Simulation of signalized intersection functioning with fuzzy control algorithm

In the course of research the fuzzy algorithm for traffic control at signalized intersection has been developed. Based on the results of simulating of intersection functioning during an hour and a day it has been established that using of developed fuzzy algorithm enables to reduce average and maximal queue lengths of vehicles before the intersection owing to adaptation of control system parameters to traffic flow volumes

Neurofuzzy control to address stochastic variation in actuated-coordinated systems at closely-spaced intersections

This dissertation documents a method of addressing stochastic variation at closely-spaced signalized intersections using neurofuzzy control. Developed on the conventional actuated-coordinated control system, the neurofuzzy traffic signal control keeps the advantage of the conventional control system. Beyond this, the neurofuzzy signal control coordinates the coordinated phase with one of the non-coordinated phases with no reduction of the green band assigned to the coordination along the arterial, reduces variations of traffic signal times in the cycle caused by early return to green , hence, makes more sufficient utilization of green time at closely-spaced intersections. The neurofuzzy signal control system manages a non-coordinated movement in order to manage queue spillbacks and variations of signal timings.Specifically, the neurofuzzy controller establishes a secondary coordination between the upstream coordinated phase (through phase) and the downstream non-coordinated phase (left turn phase) based on real-time traffic demand. Under the fuzzy logic signal control, the traffic from the upstream intersection can arrive and join the queue at the downstream left turn lane and be served, and hence, less possibly be blocked on the downstream left turn lane. This secondary coordination favors left turn progression and, hence, reduces the queue spillbacks. The fuzzy logic method overcomes the natural disadvantage of currently widely used actuated-coordinated traffic signal control in that the fuzzy logic method could coordinate a coordinated movement with a non-coordinated movement. The experiment was conducted and evaluated using a simulation model created using the microscopic simulation program - VISSIM.The neurofuzzy control algorithm was coded with MATLAB which interacts with the traffic simulation model via VISSIM\u27s COM interface. The membership functions in the neurofuzzy signal control system were calibrated using reinforcement learning to further the performance. Comparisons were made between the trained neurofuzzy control, the untrained neurofuzzy control, and the conventional actuated-coordinated control under five different traffic volumes. The simulation results indicated that the trained neurofuzzy signal control outperformed the other two for each traffic case. Comparing to the conventional actuated-coordinated control, the trained neurofuzzy signal control reduced the average delay by 7% and the average number of stops by 6% under the original traffic volume; as traffic volume increasing to 120%, the reductions doubled

Dynamic Left-turn Phase Optimization Using Fuzzy Logic Control

The left-turn movement at an intersection has long been a concern of traffic engineers as it is a major capacity reduction factor. Different left-turn signal phasings have been shown to result in significant differences in delay, intersection capacity, and even safety level. First, past studies about leading and lagging signal phases and signal control application are overviewed. Then this research gives a theoretical analysis of signal left-turn phase operations at both isolated and coordinated signalized intersections, compares the difference in delay based on leading and lagging left-turn signal phase designs, analyzes the influences of traffic control delay components for leading and lagging left-turn, identifies the main control factors, and gives a new model to guide the choosing between the leading and lagging left-turn phases. In the third part of this research, some basic mathematical definitions and rules of fuzzy logic control are described. A four-level fuzzy logic control model is designed. To implement this control model, observed approaching traffic flows are used to estimate relative traffic intensities in the competing approaches. These traffic intensities are then used to determine whether a leading or lagging signal phase should be selected or terminated. Finally, this research designs a dynamic traffic signal left-turn phase control system, and implements the four-level fuzzy logic control model to optimize signalized intersection operation. The performance of this dynamic traffic signal left-turn phase fuzzy logic control system compared favorably in all categories to fixed time control, actuated control, and traditional fuzzy control based on simulation using field data. The results suggest that the proposed dynamic traffic signal left-turn phase fuzzy logic control system is a superior and efficient tool for reducing intersection traffic delay. The study also demonstrated that the successful implementation of the proposed model does not rely on the installation of expensive or complicated equipment

A multi-agent Framework for dynamic traffic management Considering Priority Link

To favor emergency vehicles, promote collective modes of transport in Moroccan cities, we propose in this paper a control system to manage traffic at signalized intersections with priority links in urban settings. This system combines multi-agent technology and fuzzy logic to regulate traffic flows. The traffic system flow is divided into two types of vehicles; priority and regular vehicles. The regular vehicles can use only the regular links, while the priority vehicles may use both priority and the regular links. This approach aims to favor emergency vehicles and promote collective modes of transport, it acts on the traffic light phases length and order to control all traffic flows. We proposed a decentralized system of regulation based on real-time monitoring to develop a local inter-section state, and intelligent coordination between neighboring intersections to build an overview of the traffic state. The regulation and prioritization decisions are made through cooperation, communication, and coordination between different agents. The performance of the proposed system is investigated and instantiated in ANYLOGIC simulator, using a section of the Marrakesh road network that contains priority links. The results indicate that the designed system can significantly develop the efficiency of the traffic regulation system