NSF Award
2200590
This Excellence in Research (EiR) project will investigate the impacts of real-time information on the sequential choice-making behavior of travelers using transportation infrastructure and services in smart cities. Smart cities of the future are envisioned as a place where digitalization of day-to-day services, interconnectedness of sensing technologies, and intelligent algorithms for city management lead to innovative socioeconomic and socio-technical growth. Travelers using transportation services in these “cities of the future” make various choices in a dynamically evolving system such as the choice of destination, departure time, mode of travel, travel route, and/or parking location. This project studies the interrelation of choices and the impact of real-time information on choices by adopting a data-driven modeling approach. The tools and models developed as part of this project will enable data-driven choice modeling and management of future transportation systems and will contribute toward NSF’s mission of promoting the progress of science and advancing the nation’s prosperity and welfare. Beyond its intellectual merit, this project will (a) enable underrepresented students at Historically Black Colleges and Universities to participate in research on future transportation systems, (b) develop educational tools for accurate choice modeling of travelers in response to real-time information, and (c) develop incentives that guide the design and development of smart cities of the future. Eventually, the project will enable agencies to perform better short-term and long-term transportation planning, which will have a substantial societal impact.<br/><br/>As its primary goal, the project will explain travelers’ choices under the presence of real-time information using an advanced econometric framework that identifies the factors affecting these choices, including time-varying characteristics of emerging technologies, level-of-service, and socio-demographic variables. This project will contribute to the choice modeling theory and experiments by (1) explicitly incorporating real-time information in sequential dynamic choices of travelers, (2) modeling the impact of the error-prone or aggregated information on travel choices through D-efficient experimental design, and (3) developing a policy-sensitive tool that will be able to evaluate a wide range of smart-city-related transportation policies. This project will adopt innovative techniques such as creating a simulation-based virtual environment for experiments on choices made by travelers in a dynamically evolving system, and developing and calibrating robust mathematical models useful for accurate forecasting of travel behavior. The focus applications in this project will also provide guidance on optimization and operations of civil infrastructure systems and will enable methodological evaluation of transportation policies in Smart Cities that may impact travel behavior over the long term.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
