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.

The shortest-time route recommendations offered by modern navigation systems fuel selfish routing in urban vehicular traffic networks and are therefore one of the main reasons for the growth of congestion. In contrast, intelligent transportation systems (ITS) prefer to steer driver-vehicle systems (DVS) toward system-optimal route recommendations, which are primarily designed to mitigate network congestion. However, due to misalignment in motives, drivers may exhibit a lack of trust in the ITS. This paper models the interaction between a DVS and an ITS as a novel, multi-stage routing game where the DVS exhibits dynamics in its trust towards the recommendations of the ITS based on counterfactual and observed game outcomes. Specifically, the DVS and ITS are modeled as a travel-time minimizer and network congestion minimizer, respectively, each having nonidentical prior beliefs about the network state. A novel approximate algorithm to compute the Bayesian Nash equilibrium, called ROSTER (Recommendation Outcome Sampling with Trust Estimation and Re-evaluation), is proposed based on Monte Carlo sampling with trust belief updating to determine the best response route recommendations of the ITS at each stage of the game. Results of simulations between an ITS and a single additional DVS in a traffic network demonstrate that the developed algorithm is able to both mitigate network congestion and reduce driver travel times more effectively than baseline single-stage route recommendation strategies, while the error in the ITS's prediction of DVS's trust converges to zero as the number of interaction stages increases.

Route recommendation systems provide a promising way to mitigate traffic congestion in vehicular networks by recommending system-optimal paths to drivers that aim to optimize congestion at the network level. However, selfish drivers have aversions to individually sub-optimal paths and exhibit varying levels of trust towards route recommendation systems. In my previous work, we developed a novel, trust-aware Stackelberg routing algorithm that sends path recommendations to all drivers in a vehicular traffic network in an effort to mitigate overall network congestion. We then compared this algorithm to several other Stackelberg routing algorithms to demonstrate the superiority of my proposed algorithm in simulations of real-world networks of various sizes and for diverse traffic demands. In my future work, we will consider a method of modeling and learning the trust of each driver in the network from the perspective of a central routing system through the path choices taken at each routing interaction.

A Stackelberg routing platform (SRP) reduces congestion in one-shot traffic networks by proposing optimal route recommendations to the selfish travelers. Traditionally, Stackel-berg routing is cast as a partial control problem where a fraction of the traveler flow complies with route recommendations, while the remaining responds as selfish travelers. In this paper, we formulate a novel Stackelberg routing framework where the agents exhibit probabilistic compliance by accepting SRP's route recommendations with a trust probability. Specifically, we propose a greedy Trust-Aware Stackelberg Routing algorithm (in short, TASR) for SRP to compute unique path recommendations to each traveler flow with a unique demand. Simulation experiments are designed with random travel demands with diverse trust values on real road networks, such as Sioux Falls, Chicago Sketch, and Sydney networks for both single-commodity and multi-commodity flows. The performance of TASR is compared with state-of-the-art Stackelberg routing methods in terms of traffic congestion and trust dynamics over repeated interaction between the SRP and the travelers. Results show that while it may require several interactions for travelers to reach perfect trust, TASR improves network congestion in the single-commodity and multi-commodity settings when compared to the most well-known Stackelberg routing strategies.

Traditional strategic information design literature assumes receivers trust the signals shared by the sender, the sender and receivers have symmetric information at the outset of the interaction, and receivers update their beliefs according to Bayes rule. In our work, we consider an interaction between a smart navigation system and multiple drivers as a Stackelberg game within a traffic network in which the leader may perturb traffic information shared with selfish receivers to reach a system-optimal routing outcome that minimizes network congestion. We propose a framework that deviates from the traditional assumptions of the strategic information design framework to better mimic real-world human behavior and consider conditions under which a sender shares deceptive information with a receiver.