PhD Forum: Towards Trust-Aware Routing of Ground Vehicle Drivers to Mitigate Travel Time

Optimizing traffic routing, specifically minimizing network travel time, has remained a long-standing concern since the development and adoption of ground vehicles. While several approaches have been proposed to guide drivers of ground vehicles towards traversing network paths that mitigate network congestion, or travel time, these approaches have consistently made simplifying assumptions regarding driver rationality preventing them from being implemented effectively in real-world settings. Modeling routing interactions as Stackelberg games between ground vehicle drivers and an intelligent traffic system offers a promising approach to developing network-wide routing solutions, but the role and impact of trust between network drivers and the system aiming to route traffic has been overlooked in existing literature. The work that I have conducted throughout my Ph.D. studies so far aims to address this gap in the literature by incorporating the human bias of trust into Stackelberg games between drivers and an intelligent traffic system. This is achieved by modeling drivers' trust towards the system, as well as system trust towards each driver, as stochastic variables. Algorithmic solutions that leverage trust to improve route recommendations, estimate evolving driver trust, and dynamically trade control among multiple potential vehicle drivers to mitigate traffic network travel time have been proposed as part of my ongoing Ph.D. research. Each of the proposed models and solutions contributes to a more realistic framework for ground vehicle traffic routing by explicitly accounting for trust from the perspective of drivers and intelligent transportation systems, ultimately enabling more effective, trust-aware routing strategies that can better adapt to real-world driver behavior.