A neuro-fuzzy algorithm for coordinated traffic responsive ramp metering

This paper proposes a nonlinear approach for designing traffic responsive and coordinated ramp control using a self adapting fuzzy system. ANFIS (Adaptive Neuro-Fuzzy Inference System), a special neuro-fuzzy architecture, is used to incorporate a hybrid learning procedure into the control system. The traffic responsive metering rate is determined every minute by the neuro-fuzzy control algorithm. Coordination between multiple on-ramps is ensured by the integration of a common input into all ramp controllers upstream of a bottleneck and a periodical update of the fuzzy control system every 15 min. by a hybrid learning procedure. The objective of the online tuning process of the fuzzy parameters is to minimize the total time spent (TTS) in the system. Therefore Payne's traffic flow model and a deterministic queuing model are integrated into the control architecture. To assess the impacts of the neuro-fuzzy ramp metering algorithm a section of 25 km of the A9 Autobahn was simulated with the FREQ model and compared with two other control scenarios. The results of the simulation of the neuro-fuzzy algorithm are very promising and an implementation of the neuro-fuzzy ramp metering system on Munich's Middle Ring Road within the MOBINET project is planned.