Optimal Lane Management in Heterogeneous Traffic Network Using Extremum Seeking Approach

This paper is focused on modeling and control of a heterogeneous traffic network consisting of human-driven and autonomous vehicles. In this paper, we consider the autonomous vehicles as controllable agents while the human-driven vehicles are considered as rational but non-controllable agents. The fundamental traffic diagram for such heterogeneous traffic networks is developed wherein the capacity and jam density of the road is determined as a function of the penetration rate and the headways of autonomous and human-driven vehicles. A cost function is defined to maximize the average flow-rate within the network. Considering the rationality of the human-driven vehicles as well as the controllability of the autonomous vehicles, a series of constraints are imposed on the cost function. We employed an extremum seeking control approach to determine the optimal flow-rate between the sub-networks so that the mobility of the network improves. Numerical simulation demonstrates the effectiveness of the proposed approach in managing the traffic flow of a heterogeneous system.