Abstract:
Currently, traffic congestion has become a serious problem in most developed cities. It is caused by an increasing number of the vehicles and the delay on arterial roads resulting in negative consequences regarding 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 are considered as the major breakthrough concepts of applying artificial intelligence in 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 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.
Machine summary:
com Optimizing Total Delay and Average Queue Length Based on the Fuzzy Logic Controller in Urban Intersections Hamid Shirmohammadi a and Farhad Hadadi a a Faculty of Civil Engineering, Urmia University, Urmia, Iran Abstract Currently, traffic congestion has become a serious problem in most developed cities.
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.
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 .
Input and output variables such as speed, volume, total stop, total delay, and average queue lengths were first collected by Synchro 8 software which was set based on the fixed-time controller.
Another aim of this study was to classify the intersections according to the optimal amounts of the total delay and average queue lengths in comparison with fixed-time controllers.
Therefore, fuzzy logic was selected as an accurate and intelligent controller system to optimize and classify the optimum total delay and queue lengths in urban intersections.
1. Input variables Since the main aim of this study was to evaluate and optimize the delay and average queue length of the vehicles in intersections, input variables like speed, total stop, and volume of the vehicles were valued and classified into the intervals based on the principles of fuzzy logic as shown in Figure 3.