Investigation on Multi-objective Freeway Control Strategy using Macroscopic and Microscopic Traffic Flow Data

Freeway congestion has become a serious problem for city manager due to the continuous growth of traffic demand. Different kinds of traffic control management measures, such as variable speed limits and ramp metering, have been proposed and partly implemented to handle this problem. Mobility, safety and emission are used as the control objectives. This paper investigates multi-objective optimization with the coordinated control of variable speed limits and ramp metering. Moreover, model predictive control is used as the control framework in a rolling horizon system. Furthermore, multi-objective particle swarm optimal algorithm is adopted to acquire the best control signal for mobility, safety and emission performance. The proposed method is evaluated through simulation and calibrated using the real-world data at a freeway stretch. The experiment result indicates that the mobility of the study network was improved, and the collision risk and carbon emission are reduced by the integration of variable speed limits and ramp metering.