Connected Cities for Smart Mobility toward Accessible and Resilient Transportation
Instructor: Zhanhang Li, Rutgers University Hands-on exercise: Yes Level: No prior experience required. Schedule: Wednesday, November 11, 2022, 1:00pm – 2:00pm Description: This course will provide students with a foundational understanding of machine learning
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,
Instructor: Hella Alnajjar, New York University Hands-on exercise: Yes Beginner level: No prior experience required. Schedule: Friday, November 18, 2022, 1:00pm – 2:00pm Description: This course is for students who want to learn how
As transportation is increasingly electrified, understanding travel and charging choices of electric vehicle drivers and users of shared electric mobility systems is crucial for service planning and operations. Ultimately, quantitative
Transportation Techies is a group of technologists, urban planners, commuters and more who are interested in how technology can enable the future of mobility in metropolitan areas.
Dr. Linda Ng Boyle is a Professor of Industrial Systems Engineering & Civil and Environmental Engineering at the University of Washington
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
3On February 22, 2023, New York University and Tongji University are joining forces to host an online urban transportation forum. The topics to be covered in this virtual event include
Instructor: Fan Zuo, New York University Schedule: Friday March 10, 11:00am-12:00pm Description: In this course, you will learn about ChatGPT, a state-of-the-art language model developed by OpenAI. You will gain
Graph Neural Networks (GNNs) are a powerful tool for analyzing and processing graph-structured data. This course covers the basics of GNNs, including graph convolutional networks, graph attention networks and graph wavelet networks, including how they work and how to implement them. The session aims to provide a solid understanding of GNN modeling for machine learning practitioners, data scientists, and anyone interested in AI advancements.
The C2SMART Center and the NYU chapter of the Institute of Transportation Engineers (ITE) are excited to host our annual Women in Transportation Panel Discussion, including an opportunity for attendees to network and socialize. This year's panel boasts three amazing women working in the transportation industry who will share their perspectives and experiences.
In this hands-on course, you’ll learn how to use Unity 3D to build 3D models of road networks. You’ll start with the basics of Unity 3D, including its interface, GameObjects, components, and scripting. From there, you’ll dive into building 3D models of road networks, covering elements such as roads, buildings, and terrain.