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Cited by 123IoT and GPS-based Vehicle Safety SystemsTransportation Systems and LogisticsTraffic and Road Safety

A Review of Artificial Intelligence and Machine Learning for Incident Detectors in Road Transport Systems

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Olugbade, Samuel, Ojo, Stephen, Imoize, Agbotiname Lucky, Isabona, Joseph, Alaba, Mathew O.·2022·Mathematical and Computational Applications
DOI 10.3390/mca27050077
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Abstract

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.

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  • This can result in cost savings and a reduced environmental footprint.

    “This can result in cost savings and a reduced environmental footprint [85] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [85]

    The Introduction says AI can optimize routes to cut emissions, and Section 4 says intelligent transportation systems may improve energy efficiency. These support a possible reduced environmental footprint, but the searched passages do not state that the applications produce cost savings. No page numbers were available in our copy. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency.

    “Route Optimization: ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [85]

    The Abstract and §3.1 identify route optimization as an application of AI and machine learning in road transportation. Section 4 also discusses AI in the movement of goods by road, but the copy does not say that ML optimizes delivery routes for logistics companies or that doing so reduces travel time and improves fuel efficiency. Page numbers were not available in the indexed passages. (LLM, conf=0.84)

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Cited by

Sorted by citations

Authors
Olugbade, S., Ojo, S., Imoize, A. L., Isabona, J., & Alaba, M. O.
Affiliations
  • Near East University (CY)
  • Anderson University - South Carolina (US)
  • University of Lagos (NG)
  • Federal University Lokoja (NG)
  • University of South Dakota (US)
Corresponding author
Imoize, Agbotiname Lucky
ORCID
Venue
Mathematical and Computational Applications
Publisher
MDPI AG
Published
2022-09-13
Volume
27
Issue
5
Pages
77
ISSN
  • 1300-686X (ISSN-L)
  • 2297-8747
DOI
10.3390/mca27050077
OpenAlex
W4296004444
Cited by
123
References
3
Citations by year
2026: 27 · 2025: 42 · 2024: 33 · 2023: 19 · 2022: 2
FWCI
8.90
Type
journal article
Language
en
Open access
Licence
Locations
  • Mathematical and Computational Applications (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Nigerian Petroleum Technology Development Fund (PTDF) (57473408)
  • German Academic Exchange Service (DAAD) through the Nigerian-German Postgraduate Program (57473408)
Topics
  • Physical Sciences › Engineering › Mechanical Engineering › IoT and GPS-based Vehicle Safety Systems
  • Physical Sciences › Engineering › Civil and Structural Engineering › Transportation Systems and Logistics
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
Keywords
automatic incident detection, traffic accidents, road safety, road administration, Internet of Vehicles, vehicular ad hoc networks, 5G, route optimization, cargo volume forecasting, real-time vehicle tracking, traffic management, road transport planning, artificial intelligence in transportation

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