Model Predictive Control for Motorway Traffic with Mixed Manual and VACS-equipped Vehicles

Abstract

Vehicle automation and communication systems (VACS) are expected to appear in an increasing amount of vehicles within the next years. Among the wide range of proposed VACS, the ones that include vehicle-to-infrastructure communication capabilities may be exploited both as sensors and as actuators. This enables traffic control centres to obtain more accurate information on the current traffic state and to assign to each vehicle appropriate control tasks, so as to achieve a global traffic flow target. The concept employs and exploits the synergistic (integrated) action of a number of old and new control measures, including ramp metering, vehicle speed control, and lane changing control, at a macroscopic level. The problem is tackled through a Quadratic Programming optimisation problem used as the core of a model predictive control framework. The optimal control actions may be sent directly to vehicles equipped with adaptive cruise control (ACC), affecting directly their cruise speed and, in addition, their lane-changing behaviour. The effectiveness and the computational feasibility of the proposed approach are demonstrated via microscopic simulation for a variety of ACC settings and penetration rates of equipped vehicles.