Impact of ride-sharing in New York City
Ride-sharing is now being offered across the globe by UberPOOL, Lyft Shared and similar solutions, which ultimately changes the landscape of urban commutes. However, its holistic impact on the complex urban system, including urban traffic, environment, economy and urban society with respect to the associated mode shift in multi-modal urban transportation, innovation adoption dynamics, etc., is not fully understood.
Using ridership data from ridesharing providers, mobile phone data, as well as a variety of other publicly available data, this project aims to develop a comprehensive holistic model of urban transportation demand given multiple available modes, including for-hire vehicles and their shared options. The model will enable assessment of the impact of shared mobility on urban transportation mode choice, which can be further translated into economic, social and environmental impacts.
This project’s primary objective is to prototype a simulation modeling framework suitable for assessment of city-scale impacts of transportation innovations and policies on urban transportation systems along with the associated environmental, economic and social implications. The assessment will be performed through a pilot use case of ride-sharing offered through UberPOOL, Lyft Shared and other FHV companies in New York City. The impacts in question include: travel time reductions/cost savings for passengers, reduction of traffic and congestion, gas consumption/emissions by type (CO, NOx, PM2.5), spatio-temporal disparities of emissions, increased earnings for Lyft and Uber drivers, balance between impacts on Lyft/Uber and taxi driver jobs. There will be a particular focus on the impact on travel affordability for disadvantaged travelers.
Once developed, the new framework will be readily applicable to the predictive assessment of the impacts of many other transportation pricing and policy decisions, such as congestion pricing.
- Composition and utility analysis of a multi-layered big data platform to support urban transportation demand estimate
- Analytic platform for traffic demand estimation
- Methodology and a nested transportation mode shift model best able to accommodate the data available
- Impact assessment of ride-sharing in NYC offered by UberPOOL and Lyft Shared
- Impact assessment of Manhattan congestion charge use case
|Principal Investigator||Stanislav Sobolevsky, NYU|
|Funding Source||C2SMART Center and cost-share provided by Arcadis|
|Total Project Cost||$90,000|
|USDOT Award #||69A3551747124|
|Start and End Dates||03/01/2019-08/31/2020|
|Implementation of Research Outcomes||The key outcome of the project is the holistic impact assessment of ridesharing and/or other transportation interventions like congestion charge, implemented through the probabilistic simulation modeling framework efficiently leveraging available multi-layered transportation data. Project outcomes will be evaluated in cooperation with actual urban policymakers and stakeholders (DOT, TLC, Uber, Lyft, transportation consulting companies) and recommendations will be given for implementing the framework in urban transportation practice at a broader scale.|
|Impacts/Benefits of Implementation||The project will provide the methodology to inform transportation planners and stakeholders about potential benefits and downsides of implementing digital ridesharing and/or other transportation interventions and policies using available multi-layered transportation data. In addition the framework will support transportation planners with travel demand estimation and a hierarchical structuring of an urban transportation system|