Abstract
This talk presents inference, control, and game-theoretic algorithms developed to improve traffic flow in transportation networks. The talk will investigate various factors that intervene in decisions made by travelers in large scale urban environments. We will discuss disruptions in demand due to the rapid expansion of the use of “selfish routing”apps, and how they affect urban planning. These disruptions cause congestion andmake traditional approaches of traffic management less effective. Game theoretic approaches to demand modeling will be presented. These models encompass heterogeneous users (some using routing information, some not) that share the same network and compete for the same commodity (capacity). Results will be presented for static loading, based on Nash-Stackelberg games, and in the context of repeated games, to account for the fact that routing algorithms learn the dynamics of the system over time when users change their behavior. The talk will present some potential remedies envisioned by planners, which range from incentivization to regulation.