Abstract
Traffic advisories to travelers are based upon traffic state information at the link level. However, with the advances in automotive technology, sensing equipment, and the Internet of Things (IoT), we can leverage more granular information. Research shows that faster and more accurate travel paths can be obtained by using lane data rather than link data. For vehicles to be able to change lanes to improve their travel times, operationally, they would need to enter into Peer-to-Peer negotiations with surrounding vehicles, where they can trade their position in time and space in exchange for monetary benefits. Our work is an exploration of this idea. We propose an agent-based optimization framework for this system, which minimizes both travel time and the “envy” induced among drivers when they are assigned paths that are inferior to their peers. Numerical results from running our optimization on an illustrative off-ramp network show that the proposed model converges to both envy-free and system optimum traffic states, even at a net zero budget, meaning this system can be used by transportation agencies without exacting tolls or giving subsidies.