optimizing total delay and average queue length based on the fuzzy logic controller in urban intersections

Currently, traffic congestion has become a serious problem in most developed cities. it is caused by an increasing number of vehicles and the delay on arterial roads resulting in negative consequences for air quality, travel time, and travel safety. to reduce the traffic volume and congestion, recent solutions offer optimization of operational characteristics including the total delay and average queue length in urban intersections. optimizing such characteristics is considered as the major breakthrough concept of applying artificial intelligence to transportation engineering. accordingly, the aim of this study was to develop and apply the fuzzy controller to reduce the total delay and average queue length in urban intersections. to this end, effective variables like the total delay and average queue length were simulated using the fuzzy logic controller. then, the results were graphically simulated for the experts. furthermore, the total delay and average queue length were compared employing the fixed-time control and fuzzy controller systems. the results indicated that in the fuzzy controller system rather than the fixed-time control system, the delay and average queue length were remarkably optimized. statistical tests also approved the efficiency of the fuzzy controller as an optimum controller system as compared to the fixed controller system. the findings of this study may help the traffic engineers and urban managers to control the traffic congestion issues based on predicting and optimizing the delay and queue length and increasing the road safety in urban intersections in the future.