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

Umesh Kumar Lilhore; Agbotiname Lucky Imoize; Chun-Ta Li; Sarita Simaiya; Subhendu Kumar Pani; Nitin Goyal; Arun Kumar; Cheng-Chi Lee

doi:10.3390/s22082908

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

Sensors (Basel). 2022 Apr 10;22(8):2908. doi: 10.3390/s22082908.

Authors

Umesh Kumar Lilhore¹, Agbotiname Lucky Imoize^{2

3}, Chun-Ta Li⁴, Sarita Simaiya⁵, Subhendu Kumar Pani⁶, Nitin Goyal⁷, Arun Kumar⁸, Cheng-Chi Lee^{9

10}

Affiliations

¹ KIET Group of Institutions, NCR, Ghaziabad 201206, UP, India.
² Department of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, Nigeria.
³ Department of Electrical Engineering and Information Technology, Institute of Digital Communication, Ruhr University, 44801 Bochum, Germany.
⁴ Department of Information Management, Tainan University of Technology, 529 Zhongzheng Road, Tainan City 710302, Taiwan.
⁵ Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India.
⁶ Krupajal Engineering College, BPUT, Kausalyapur 751002, Odisha, India.
⁷ Computer Science Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, Haryana, India.
⁸ Panipat Institute of Engineering and Technology, Panipat 132102, Haryana, India.
⁹ Research and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24205, Taiwan.
¹⁰ Department of Computer Science and Information Engineering, Asia University, Taichung City 41354, Taiwan.

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.

Keywords: DBSCAN method; adaptive traffic management system; intelligent traffic management; intelligent transport system; internet of things; machine learning; smart road.

MeSH terms

Accidents
Automobiles*
Cities
Machine Learning*
Transportation