Disaggregate Control of Vehicles using In-Vehicle Advisories and Peer-to-Peer Negotiations

Traffic advisories to travelers are based upon traffic state information at the link level. This is due to existing infrastructure which sometimes can only provide link-level information. However, the primary justification for providing link-level data is the reluctance of Traffic Management Agencies to consider more detailed traffic state data for operational and safety reasons. However, with the advances in automotive technology, sensing equipment, and the Internet of Things (IoT), we can do better. Research shows that faster and more accurate travel paths can be obtained by using lane data rather than link data. Our contention is that 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 begin with a simple in-vehicle advisory control policy, partially inspired by the Kinetic theory of traffic. We then move towards an individual-level Peer-to-Peer negotiated lane change framework by first investigating its efficacy by means of microsimulation studies. We then 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.
Our proposed framework of routing vehicles on a lane to lane basis can only be realized in the field if the mediating agency (TMC, or a mobility service) has accurate information about traffic conditions. We propose multiple algorithms, including a LSTM (Long Short Term Memory) neural network architecture-based framework to estimate traffic states solely using information collected from sensor-equipped probe vehicles, without the need for any other data such as those obtained from traditional embedded loop detectors.