Abstract: This talk will discuss how next-generation travel demand models and machine learning algorithms are incorporated for developing smart solutions for smart cities. First, a data-driven approach will be presented to model the dynamics of mode choice behavior. Next, a dynamic discrete-continuous modeling approach will be presented to capture individuals’ tour-based mode choices and continuous time expenditure choices trade-offs in a 24-hour time frame. The empirical model reveals that users of newly introduced mobility services (e.g., Uber, Lyft) tend to have different mode choice patterns, time expenditure choices and value of travel time savings than non-users of these services.

Bio: Md Sami Hasnine is an assistant professor in the Department of Civil and Environmental Engineering at Howard University. Hasnine's research is at the intersection of transportation engineering, econometrics, data science and psychology - how human behavior is connected to transportation decision-making. Hasnine has published 32 peer-reviewed journal articles and 51 peer-reviewed conference papers in the last seven years. The list of his research sponsors includes the National Science Foundation, U.S. Department of Transportation University Transportation Centers Programs, DDOT, the North Carolina Department of Transportation, the Federal Highway Administration , Amazon, DesignSafe and Bezos Earth Fund.