Genetic fuzzy logic traffic signal control with cell transmission modeling

Abstract

This study develops an adaptive traffic signal control model based on an iterative genetic fuzzy logic controller (GFLC). The proposed model considers traffic flow and queue length as state variables and extension of green time as control variable, toward the minimization of total vehicle delays. For the learning efficiency of GFLC and the capability in capturing traffic behaviors, cell transmission model is used to replicate the traffic condition. To investigate, the performance of the proposed model in the case of an isolated intersection, comparisons to pretimed signal timing plans determining by Webster and total enumeration methods, and two queue-length based adaptive models are conducted. Results show that our proposed GFLC model performs best. As traffic flows vary more noticeably, the GFLC traffic signal control model performs even better than any timing plans. In the case of sequential intersections with four coordinated signal systems: simultaneous, progressive, alternate, and independent, the experimental example study also show that the proposed GFLC model can also perform better than current and pretimed timing plans, suggesting that the proposed GFLC signal control model is effective, robust, and adaptable.