Citation

Modi Y, Teli R, Mehta A, Shah K, Shah M. Innov. Infrastruct. Solut. 2021; 7(1): e128.

Copyright

(Copyright © 2021, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s41062-021-00718-3

PMID

unavailable

Abstract

Traffic Clog is the main issue of the fast and evolving world. Due to the rise in the use of more private vehicles and low road network capacity managing traffic with the traditional approach is cumbersome. Pollution and productivity of individuals are highly affected due to traffic. The use of mundane methods may not be an efficient and significant solution for varying traffic congestion. Nowadays, artificial intelligence (AI) and machine learning (ML) are playing an important role in solving many real-world problems. So, to tackle this problem, use of artificial intelligence and machine learning can give optimal solutions. An AI-enabled traffic management system can provide greater leeway to vehicles as they can then be directed and controlled more by the external environment. The main aim of using AI is to decrease manual interfacing. Various algorithms have been designed to curb this problem. The traffic management system consists of tools and technologies to gather information from heterogeneous sources. This study will help in identifying hazards that may potentially degrade traffic efficiency and its overcome technique. This article presents the detailed methodology, review, challenges, and future scope of the use of various algorithms for optimizing different aspects of Traffic Management System, i.e., Smart Traffic Signal Management, Traffic Flow Prediction, Traffic Congestion Detection, and its Management, and Automatic Detection of Traffic Signal.

Language: en

Keywords

CNN; Deep learning; Intelligent traffic management system; KNN