C2SMART has designated funding for 12 new projects that seek to tackle a wide range of issues in transportation, both building on the center’s current work and investigating new challenges in the center’s research areas, including C2SMART’s new focus on underrepresented and disadvantaged populations in transportation. C2SMART, a USDOT Tier 1 University Transportation Center led by NYU Tandon School of Engineering, uses cities as living laboratories to study challenging transportation problems and find solutions from the unprecedented recent advances in communication and smart technologies.
A request for proposals was put out in November 2018, and proposals were solicited from C2SMART’s member institutions. Submitted proposals underwent a multi-step review process, including multiple independent reviews.
“Impact Of Ride-Sharing In New York City,” led by Associate Professor of Practice Stanislav Sobolevsky, will be one such project supported by C2SMART, with funding by US DOT and matching contributions from ARCADIS for one year.
The project will develop a citywide data-driven transportation simulation modeling framework for probabilistic assessment of the associated mode-shift and resulting environmental, social and economic impacts of ride-sharing solutions (e.g. UberPOOL, Lyft shared etc) on urban transportation system in New York City efficiently leveraging available partial transportation data. The impacts in question include: travel time cut for passengers, reduction of traffic, gas consumption/ emissions by type (CO, NOx, PM2.5), travel time/cost savings for passengers, increased earnings for Lyft and Uber drivers, jobs for for-hire-vehicle drivers. Once developed, the new framework is readily applicable to the predictive assessment of the impacts of many other transportation pricing and policy decisions, such as Manhattan congestion charge which is scheduled to come into effect by January,2019 – additional use case depicting it will be provided.
Professor Sobolevsky is the head of the Urban Complexity Group at NYU CUSP. The Urban Complexity Group is unfolding the complexity of urban systems for research, innovation and applications, and applies cutting edge data science, machine learning and network analysis techniques to leverage big urban data for making our cities more smart, efficient, sustainable, resilient – a better place to live in.