Stairway Towards Systematic Review: Utilizing Rayyan Software & PRISMA Guidelines

Virtual 6 MetroTech Center, Brooklyn, NY, United States

This course covers the basics of graph neural networks (GNNs), including graph  convolutional networks, graph attention networks and graph wavelet networks. The  session aims to provide a solid understanding of GNN modeling for machine  learning practitioners, data scientists, and anyone interested in AI advancements. 

Python for GIS: An Introduction to OSMnx

Virtual 6 MetroTech Center, Brooklyn, NY, United States

In this course, you will learn how to harness the power of OSMnx, a Python library for extracting and visualizing Open Street Maps data. Through hands-on exercises, you will gain practical experience in using OSMnx to model and simulate projects and have a solid understanding of this Python library and be able to apply OSMnx to real-world problems.

Old Models with New Tricks: Bridging the Gap in Bureau of Public Roads (BPR) Functions with A Cross-Resolution Perspective of Theoretical Fundamentals and Emerging Applications

Virtual 6 MetroTech Center, Brooklyn, NY, United States
Virtual Event Virtual Event

  In transportation planning, volume-delay functions (VDFs) are essential functions used for traffic assignment and network design problems. However, the static Bureau of Public Roads (BPR) function, created by the

KAIST-NYU: KN-C³ Workshop

C2SMART Center Viz Lab 6 Metrotech Center, Room 460, Brooklyn

C2SMART is thrilled to welcome members of faculty from KAIST (Korea Institute for Advanced Study of Science and Technology).

Learning from big and small data for transportation planning and resilience analysis

Virtual Event Hybrid Event

COVID has exacerbated two emerging trends in transportation analysis: (1) the rise of passively-generated big data; and (2) the increasing need to deal with the “unexpected” disruptions. This talk emphasizes the need for learning big and small data for transportation planning and resilience analysis. Different ways of learning are described, with applications ranging from long-term planning analysis to rapid responses under disruptions.

Deep Neural Networks for Choice Analysis

Virtual Event Hybrid Event

This presentation introduces a deep choice framework that synergizes DNNs and DCMs to model individual travel decision.

Summer Webinar Series

Virtual Event Virtual Event

Presented by the Transportation Research Board (TRB) subcommittee AEP30(2) Route Choice and Spatio-Temporal Behavior Speaker: Professor Marcela A. Munizaga, Universidad de Chile.

Webinar: Autonomous Vehicle Good Citizenry Standard

Virtual Event Virtual Event

Presented by: Sarah Kaufman, Interim Director, NYU Rudin Center for Transportation Joseph Chow, Associate Professor, NYU Thursday, June 29, 2023: 12:00pm - 1:00pm ET | Virtual New York City is