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Incentive Design for Promoting Ridesharing

Virtual 6 MetroTech Center, Brooklyn, NY, United States

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. This event addresses these obstacles by introducing a Traveler Incentive Program (TIP) to promote community-based ridesharing with a ride-back home guarantee among commuters.

Employer-Backed Bikes Concept Showcase

Virtual 6 MetroTech Center, Brooklyn, NY, United States

Join the New York City Economic Development Corporation (NYCEDC), in partnership with NYU C2SMART Center, Fraunhofer, and Nelson\Nygaard, as they convene public and private stakeholders toward implementing this bike access model in NYC.

Transportation Camp NYC 2021

Virtual 6 MetroTech Center, Brooklyn, NY, United States

TransportationCamp is an unconference – every session is planned, proposed, and led by attendees like you. It’s the perfect opportunity to share your transportation passion with a diverse gathering of fellow enthusiasts.

Proactive Safety Management Empowered by Big Data

Virtual 6 MetroTech Center, Brooklyn, NY, United States

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.

Flooding and Public Transportation

The NYU Rudin Center for Transportation presents a virtual panel discussion about mitigating climate change impacts, keeping transit riders safe and reassured, and activating alternative modes.

NYC 2025: Road to Recovery Series

Virtual 6 MetroTech Center, Brooklyn, NY, United States

The inaugural event of the NYC 2025: Road to Recovery Series, a partnership between NYU Wagner and the Stern Center for Sustainable Business, presents an interactive panel featuring contributors to NYC 2025. Panelists will discuss their vision for a stronger, fairer and more equitable New York City.

State of the Field: Structural Health Monitoring (SHM) towards Infrastructure Resiliency

Virtual 6 MetroTech Center, Brooklyn, NY, United States

What does the best of transportation engineering research have to say about maintenance, rehabilitation, and replacement of critical infrastructure? 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.

Using AI to Improve CAV Operations in Mixed Traffic

Dr. Sikai (Sky) Chen will discuss recent developments in vehicle automation with mixed traffic stream, including the challenges and opportunities associated with AI/ML algorithm development and application for CAV operations.

Equity in Transportation Research

Virtual 6 MetroTech Center, Brooklyn, NY, United States

As C2SMART heads into its sixth year of projects, we are reshaping our RFP process to solicit a project slate which will be bigger and bolder than ever before. We are looking for projects which showcase possibility, addresses complex challenges, broadens collaboration, and directly strengthens the transportation field. Across and underlying each of these themes, however, will be an emphasis on transportation equity. Each proposal will, in some form, need to be prepared to discuss how the project will directly address equity concerns, or else include an equity performance measure of some kind.

Roadmap to Cooperative & Automated Transportation

Virtual 6 MetroTech Center, Brooklyn, NY, United States

To realize the full potential of AVs, Dr. Li proposes a roadmap of cooperative & automated transportation, from optimal trajectory control in ideal conditions through a cooperative control framework incorporating edge computing and machine learning under real-world constraints.