Delay Optimization Using Genetic Algorithm at the Road Intersection

Delay Optimization Using Genetic Algorithm at the Road Intersection

Bharti Sharma (Shivalik College of Engineering Dehradun, India) and Sachin Kumar (College of Information Business Systems, National University of Science and Technology, MISiS, Russian Federation)
Copyright: © 2019 |Pages: 10
DOI: 10.4018/IJIRR.2019040101
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Metropolitan road traffic mechanisms in developing countries are a critical problem due to fast motorization. The optimization of traffic control is one method to decrease this problem. In this study, a genetic algorithm was implemented to minimize delay at an intersection by finding red and green cycle intervals at an intersection. The objective function minimizes the delay at an intersection and increases progressive flows of traffic on roads. The study was done on real data collected from three t- intersections in the city of Hardwar, India. Traffic data for traffic flows, queue sizes, and traffic speed are collected using video detection systems in the study area. The digital images from the camera were analyzed in real time. The results show that the traffic control performance is improved up to 85% over existing algorithms proposed by the same author.
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An intelligent transport system (ITS) (Kumar and Toshniwal, 2015, 2016) has a critical role in designing different type of traffic control algorithms. ITS helps one to develop the self-regulating algorithms for controlling and managing the road traffic to improve the traffic safety, making the flow of traffic smooth and reduction in fuel consumption on roads. In general, ITS is divided into four parts: a surveillance system, a communication system, energy efficiency system and a traffic light control system. Modification in existing traffic control systems infrastructure requires involvement of significant human effort and time. Due to increasing number of vehicles day by day, traffic congestion is a critical problem in many urban cities in India. There are many issues related to our daily life due to traffic congestion like high waiting and traveling time and much fuel consumption. These factors (Kumar et al., 2017) lead to a bad impact on the economy of a country as well. Unregulated and heavy traffic volumes are most important factors of the road accidents (Kumar and Toshniwal, 2017), which are increasing very rapidly (Alba and Garcia, 2011; Cheng and Yun, 2010).

Different methods have been suggested for the implementation of intelligent traffic light control systems, such as, Genetic Algorithm (Hasbullah et al., 2011), Fuzzy Logic Control (Liang et al., 2004; Kulkarni and Wainganka, 2007), Neural Network (Abdul et al., 2008; Binbin et al., 2010) and Queuing Network (Wu and Miao, 2010; Woensel and Vandaele, 2007), etc. Different assumptions have been used to model traffic congestion and flow at the road intersection. The traffic flow control methods are divided into interrupted and uninterrupted flow control methods (Webster, 1958). The interrupted flow is regulated by an external means such as traffic lights or traffic police, while the uninterrupted flow is defined as all flows regulated by vehicle-vehicle interaction and interaction between vehicles traveling on a roadway.

Several suppositions were prepared about the entrance and exit process of the vehicles at controlled intersections. The traffic at intersection considering a Poisson arrival process investigated the performance parameters, such as the average length of the queue and waiting time of the entire vehicle and presented an equation of the average deceleration of a vehicle in closed form, which is based on simulation (Jiannong et al., 2010) .These conventions were accepted and modified by many researchers (Miller, 1968; Tarko et al., 2000). In addition to the Poisson arrival cases, there are other assumptions, such as [D / D / 1], [D / G / 1], [M / G / 1] (Yusuf and Black, 2000; Karim et al., 2005).

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