A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems
Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/27/5/77 |
| _version_ | 1797471641841696768 |
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| author | Samuel Olugbade Stephen Ojo Agbotiname Lucky Imoize Joseph Isabona Mathew O. Alaba |
| author_facet | Samuel Olugbade Stephen Ojo Agbotiname Lucky Imoize Joseph Isabona Mathew O. Alaba |
| author_sort | Samuel Olugbade |
| collection | DOAJ |
| description | Road transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management. |
| first_indexed | 2024-03-09T19:52:05Z |
| format | Article |
| id | doaj.art-45ffdb88061f47a581d2eaf804b26b2c |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-09T19:52:05Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-45ffdb88061f47a581d2eaf804b26b2c2023-11-24T01:08:44ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472022-09-012757710.3390/mca27050077A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport SystemsSamuel Olugbade0Stephen Ojo1Agbotiname Lucky Imoize2Joseph Isabona3Mathew O. Alaba4Department of Tourism and Hotel Management, Faculty of Social Sciences, Near East University, Nicosia 99138, Northern Cyprus, TurkeyDepartment of Electrical and Computer Engineering, College of Engineering, Anderson University, Anderson, SC 29621, USADepartment of Electrical and Electronics Engineering, Faculty of Engineering, University of Lagos, Lagos 100213, NigeriaDepartment of Physics, Federal University Lokoja, Lokoja 260101, NigeriaDepartment of Biomedical Engineering, University of South Dakota, Sioux, SD 57069, USARoad transport is the most prone to accidents, resulting in significant fatalities and injuries. It also faces a plethora of never-ending problems, such as the frequent loss of lives and valuables during an accident. Appropriate actions need to be taken to address these problems, such as the establishment of an automatic incident detection system using artificial intelligence and machine learning. This article explores the overview of artificial intelligence and machine learning in facilitating automatic incident detector systems to decrease road accidents. The study examines the critical problems and potential remedies for reducing road traffic accidents and the application of artificial intelligence and machine learning in road transportation systems. More, new, and emerging trends that reduce frequent accidents in the transportation sector are discussed extensively. Specifically, the study organized the following sub-topics: an incident detector with machine learning and artificial intelligence and road management with machine learning and artificial intelligence. Additionally, safety is the primary concern of road transport; the internet of vehicles and vehicle ad hoc networks, including the use of wireless communication technologies such as 5G wireless networks and the use of machine learning and artificial intelligence for road transportation systems planning, are elaborated. Key findings from the review indicate that route optimization, cargo volume forecasting, predictive fleet maintenance, real-time vehicle tracking, and traffic management are critical to safeguarding road transportation systems. Finally, the paper summarizes the challenges facing the application of artificial intelligence in road transport systems, highlights the research trends, identifies the unresolved questions, and highlights the essential research takeaways. The work can serve as reference material for road transport system planning and management.https://www.mdpi.com/2297-8747/27/5/77artificial intelligencemachine learningincident detectorroad transport systemstraffic managementautomatic incident detection |
| spellingShingle | Samuel Olugbade Stephen Ojo Agbotiname Lucky Imoize Joseph Isabona Mathew O. Alaba A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems Mathematical and Computational Applications artificial intelligence machine learning incident detector road transport systems traffic management automatic incident detection |
| title | A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems |
| title_full | A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems |
| title_fullStr | A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems |
| title_full_unstemmed | A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems |
| title_short | A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems |
| title_sort | review of artificial intelligence and machine learning for incident detectors in road transport systems |
| topic | artificial intelligence machine learning incident detector road transport systems traffic management automatic incident detection |
| url | https://www.mdpi.com/2297-8747/27/5/77 |
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