High accuracy traffic light controller for increasing the given green time utilization

Abstract

Traffic congestion has become one of the major problems in the urban cities according to the increasing number of vehicles in those cities, obsolete technologies used on the roads of those cities, inappropriate road design, and many other reasons. So, that has urged the need for a more accurate traffic light controlling system; one that will help in maintaining high stability at all levels of demand. This paper introduces a dynamic traffic light phase plan protocol for Single-Isolated Intersections. The developed controlling method was compared with four other methods and showed a good performance in terms of reducing the average and maximum queue lengths, optimizing the given green time amount as needed, and increased the intersection's throughput (increased the given green time utilization). In addition, it maintained a good traffic light stability at all levels of demand.

A survey of vehicular communications for traffic signal optimization

“…McKenney and White [38] proposed that vehicle information be shared with its neighbors; however, it does not attempt to coordinate signal timings with neighbors. Most of the surveyed literature offer strategies in isolation from other signals [32][33][34][35]37,36,40]. These algorithms work in isolation, but still manage to get better results than traditional fixed-time signals.…”

An Innovative Framework for Dynamic Traffic Lights Management Based on the Combined Use of Fuzzy Logic and Several Network Architectures

“…In the Matlab/Simulink simulation environment using TrueTime [33], to generate the variable relating to the vehicular traffic existing in a given road (this is a random variable characterized by a probability density that varies over time), a real number generator was used to generate on the interval [0-0.7]. e characteristic of this sequence of numbers depends on the initial seed: when this initial seed varies, a completely different numerical sequence follows in which there is no mathematical relationship between one number and another; instead, if the seed is kept from one simulation to another, the generated sequence will be the same with consequent generation of the same traffic configuration [34]. Since, in the case under examination, the generation of eight tails, there are eight generators of pseudorandom numbers with different seeds (values between 1 and 8).…”

Traffic Signal Optimization under Connected-Vehicle Environment: An Overview

“…In contrast, the upside of individual information is fine-grained considerations of the optimization problem. Examples of aggregative information include total vehicle counts, equipped vehicle ratio [32], and queue length in the lanes related to each traffic lights phase [33,34]. Required individual vehicle information includes real-time location [35], speed [36], heading [37], and acceleration [38] as well as vehicle type [39] of each vehicle within a certain spatial range.…”

“…Some of the studies also considered the variance of waiting time to improve fairness [44]. Several studies conducted queue-based optimization, that is, minimizing average or maximum queue length [34,45]. In order to consider the environmental influence, decreasing pollutant emissions can be imported as an objective function [46].…”