The state-of-the-art in the application of artificial intelligence-based models for traffic noise prediction: a bibliographic overview

Abstract

This paper reviews the application of artificial intelligence (AI)-based models in modeling vehicular road traffic noise. A computerized search method was used to conduct the literature search. Fifty published articles from 2007 to 2023 were reviewed regarding observation time, input data, countries where studies were performed, and modeling techniques. Sixty-three percent of the studies used an observation period of 60 min, and 29% used 15 min. All the reviewed papers considered traffic flow as the major input parameter, followed by average speed, with 95% of the researchers using it as an input parameter. It was found that using AI-based models for traffic noise prediction was popular in countries with no established empirical models. The primary input parameters for the AI-based models are traffic volume and speed. Traffic volume is used either as total traffic volume or classified into sub-categories, and each category is used as an independent input parameter. Although AI-based models have demonstrated reliable performance regarding prediction error and goodness of fit, the accuracy of the AI-based models’ performance should be compared with the results of the empirical models in countries with established models, such as the UK (CoRTN) and the USA (FHWA).

Keywords:

• Artificial intelligence
• noise
• speed
• vehicular traffic
• prediction

Reviewing Editor:

• Sanjay Kumar Shukla
• Edith Cowan University
• Australia

Subjects:

• Civil, Environmental and Geotechnical Engineering
• Transportation Engineering; Environmental Health
• Pavement Engineering

Acknowledgements

The authors wish to acknowledge the support of Prince Sultan University, Riyadh Saudi Arabia. This study was also supported by Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444).

Author contributions

All authors equally contributed to the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

This study receives no funding.

Data availability statement

No data has been generated during the study.

Additional information

Notes on contributors

Ibrahim Khalil Umar

Ibrahim Khalil Umar obtained his PhD from Near East University Cyprus in 2022. He is currently a Lecturer at the Kano State Polytechnic, Nigeria. His area of research interest includes Machine learning, noise pollution, road safety, traffic induced pollutions.

Musa Adamu

Musa Adamu obtained his PhD from Universiti Teknologi Petronas Malaysia. He is currently a Researcher at Prince Sultan University, Saudi Arabia. His area of research interest includes Artificial intelligence and machine learning, Natural Fiber reinforced concrete, Sustainable Concrete utilizing Supplementary cementitious materials, Rubberized concrete, Modelling and Optimization of concrete’s properties using response surface methodology.

Nour Mostafa

Nour Mostafa received a PhD degree from the Queen’s University Belfast School of Electronics, Electrical Engineering and Computer Science, UK, in 2013. He previously worked as a software developer with Liberty Information Technology, USA/UK, and is currently an associate professor of computer science in the College of Engineering and Technology, American University of the Middle East. His current research interests include grid computing, large database management, artificial intelligence, machine learning, distributed computing, cloud, fog, and IoT computing. He has authored and co-authored many refereed journal articles, conference papers and book chapters. He is an active reviewer for many reputed international and IEEE journals and letters. Also, he has been selected as an International Steering Committee Member of many conferences, and he has joined the editorial board of international journals.

Sadi I. Haruna

Sadi I. Haruna obtained his PhD in Civil Engineering from Tianjin University in 2023. He is currently a lecturer at Bayero University Kano, and a Researcher at Prince Sultan University Saudi Arabia. His area of research interest includes dynamic analysis, Artificial intelligent, Sustainability of Construction Materials; Polymer Materials, Reuse, Durability of Concrete Materials, Materials Characterization Mineral and chemical admixtures for concrete, Structural Rehabilitation with Composite Systems and PU materials.

Mukhtar Fatihu Hamza

Mukhtar Fatihu Hamza obtained his PhD (Control, Automation and Robotics) from University of Malaya 2017. He is currently an assistant professor in the department of mechanical engineering, Prince Sattam bin Abdulaziz University, Al Kharj Saudi Arabia. His area of research interest includes Artificial general intelligence and intelligent controllers, Lower limb exoskeleton robot stability for power argument, Cloud-based CNC machine tools; and cloud-based status monitoring of CNC machine tool.

Omar Shabbir Ahmed

Omar Shabbir Ahmed is a master’s student at King Saud University in Saudi Arabia. He joined Prince Sultan University (PSU) in 2020 as a lab engineer in the Engineering Management department. He is a member of the American Society of Civil Engineers (ASCE) and the Structure and Materials Research Lab. Omar has participated in numerous research projects and published several papers in the areas of construction management, concrete design, and materials structure.

Marc Azab

Marc Azab is currently an Assistant Professor with the Department of Civil Engineering, American University of the Middle East, Kuwait. He received the Ph.D. degree in materials and fracture mechanics from Université Grenoble Alpes, in 2016. His extensive experience in teaching and research, combined with his passion for innovative solutions to complex problems, has earned him a well-deserved reputation as a respected authority in civil engineering and related disciplines. He has published more than 30 articles in prestigious journals, contributing significantly to the advancement of knowledge in his field. His research interests include fracture mechanics, construction materials, and sustainability.