{"title":"ADAPTIVE TRAFFIC SYSTEM CONTROLLERS IN TRAFFIC ENGINEERING : A SURVEY","authors":"Amarpreet Singh, Sandeep Singh, Alok Aggarwal","doi":"10.55766/sujst-2023-03-e03030","DOIUrl":null,"url":null,"abstract":"In today’s era, traffic congestion is the widest spread problem observed all over the world, arising as consequence of exponential rise in vehicle count at the traffic intersections. This growth has largely affected the people as they are experiencing enhanced delay in travelling time and increased fuel consumption which led to wastage of billions of dollars. The current road infrastructure design and traffic signal controlling using a cycle of fixed time phase of green/red/yellow lights are not adequate to tackle the rising demands of traffic in an optimum way. These traditional traffic signal systems cannot handle the dynamics of road traffic at the intersections and hence results in exceeding delays. Also, the volume of traffic at any intersection at different times of the day is uncertain and hence it is hard to get an exact mathematical model for this problem. Many researchers have proposed some solution to this problem and their work is reviewed extensively in this paper. Due to its ability to deal with uncertainty, fuzzy logic is considered as the most appropriate technique to solve this problem and is highly recommended method for implementing automated traffic controllers. Due to its inherent advantages, most of the research in the field of traffic engineering is carried out using fuzzy logic techniques. Hence, this paper presents a systematic review of various techniques that are used for an effective management of traffic, especially focusing on different fuzzy based traffic controllers and their performance comparison to identify the best input output parameter.","PeriodicalId":43478,"journal":{"name":"Suranaree Journal of Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.2000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Suranaree Journal of Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55766/sujst-2023-03-e03030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
In today’s era, traffic congestion is the widest spread problem observed all over the world, arising as consequence of exponential rise in vehicle count at the traffic intersections. This growth has largely affected the people as they are experiencing enhanced delay in travelling time and increased fuel consumption which led to wastage of billions of dollars. The current road infrastructure design and traffic signal controlling using a cycle of fixed time phase of green/red/yellow lights are not adequate to tackle the rising demands of traffic in an optimum way. These traditional traffic signal systems cannot handle the dynamics of road traffic at the intersections and hence results in exceeding delays. Also, the volume of traffic at any intersection at different times of the day is uncertain and hence it is hard to get an exact mathematical model for this problem. Many researchers have proposed some solution to this problem and their work is reviewed extensively in this paper. Due to its ability to deal with uncertainty, fuzzy logic is considered as the most appropriate technique to solve this problem and is highly recommended method for implementing automated traffic controllers. Due to its inherent advantages, most of the research in the field of traffic engineering is carried out using fuzzy logic techniques. Hence, this paper presents a systematic review of various techniques that are used for an effective management of traffic, especially focusing on different fuzzy based traffic controllers and their performance comparison to identify the best input output parameter.