Design and Implementation of an ML and IoT based Adaptive Traffic Management System for Smart Cities

ASIAIR > College of Computer Science > Department of Photonics and Communication Engineering > Journal Article > Item 310904400/115034

Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/115034

Title:	Design and Implementation of an ML and IoT based Adaptive Traffic Management System for Smart Cities
Authors:	Kumar, Umesh;Lilhore, Umesh Kumar;李正吉;Lee, Cheng-Chi
Contributors:	資訊電機學院光電與通訊學系
Keywords:	adaptive traffic management system;internet of things;machine learning;DBSCAN method;intelligent traffic management;smart road;intelligent transport system
Date:	2022-04-01
Issue Date:	2023-03-28 02:02:27 (UTC+0)
Publisher:	亞洲大學
Abstract:	The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.
Appears in Collections:	[光電與通訊學系] 期刊論文

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