Impact of Ridesharing in New York City
With a pilot use case of ride-sharing offered through UberPOOL, Lyft Shared, and other for hire vehicle (FHV) companies in New York City, Dr. Sobolevsky prototyped a simulation modeling framework to assess city-scale impacts of transportation innovations and related policies on urban transportation systems, along with associated environmental, economic, and social implications. He discussed his prototype, and the results of his assessment of impact on: travel time reductions and cost savings for passengers, reduction of traffic and congestion, gas consumption and emissions by type (CO, NOx, PM2.5), spatio-temporal disparities of emissions, increased earnings for Lyft and Uber drivers, and the balance of impacts on Lyft/Uber and taxi driver jobs, with a particular focus on the impact on travel affordability for disadvantaged travelers. Dr. Sobolevsky aims for the new framework to be readily applicable to the predictive assessment of the impacts of many other transportation pricing and policy decisions, such as congestion pricing.
Stanislav Sobolevsky is an Associate Professor of Practice and Director Of Urban Complexity Lab at the Center for Urban Science and Progress at New York University, and a Research Affiliate at the MIT Senseable City Lab. He holds a Ph.D. (1999) and Doctor of Science habilitation degree (2009) in Mathematics. Dr. Sobolevsky teaches applied aspects of data science, machine learning, and network analysis. Research of his group studies human behavior in the urban context through its digital traces: spatio-temporal big data created by various aspects of human activity, such as social media, cell phone records, vehicle/vessel GPS traces, public service requests, credit card transactions and others.