Simulation Testing of a Fuzzy Neural Ramp Metering Algorithm

Fuzzy logic ramp metering algorithms will address the needs of Seattle's freeway system and overcome the limitations of the existing ramp metering algorithm. The design of the fuzzy logic controller (FLC) reduced the sensitivity to sensor data, which frequently contains errors or noise. The rule base effectively balanced the two opposing needs: to alleviate mainline congestion by restricting the metering rate, and to disperse the ramp queue by increasing the metering rate. To avoid oscillation between these two conflicting demands, the controller used inputs that were more descriptive of congestion levels, providing smooth transitions rather than threshold activations.

Testing was performed with the freeway simulation software FRESIM. A multiple-ramp study site from Seattle's I-5 corridor was modeled using data such as freeway geometry entry volumes, desired speeds, and driver behavior. To evaluate the FLC under a variety of conditions, entry volumes and incidents (such as blocked lane or reduced capacity) were varied to create six test data sets. The performance of the FLC was compared to that of other available controllers, including clock, demand/capacity, and speed metering. The objective was to maximize total vehicle miles, maximize mainline speeds, and minimize delay/vehicle-mile while maintaining an acceptable ramp queue. For five of the six data sets, the FLC outperformed the other three controllers. In the FLC, sensors from the on-ramp were helpful in maintaining an acceptable ramp queue. Future work will involve on-line testing of the FLC
Publication Date: 
Sunday, October 1, 1995
Publication Number: 
WA-RD 395.1
Last modified: 
10/12/2016 - 15:41
Cynthia E. Taylor, Deirdre R. Meldrum.
Washington State Transportation Center (TRAC)
Number of Pages: 
Algorithms, Evaluation and assessment, Fuzzy controllers, Fuzzy logic, Neural networks, Queuing, Ramp metering, Testing, Traffic congestion, Traffic simulation.