Prof. Andreas Malikopoulos delivered a talk entitled “A Traveler-Centric Mobility Game Under Rationality and Prospect Theory” at the CISE Seminars at Boston University on October 6, 2023.
Emerging mobility systems, e.g., connected and automated vehicles (CAVs), and shared mobility, provide the most intriguing opportunity for enabling users to better monitor transportation network conditions and make better decisions for improving safety and transportation efficiency. It is expected that CAVs will gradually penetrate the market and interact with human-driven vehicles in ways that will improve safety and transportation efficiency over the next several years. However, different levels of vehicle connectivity and automation in the transportation network can significantly alter transportation efficiency metrics ranging from 45% improvement to 60% deterioration. Moreover, we anticipate that efficient transportation and travel cost reduction might alter human travel behavior causing rebound effects, e.g., by improving efficiency, travel cost is decreased; hence willingness-to-travel is increased. The latter would increase overall vehicle miles traveled, which in turn might negate the benefits in terms of energy and travel time. In this talk, I will present a game-theoretic framework that studies the travelers’ decision-making under two behavioral models: (a) rational choice theory, where decision-makers are considered to be selfish and seek to maximize only their own utility; and (b) prospect theory, where the decision-makers’ biases and subjectivity are considered when decisions are made. The proposed framework aims at distributing travel demand in a given transportation network resulting in a socially-efficient mobility system, i.e., one which (1) respects and satisfies the travelers’ preferences, hence travelers would be willing to accept; and (2) ensures alleviation of congestion. The framework embraces a mobility pricing mechanism to control travel demand leading to a pure-strategy Nash equilibrium (NE). I will discuss the inefficiencies at a NE and provide a bound that remains small as the number of travelers increases. Finally, I will present a simple simulation study highlighting the framework’s attributes.
More details can be found here.