GENETIC FUZZY LOGIC TRAFFIC SIGNAL CONTROL WITH A STEPWISE LEARNING ALGORITHM

Abstract

This paper develops an adaptive traffic signal control model based on a stepwise genetic fuzzy logic controller (SGFLC). The proposed model considers traffic flow and queue length as state variables and extension of green time as control variable, towards the minimization of total vehicle delays. For the learning efficiency of SGFLC and the capability in capturing traffic behaviors, cell transmission model (CTM) is used to replicate traffic behaviors. To investigate the performance of the proposed model at an isolated intersection, comparisons to three pre-timed and two adaptive signal timing models are also conducted. Results show that in both cases, our proposed SGFLC model almost achieved the optimal control. As traffic flows vary more noticeably, the SGFLC model performs even better. In the case of sequential intersections with four coordinated signal systems: simultaneous, progressive, alternate and independent, the results also show that the proposed SGFLC model can also perform best, suggesting that the proposed SGFLC signal control model is effective, robust and applicable.