Intelligent management of crossroads with traffic lights using an hybrid method combining genetic algorithm and fuzzy logic

Abstract

One of the pertinent concerns in traffic management is to optimize the waiting time at the traffic light junctions. We have has already developed an integrated nonlinear model which heavily relies on the genetic algorithm (GA). Indeed, GA proves efficient in terms of the computational time given the environmental constraints and the various variables inherent to the types of users and the degree of priority allotted to each of them. However, it was revealed that some issues having to do with instability require further adjustments.

In the present article the aforementioned model is revisited with the aim of addressing the high standard deviations attributed to the objective function. More specifically, the present work considers the side effects of GA in sweeping the entire space of eligible solutions. In this respect, fuzzy Logic (FL) is integrated as a major component in order to orient the GA research. At the computational level, GA places the solution found by FL at the center of the solution space around which the initial population can be built.

The implementation of this hybrid method reduces both the waiting time at traffic lights and the standard deviation of the results, showing a significant improvement in the management system.