Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities
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 t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/8/2908 |
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| author | Umesh Kumar Lilhore Agbotiname Lucky Imoize Chun-Ta Li Sarita Simaiya Subhendu Kumar Pani Nitin Goyal Arun Kumar Cheng-Chi Lee |
| author_facet | Umesh Kumar Lilhore Agbotiname Lucky Imoize Chun-Ta Li Sarita Simaiya Subhendu Kumar Pani Nitin Goyal Arun Kumar Cheng-Chi Lee |
| author_sort | Umesh Kumar Lilhore |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T13:03:08Z |
| format | Article |
| id | doaj.art-c454ef7b827c4172965341076498c4f5 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T13:03:08Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c454ef7b827c4172965341076498c4f52023-11-30T21:52:35ZengMDPI AGSensors1424-82202022-04-01228290810.3390/s22082908Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart CitiesUmesh Kumar Lilhore0Agbotiname Lucky Imoize1Chun-Ta Li2Sarita Simaiya3Subhendu Kumar Pani4Nitin Goyal5Arun Kumar6Cheng-Chi Lee7KIET Group of Institutions, NCR, Ghaziabad 201206, UP, IndiaDepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Akoka, Lagos 100213, NigeriaDepartment of Information Management, Tainan University of Technology, 529 Zhongzheng Road, Tainan City 710302, TaiwanInstitute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, IndiaKrupajal Engineering College, BPUT, Kausalyapur 751002, Odisha, IndiaComputer Science Engineering Department, Shri Vishwakarma Skill University, Palwal 121102, Haryana, IndiaPanipat Institute of Engineering and Technology, Panipat 132102, Haryana, IndiaResearch and Development Center for Physical Education, Health, and Information Technology, Department of Library and Information Science, Fu Jen Catholic University, New Taipei City 24205, TaiwanThe 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.https://www.mdpi.com/1424-8220/22/8/2908adaptive traffic management systeminternet of thingsmachine learningDBSCAN methodintelligent traffic managementsmart road |
| spellingShingle | Umesh Kumar Lilhore Agbotiname Lucky Imoize Chun-Ta Li Sarita Simaiya Subhendu Kumar Pani Nitin Goyal Arun Kumar Cheng-Chi Lee Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities Sensors adaptive traffic management system internet of things machine learning DBSCAN method intelligent traffic management smart road |
| title | Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities |
| title_full | Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities |
| title_fullStr | Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities |
| title_full_unstemmed | Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities |
| title_short | Design and Implementation of an ML and IoT Based Adaptive Traffic-Management System for Smart Cities |
| title_sort | design and implementation of an ml and iot based adaptive traffic management system for smart cities |
| topic | adaptive traffic management system internet of things machine learning DBSCAN method intelligent traffic management smart road |
| url | https://www.mdpi.com/1424-8220/22/8/2908 |
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