A genetic-fuzzy control application to ramp metering and variable speed limit control

This paper proposes a fuzzy control approach for the traffic-responsive ramp metering and variable speed limits control, in order to reduce the peak-hour congestion on freeways. The objective of control is to minimize the total time spent in the traffic network. To ease the calibration process of fuzzy controller and improve the overall performance of ramp metering and variable speed limits, genetic algorithm is applied for tuning the fuzzy sets parameters. In order to evaluate the controller's efficiency and applicability, a comparison is made with traditional ALINEA based controller and the fuzzy ramp metering only case by a case-study. The macroscopic traffic model METANET and its extension for dynamic speed limits are used for optimization procedure and presenting the simulation results. The paper concludes that genetic fuzzy control of ramp metering and speed limits is expected to enhance the performance of the freeway traffic network.