Ride-sharing is now being offered across the globe by UberPOOL, Lyft Shared, and similar solutions, which ultimately change 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., are 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.
Understanding the holistic impact of planned transportation solutions and interventions on urban systems is challenged by their complexity but critical for decision making. The cornerstone for such impact assessments is estimating the transportation mode-shift resulting from the intervention. And while transportation planning has well-established models for the mode-choice assessment such as a nested multinomial logit model, an individual choice simulation could be better suited for addressing the mode-shift allowing to consistently account for individual preferences. In addition, no model perfectly represents reality while the available ground-truth data on the actual transportation choices needed to infer the model is often incomplete or inconsistent.
The project addresses those challenges by offering an individual mode-choice and mode-shift simulation model and the Bayesian inference framework. It accounts for uncertainties in the data as well as the model estimate and translates them into uncertainties of the resulting mode-shift and the impacts. The framework is evaluated on the two intervention cases: introducing ride-sharing for-hire-vehicles in NYC as well as the recent introduction of the Manhattan Congestion Surcharge. Being successfully evaluated on the cases above, the framework can be used for assessing mode-shift and resulting economic, social, and environmental implications for any future urban transportation solutions and policies being considered by decision-makers or transportation companies.
Associate Professor, NYU
Stanlislav Sobolevsky is the Principal Investigator on this project.
Director, C2SMART Professor, NYU
Kaan Ozbay is a Co-Principal Investigator on this project.
Chinmay Singhal is a Researcher on this project.
Researcher, Urban Complexity lab
Devashish Khulbe is a Researcher on this project.
Mingyi He is a Researcher on this project.
Chaogui Kang is a Researcher on this project.