\, function calling\, and multi-agent coordination work in practice. The webinar will include a live “vibe coding” demo to prototype use cases such as a transportation data dashboard using Google AI Studio\, VS Code + AI extensions\, and Claude Desktop to demonstrate how AI agents can orchestrate tools and skills to accelerate development\, automate data workflows\, and support transportation analytics and decision-making. \nPresented by Dr. Yu Hu\, Senior Software Engineer at Comcast.
URL:https://c2smart.engineering.nyu.edu/event/webinar-ai-agent-101-and-vibe-coding-demo/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251121T120000
DTEND;TZID=America/New_York:20251121T130000
DTSTAMP:20260416T120659
CREATED:20251016T154633Z
LAST-MODIFIED:20251016T154633Z
UID:89901-1763726400-1763730000@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Building a Scalable Multithreaded Processing System on AWS Cloud
DESCRIPTION:This lecture introduces the design and implementation of a serverless multithreaded processing system using AWS Step Functions\, AWS Lambda\, and Amazon S3. The session covers how to orchestrate parallel tasks\, manage state transitions\, and scale processing pipelines efficiently on the AWS cloud platform. Emphasis will be placed on cloud-native scalability\, fault tolerance\, and cost optimization through automation and serverless architecture. Hosted by NYU’s Zhi Jie (Jeffrey) Wang!
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-building-a-scalable-multithreaded-processing-system-on-aws-cloud/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251112T130000
DTEND;TZID=America/New_York:20251112T140000
DTSTAMP:20260416T120700
CREATED:20251020T153149Z
LAST-MODIFIED:20251020T153518Z
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251107T120000
DTEND;TZID=America/New_York:20251107T130000
DTSTAMP:20260416T120700
CREATED:20251016T154452Z
LAST-MODIFIED:20251016T154452Z
UID:89896-1762516800-1762520400@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Introduction to Online Recommendation Systems: From Predict-Sort Architecture to Real-Time Feature Service
DESCRIPTION:This session introduces how modern online recommendation systems\, like TikTok shop\, deliver personalized content in real time. I’ll walk through the overall system architecture and then focus on the predict-sort stage\, explaining how BFS (Bytedance Feature Service) and UDA (User Data Accessor) enable large-scale feature computation using a DAG-based (Directed Acyclic Graph) operator framework and a domain-specific language (DSL). Participants will gain insight into how these components work together to achieve millisecond-level predictions. Hosted by NYU’s Hongdao Meng!
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-introduction-to-online-recommendation-systems-from-predict-sort-architecture-to-real-time-feature-service/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20251016T180000
DTEND;TZID=America/New_York:20251016T190000
DTSTAMP:20260416T120700
CREATED:20251014T190229Z
LAST-MODIFIED:20251016T162246Z
UID:89870-1760637600-1760641200@c2smart.engineering.nyu.edu
SUMMARY:Author Talk: A Conversation with Michael M. Greenburg
DESCRIPTION:This Thursday is the NYU Press Author Talk with Mr. Michael Greenburg. The book focuses on William LeMessurier\, the structural engineer who discovered a fatal flaw in his building’s design and his decision to blow the whistle on himself. Please make sure you register for the event here if you plan to attend. \nFood & refreshments provided.
URL:https://c2smart.engineering.nyu.edu/event/author-talk-a-conversation-with-michael-m-greenburg/
LOCATION:Dibner Library\, 5 MetroTech\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Conferences,Seminars,Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250507T130000
DTEND;TZID=America/New_York:20250507T140000
DTSTAMP:20260416T120700
CREATED:20250422T173123Z
LAST-MODIFIED:20250506T154823Z
UID:88555-1746622800-1746626400@c2smart.engineering.nyu.edu
SUMMARY:Prof. Vinitsky Seminar: Robust Self-driving Emerges from Self-play
DESCRIPTION:Self-play has powered breakthroughs in two-player and multi-player games. Here we show that self-play is a surprisingly effective strategy in another domain. We show that robust and naturalistic driving emerges entirely from self-play in simulation at unprecedented scale — 1.6~billion~km of driving. This is enabled by Gigaflow\, a batched simulator that can synthesize and train on 42 years of subjective driving experience per hour on a single 8-GPU node. The resulting policy achieves state-of-the-art performance on three independent autonomous driving benchmarks. The policy outperforms the prior state of the art when tested on recorded real-world scenarios\, amidst human drivers\, without ever seeing human data during training. The policy is realistic when assessed against human references and achieves unprecedented robustness\, averaging 17.5 years of continuous driving between incidents in simulation.
URL:https://c2smart.engineering.nyu.edu/event/prof-vinitsky-seminar-robust-self-driving-emerges-from-self-play/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250411T120000
DTEND;TZID=America/New_York:20250411T130000
DTSTAMP:20260416T120700
CREATED:20250304T191106Z
LAST-MODIFIED:20250304T191106Z
UID:87962-1744372800-1744376400@c2smart.engineering.nyu.edu
SUMMARY:SLH: Discrete Choice Modeling for Travel Behavior Analysis: From Multinomial Logit to More Advanced Forms
DESCRIPTION:Abstract: In this course\, we will discuss the decision theory of random utility maximization and discrete choice models (DCMs) including multinomial logit (MNL)\, nested logit (NL)\, mixed logit (MXL)\, and agent-based mixed logit (AMXL). You will learn about their applications in travel behavior analysis (e.g.\, travel mode choice\, activity scheduling choice\, etc.) and how to build DCMs with long-shape and wide-shape choice datasets in R and Python. A recent study on New York State travel mode choice will be introduced as an example.
URL:https://c2smart.engineering.nyu.edu/event/slh-discrete-choice-modeling-for-travel-behavior-analysis-from-multinomial-logit-to-more-advanced-forms/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250314T120000
DTEND;TZID=America/New_York:20250314T130000
DTSTAMP:20260416T120700
CREATED:20250304T190838Z
LAST-MODIFIED:20250304T190838Z
UID:87959-1741953600-1741957200@c2smart.engineering.nyu.edu
SUMMARY:SLH: An Introduction to Multi-agent Driving Simulation with GPUDrive
DESCRIPTION:In this seminar\, I will introduce GPUDrive\, a high-performance\, data-driven driving simulator that operates at 1 million FPS. GPUDrive is built on the Madrona Game Engine and uses GPU acceleration to enable scalable multi-agent simulation. I will discuss how this simulator can be used to train reinforcement learning agents efficiently\, drawing from a recent paper where we demonstrate its application to the Waymo Open Motion Dataset. Additionally\, I will walk through tutorials on how to set up and use the simulator. \nThe repository can be found at https://github.com/Emerge-Lab/gpudrive/tree/main
URL:https://c2smart.engineering.nyu.edu/event/slh-an-introduction-to-multi-agent-driving-simulation-with-gpudrive/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250307T130000
DTEND;TZID=America/New_York:20250307T140000
DTSTAMP:20260416T120700
CREATED:20250304T190540Z
LAST-MODIFIED:20250304T190540Z
UID:87955-1741352400-1741356000@c2smart.engineering.nyu.edu
SUMMARY:SLH: Understanding Travel Demand through Passively-generated Mobile Data: a Python-based Mobility Analysis Workshop
DESCRIPTION:The ubiquity of GPS-equipped mobile devices has enabled the collection of human mobility data with high spatiotemporal granularity. Indeed\, there now exists an ecosystem of both data providers and consulting agencies centered around collecting\, processing\, and extracting insights from this type of data. Though much of the raw data cannot be made public due to privacy-preserving agreements\, the academic community can still access geographically aggregated data. This workshop will cover the (Python-based) preprocessing and wrangling of aggregated mobility data in New York state provided by SafeGraph. It will highlight concepts including (but not limited to) origin-destination matrices\, the four-step travel demand model\, self-selection bias in passively-generated data\, and demand prediction. It will also describe how to fuse such data with the American Community Survey (ACS) providing sociodemographic information at the census block group-level. Relevant packages that will be leveraged and explained include GeoPandas and scikit-Mobility. Intermediate familiarity with Python\, data structures\, and object-oriented programming is recommended.
URL:https://c2smart.engineering.nyu.edu/event/slh-understanding-travel-demand-through-passively-generated-mobile-data-a-python-based-mobility-analysis-workshop/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250218T140000
DTEND;TZID=America/New_York:20250218T150000
DTSTAMP:20260416T120700
CREATED:20250116T203640Z
LAST-MODIFIED:20250218T163925Z
UID:87727-1739887200-1739890800@c2smart.engineering.nyu.edu
SUMMARY:Seminar: Gittins Indices for Cost-aware and Freeze-thaw Bayesian Optimization
DESCRIPTION:Presented by Qian Xie\, Cornell \nHyperparameter optimization is crucial in real-world applications such as machine learning model training\, robotics control\, material design\, and plasma physics. In transportation\, hyperparameter optimization plays a significant role in applications like traffic flow prediction\, dynamic pricing\, route planning\, and public transportation scheduling\, where complex models need to be fine-tuned to achieve optimal performance. These scenarios are often modeled as black-box functions\, which take hyperparameters as inputs and output performance metrics. Bayesian optimization is a powerful framework for efficiently optimizing such black-box functions\, especially when evaluations are time-consuming or expensive. However\, practical factors such as varying function evaluation costs and observable partial feedback during function evaluation remain under-explored in this framework. My research leverages Gittins indices\, which are inherently cost-aware and feedback-aware\, by drawing connections to Pandora’s Box problems and Markovian/Bayesian bandits\, where Gittins indices are Bayesian optimal. \nIn the first half of my talk\, I will present my published work\, which adapts Gittins indices into a cost-aware acquisition function class for Bayesian optimization\, demonstrating competitive empirical performance\, particularly in medium-to-high dimensions. In the second half\, I will discuss my ongoing work on developing Gittins indices for freeze-thaw Bayesian optimization involving decisions on early stopping and switching of hyperparameter tests based on partial feedback.
URL:https://c2smart.engineering.nyu.edu/event/seminar-gittins-indices-for-cost-aware-and-freeze-thaw-bayesian-optimization/
LOCATION:C2SMART Center Viz Lab\, 6 Metrotech Center\, Room 460\, Brooklyn\, 11201
CATEGORIES:Seminars,Virtual Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241108T130000
DTEND;TZID=America/New_York:20241108T140000
DTSTAMP:20260416T120700
CREATED:20241007T153043Z
LAST-MODIFIED:20241108T174728Z
UID:86128-1731070800-1731074400@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Online Optimization Meets Urban Transportation
DESCRIPTION:Instructor: Tao Li\, New York University \nBeginner level: No prior experience required. Basic optimization knowledge would be helpful but not required. \nDescription: Urban transportation networks are complex and dynamic\, and as a result\, offline planning alone may not ensure effective real-time management. Therefore\, this course introduces online optimization methods with a focus on transportation applications. We’ll start with gradient descent in conventional convex optimization\, move to online gradient descent\, and briefly explore multi-agent online learning and associated equilibrium convergence. We’ll also discuss challenges in deploying online optimization in urban transportation.
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-online-optimization-meets-urban-transportation/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241108T103000
DTEND;TZID=America/New_York:20241108T113000
DTSTAMP:20260416T120700
CREATED:20241024T202005Z
LAST-MODIFIED:20241028T170122Z
UID:86306-1731061800-1731065400@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Bridging the Gap: Enhancing Infrastructure Safety with Robust Vibration-based Monitoring
DESCRIPTION:Abstract: The idea that the majority of bridges have reached the end of their service life has become widely accepted. The need for continuous monitoring of a large number of structures has become both a duty and a burden for administrations and operators. While technological advancements enable the acquisition of numerous structural parameters\, effectively harnessing the vast amount of data generated is not a straightforward task. Therefore\, an automated tool that can conduct end-to-end analysis with minimal effort and cost is crucial. The presented solution is applied to a monumental reinforced concrete arch bridge\, and instrumented with tailored monitoring system\, from sensors to the cloud-based dashboard. Modal parameters such as vibration modes\, modal shapes\, and damping are determined using the Operational Modal Analysis (OMA) algorithm\, specifically 2 nd order blind identification\, in an automated process. The analysis is performed through robust software for automatic modal identification\, providing high quality results purified by any environmental effects. Finally\, by enhancing the potential of the cloud for measurement data storage\, the implementation of advanced data management tools is being considered as interesting emerging future prospects. \nBio: Giacomo Imposa is a structural engineer with a PhD in Structural Health Monitoring (SHM) from Iuav University of Venice\, Italy\, specializing in dynamic identification of masonry structures and bridges. His academic pursuits have primarily unfolded in Italy\, with a notable stint in Portugal\, in Guimaraes\, where he was hosted by Prof. Lourenco\, the most important expert in the world regarding Heritage buildings. At Kistler\, since January\, Giacomo has been globally engaged in advancing new bridge-related applications concerning structural monitoring in his capacity as a business developer. His responsibilities encompass the entire measurement chain\, spanning from sensors to data\, encapsulated through bridge reporting.
URL:https://c2smart.engineering.nyu.edu/event/webinar-bridging-the-gap-enhancing-infrastructure-safety-with-robust-vibration-based-monitoring/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241106T170000
DTEND;TZID=America/New_York:20241106T180000
DTSTAMP:20260416T120700
CREATED:20241016T194032Z
LAST-MODIFIED:20241028T194901Z
UID:86225-1730912400-1730916000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Tension Stiffening Related to Cracking and Deflection of Reinforced Concrete
DESCRIPTION:Hardy Cross once wrote that “strength is essential and otherwise unimportant” to emphasize it makes little difference what other attributes a structure has if it is not sufficiently strong. Looking at this from a different perspective one could say that “strength is essential and otherwise unimportant … when serviceability governs” to ensure the structure performs adequately under the day-to-day service conditions once we know the structure has adequate strength. In fact\, certain types of structures such as elevated slabs and fiber reinforced polymer (FRP) reinforced concrete members are often stronger than needed once serviceability requirements are satisfied\, and in these types of cases the structure is designed for serviceability first and then checked for strength. Hence\, safety and serviceability related to cracking and deflection play an important role in the design of concrete structures. \nThis presentation is focused on deflection of reinforced concrete. The role of cracking\, tension stiffening and shrinkage in understanding deflection behavior of reinforced concrete is explored. In particular\, emphasis is placed on the pitfalls of ignoring the concrete shrinkage. Rationale and justification are provided for the recent adoption by most codes in North America of a new approach for the effective member stiffness used to calculate deflection. Work is presently underway with colleagues from Rutgers to extend this approach to prestressed concrete. \nPresented by Peter H. Bischoff\, Department of Civil Engineering\, University of New Brunswick\, Fredericton\, NB\, Canada \nPeter H. Bischoff is an Honorary Research Professor in the Department of Civil Engineering at the University of New Brunswick in Fredericton\, New Brunswick\, Canada\, where he has contributed to teaching and research in reinforced and prestressed concrete. He is a fellow of the American Concrete Institute (ACI) and Canadian Society for Civil Engineering (CSCE) and has served on numerous ACI Committees during his career. Dr. Bischoff is a recognized expert in serviceability of concrete structures related to deflection and has received several awards for work in this area. He has also been involved in development of a design code for fiber reinforced concrete (FRC) and UHPC that has been implemented into the Canadian Highway Bridge Design Code S6.
URL:https://c2smart.engineering.nyu.edu/event/webinar-tension-stiffening-related-to-cracking-and-deflection-of-reinforced-concrete/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241030T100000
DTEND;TZID=America/New_York:20241030T110000
DTSTAMP:20260416T120700
CREATED:20241014T193918Z
LAST-MODIFIED:20241028T193657Z
UID:86211-1730282400-1730286000@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Spatio-temporal Adaptive AI for Urban Mobility Modeling
DESCRIPTION:Abstract: Rapidly developing mobile and sensor networks are accumulating massive volumes of human mobility data in cities. Predictive modeling on these data is a fundamental problem in building decision support systems for various urban and transportation applications. In the real world\, such spatio-temporal data show multifold heterogeneity over space and non-stationarity over time\, which makes the prediction task especially challenging. My research focuses on developing adaptive AI algorithms (e.g.\, meta learning) to enhance the robustness of mobility prediction models. Making robust predictions across space and time lays a foundation for not only next generation mobility services\, but also emergency response to adverse events\, including traffic accidents\, pandemic lockdown\, and natural disasters. \nBio: Zhaonan Wang joined NYU Shanghai in Fall 2024 as a Tenure-Track Assistant Professor jointly appointed by Urban Studies and Computer Science. Zhaonan obtained his PhD degree in 2022 from the University of Tokyo\, advised by Ryosuke Shibasaki; during his PhD study\, he also visited the research group led by Flora Salim\, Cisco Chair at UNSW CSE. After graduation\, Zhaonan did his postdoctoral research on Spatial AI under the supervision of Shaowen Wang and Jiawei Han at the University of Illinois Urbana-Champaign (CyberGIS Center & NSF I-GUIDE). Zhaonan’s research interests lie in the interdisciplinary area between urban & AI\, and he’s been published in top-tier AI and data science venues\, including AAAI\, KDD\, WWW\, CIKM\, ICDE\, AIJ\, TKDE. He’s also been invited to serve as a Program Committee member and awarded travel grants for multiple times. During his PhD\, Zhaonan won a MEXT (Japanese government) scholarship in Top Global University Initiative\, Top-10 candidates in KDD Cup 2019 Humanity RL Track\, and best resource paper runner-up at ACM CIKM 2021. He also had close collaboration with industry\, including the National Institute of Advanced Industrial Science and Technology (AIST in Japan)’s AI Research Center\, Yahoo Japan\, and Toyota.
URL:https://c2smart.engineering.nyu.edu/event/zhaonan-wang-webinar/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241011T130000
DTEND;TZID=America/New_York:20241011T140000
DTSTAMP:20260416T120700
CREATED:20240925T141023Z
LAST-MODIFIED:20241007T155409Z
UID:86094-1728651600-1728655200@c2smart.engineering.nyu.edu
SUMMARY:Student Learning Hub: Comprehensive Urban Data Collection Using Drones: From Licensing to Advanced Sensor Applications
DESCRIPTION:Instructor: Tu Lan & Vivaldi Rinaldi\, New York University\nBeginner level: No prior experience required.\nSchedule: Friday\, October 11\, 1:00 pm – 2:00 pm ET\nDescription: Drones are becoming an invaluable tool for urban data collection\, offering a wide range of applications from traffic monitoring to environmental analysis. This course introduces participants to the full scope of using drones for traffic data collection\, starting with the necessary FAA licensing and airspace regulations\, followed by practical guidance on drone operation and control. We’ll explore the different sensors and gimbals that can be equipped on drones\, such as cameras\, thermal imaging\, and LiDAR\, and their respective applications in collecting urban data. This session provides a comprehensive understanding of how drones can be utilized for diverse data-driven urban analyses.
URL:https://c2smart.engineering.nyu.edu/event/student-learning-hub-comprehensive-urban-data-collection-using-drones-from-licensing-to-advanced-sensor-applications/
CATEGORIES:Student Events,Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20240816T113000
DTEND;TZID=America/New_York:20240816T123000
DTSTAMP:20260416T120700
CREATED:20240808T153029Z
LAST-MODIFIED:20240821T153927Z
UID:85874-1723807800-1723811400@c2smart.engineering.nyu.edu
SUMMARY:Webinar: Prof. Morteza Bagheri\, Iran University of Science and Technology (IUST)
DESCRIPTION:Hosted by Morteza Bagheri\, Associate Professor\, Iran University of Science and Technology (IUST) \nRecently\, the United States has faced several significant freight train derailments involving hazardous materials (HAZMAT)\, raising serious concerns about rail safety. This seminar investigates various approaches to mitigate the risks associated with HAZMAT transport by rail. Through this seminar\, we aim to explain the critical importance of effective risk management in HAZMAT transportation and emphasize the necessity of collaboration among all stakeholders to ensure everyone’s safety.
URL:https://c2smart.engineering.nyu.edu/event/webinar-morteza-bagheri/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230922T113000
DTEND;TZID=America/New_York:20230922T123000
DTSTAMP:20260416T120700
CREATED:20230912T184858Z
LAST-MODIFIED:20230912T185245Z
UID:80560-1695382200-1695385800@c2smart.engineering.nyu.edu
SUMMARY:Reducing US Transit Costs: An Empirical Review and Comparative Case Study of Portland\, Manchester Rail Systems
DESCRIPTION:Presented by Chetan Sharma and Prof. Joseph Chow
URL:https://c2smart.engineering.nyu.edu/event/reducing-us-transit-costs-an-empirical-review-and-comparative-case-study-of-portland-manchester-rail-systems/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230907T120000
DTEND;TZID=America/New_York:20230907T130000
DTSTAMP:20260416T120700
CREATED:20230810T200745Z
LAST-MODIFIED:20250514T173423Z
UID:79990-1694088000-1694091600@c2smart.engineering.nyu.edu
SUMMARY:NY Statewide Behavioral Impact Decision Support Tool with Replica
DESCRIPTION:One of the enduring challenges in statewide transportation planning is that consistent population travel data remains scarce. This is changing with the availability of large-scale ICT data. A one-year project was initiated to develop a behavioral impact decision support tool based on NY statewide synthetic population data provided by Replica Inc. First\, a NY statewide model choice model is developed to deterministically fit heterogeneous coefficients for trips along each census block-group OD pair\, called group-level agent-based mixed (GLAM) logit. Considerations were made for four population segments\, six trip modes\, and twelve attributes. Second\, a decision support tool for statewide mobility service region design was proposed. The tool is based on an assortment optimization problem with agent-specific coefficients and linear constraints\, which can be efficiently solved through linear or quadratic programming (depending on variant). The decision support tool is applied to optimize service regions with one of the three objectives: (1) maximizing the total revenue; (2) maximizing the total change of consumer surplus; (3) minimizing the disparity between communities. \nPresented by Xiyuan Ren; PHD Candidate\, NYU; Joseph Chow\, Associate Professor\, NYU
URL:https://c2smart.engineering.nyu.edu/event/ny-statewide-behavioral-equity-impact-decision-support-tool-with-replica/
CATEGORIES:Webinars
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20230814T120000
DTEND;TZID=America/New_York:20230814T130000
DTSTAMP:20260416T120700
CREATED:20230810T200203Z
LAST-MODIFIED:20230810T200252Z
UID:79982-1692014400-1692018000@c2smart.engineering.nyu.edu
SUMMARY:Digital Twin Technologies Towards Understanding the Interactions between Transportation and other Civil Infrastructure Systems: Phase 2
DESCRIPTION:
URL:https://c2smart.engineering.nyu.edu/event/digital-twin-technologies-towards-understanding-the-interactions-between-transportation-and-other-civil-infrastructure-systems-phase-2/
CATEGORIES:Big Data & Planning for Smart Cities,Webinars
LOCATION:https://nyu.zoom.us/webinar/register/WN_jQg9UllxRB26w9QmMn9OqQ
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20221116T160000
DTEND;TZID=America/New_York:20221116T170000
DTSTAMP:20260416T120700
CREATED:20221005T141434Z
LAST-MODIFIED:20221111T225455Z
UID:77918-1668614400-1668618000@c2smart.engineering.nyu.edu
SUMMARY:Connected Vehicle Applications: Lessons Learned and Future Research & Deployment Roundtable
DESCRIPTION:The USDOT Connected Vehicle (CV) Pilot Program sought to test vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) applications to improve tranportation systems\, mobility\, and safety with real-world deployments in New York City\, Tampa\, and Wyoming. \nThis roundtable discussion will focus on these recently completed connected vehicle pilots and the lessons learned. Our panelists will feature practitioners\, decision-makers\, and researchers involved in CV deployments and leading the way for their wide-scale adoption. \nThis roundtable caps off C2SMART’s State of the Field: Connected Vehicle Applictions series\, and there will be a synthesis of prior presentations and a discussion on future directions and applications for research\, testing\, and deployment. \nWe look forward to your participation! \nPanelists: \n \nSisinnio Concas serves as Program Director at the Center for Urban Transportation Research (CUTR) and Research Associate Professor the University of South Florida (USF) College of Engineering. Dr. Concas has over 20 years of experience as a transportation economist conducting economic impact and benefit-cost analyses for public transportation\, airport and roadway projects. Dr. Concas leads CUTR’s Autonomous & Connected Mobility Evaluation (ACME) Program. ACME focuses on producing quick-response solutions to better inform practitioners and policy maker in selecting and prioritizing cost-feasible innovative transportation alternatives. He has performed numerous research projects for the U.S. Federal Transit Administration\, Federal Highway Administration\, the Florida Department of Transportation\, state and local transportation authorities. Dr. Concas leads the Performance Measurement Evaluation and Support of the Tampa CV Pilot Deployment. \n \nDr. Mohamad Talas is the Deputy Director of ITS System Engineering\, New York City Department of Transportation. He brings long standing career experience in traffic engineering and continue with over 27 years in Traffic Engineering and Operation experience in New York City Department of Transportation. He currently serves as the Director for the NYC Department of Transportation ITS project Management\, Research and Development where he supervises the Intelligent Transportation System projects and initiatives in New York City. These projects include the development and implementation of the New York City Traffic Computerization System at the Traffic Management Center modernizing and operating over 12\,000 signals and the currently deployed Active Traffic Management System in in Manhattan(Midtown In Motion) and NYC Connected Vehicle Pilot Deployment. He has earned his PhD in Transportation Planning and Engineering at NYU -Poly University\, Master degrees in Transportation\, Planning and Engineering and a Masters in Electrical Engineering from Fairleigh Dickinson University. \n \nBilly Chupp is a data analyst and engineer at the U.S. Department of Transportation’s Volpe Transportation Systems Center in Cambridge\, Massachusetts. Mr. Chupp supports a wide range of projects at the Volpe Center including cloud database and analysis system development and management for DOT’s Chief Data Officer\, artificial intelligence and machine learning development initiatives for DOT’s ITS Joint Program Office\, and air quality modeling and data analysis projects for the Federal Highway Administration. Mr. Chupp most recently served as the technical lead on Volpe’s independent safety evaluation effort for the three connected vehicle pilot programs in New York City\, Tampa\, and Wyoming\, and continues to support the ITS JPO on data documentation and strategy efforts within the connected vehicle space and beyond. \n \nDr. Karl Wunderlich holds a joint appointment at Noblis in Washington\, DC.\, serving as both is the Director of the Surface Transportation Division and the Director of the Noblis Autonomous Systems Research Center. He is a key contributor to both research and development projects and technology deployment programs sponsored by the US Department of Transportation (USDOT) and the Federal Highway Administration (FHWA). Dr. Wunderlich is an expert in the use of simulation techniques to evaluate the potential impact of emerging technologies to improve traveler mobility or system productivity – including vehicle connectivity\, autonomy\, and blockchain. He is a published author and patent-holder in orchestrated autonomy\, which leverages blockchain to create efficient and collision-free path planning among heterogenous\, unfamiliar\, and autonomous machines. Dr. Wunderlich holds a Ph.D. in Operations Research from the University of Michigan. \nDr. Kaan Ozbay is a Professor at New York University’s Tandon School of Engineering\, and Director of C2SMART Center\, a Tier 1 USDOT University Transportation Center. Dr. Ozbay served as Principal Investigator (PI) of the NYU/C2SMART team as part of the NYCDOT-led New York City Connected Vehicle Pilot\, under USDOT’s Connected Vehicle Pilot Program. He joined NYU’s Department of Civil and Urban Engineering and Center for Urban Science and Progress (CUSP) in August 2013\, and is also Global Network Professor of Civil and Urban Engineering\, NYU Abu Dhabi (NYUAD) and Global Network Professor of Engineering and Computer Science\, NYU Shanghai (NYUSH). \nModerated by: \nJingqin (Jannie) Gao completed her Ph.D. in Transportation Planning and Engineering at NYU Tandon\, where she works with C2SMART Director Kaan Ozbay. She studied Science and Technology of Optical Information and received her B.S. from Tongji University in China and her M.S in Transportation Planning and Engineering from New York University. Her research interests lie in offline and real-time simulation modeling\, big data and machine learning approach for transportation\, and transportation economics. She also worked for the New York City Department of Transportation on modeling and data analysis to support the agency’s internal planning\, technical review processes and coordinated with external agencies on regional projects since 2012. Jingqin is the former president of NYU’s joint Institute of Transportation Engineers (ITE) and The Intelligent Transportation Society of America (ITS) Student Chapter during 2018-2019\, through which she organized various company visits\, tech talks\, women in transportation events and the 2019 ITE Northeastern District Traffic Bowl.
URL:https://c2smart.engineering.nyu.edu/event/connected-vehicle-applications-lessons-learned-and-future-research-deployment-roundtable/
LOCATION:Virtual\, 6 MetroTech Center\, Brooklyn\, NY\, 11201\, United States
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220420T120000
DTEND;TZID=America/New_York:20220420T130000
DTSTAMP:20260416T120700
CREATED:20220411T132807Z
LAST-MODIFIED:20220415T204328Z
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:20260416T120700
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/
CATEGORIES:Connected & Autonomous Mobility,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220224T120000
DTEND;TZID=America/New_York:20220224T130000
DTSTAMP:20260416T120700
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/
CATEGORIES:Infrastructure Resiliency,Virtual Events,Webinars
ORGANIZER;CN="C2SMART":MAILTO:c2smart@nyu.edu
LOCATION:https://c2smart.engineering.nyu.edu/event/state-of-the-field-new-fiber-optic-structural-health-monitoring-shm-technologies-and-recent-case-studies/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20211207T120000
DTEND;TZID=America/New_York:20211207T133000
DTSTAMP:20260416T120700
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/
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:20260416T120700
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:20260416T120700
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:20260416T120700
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
END:VEVENT
END:VCALENDAR