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Cited by 31Internet of Things and AIRoad Transport and Highway SystemsTraffic Prediction and Management Techniques

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Machine Learning for Internet of Things-Based Smart Transportation Networks

Khawar, Hammad, Soomro, Tariq Rahim, Kamal, Muhammad Ayoub·2022·Advances in Human and Social Aspects of Technology
DOI 10.4018/978-1-6684-4045-2.ch006
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Abstract

The world's population is expanding, and people want to live in cities, making city administration a difficult task. Traditional cities, with their shared characteristics, will be unable to provide human demands. Machine learning (ML) techniques are being used to increase an application's understanding and capabilities as the volume of data received rises. Smart transportation is defined as an umbrella concept that describes route optimization, parking, street lighting, and infrastructure applications in this evaluation. The purpose of this research is to present a self-contained assessment of machine learning techniques and internet of things applications in intelligent transportation to provide a clear picture of the current state of circumstances. In this chapter, the authors attempt to explain several features of smart transportation in greater depth.

Overview

Integrity signals

Registry

Crossref · 10.4018/978-1-6684-4045-2.ch006

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • AI, ML, and DL are increasingly being used in advanced transportation systems to improve safety, efficiency, and convenience.

    “Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly being used in advanced transportation systems to improve safety, efficiency, and convenience [79] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [79]

Citations

Cites

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Metadata

Authors
Khawar, H., Soomro, T. R., & Kamal, M. A.
Affiliations
  • Baqai Medical University (PK)
  • Institute of Business Management (PK)
ORCID
Venue
Advances in Human and Social Aspects of Technology
Publisher
IGI Global
Published
2022-06-24
Pages
112-134
ISSN
  • 2328-1316 (ISSN-L)
  • 2328-1324
DOI
10.4018/978-1-6684-4045-2.ch006
OpenAlex
W4292622093
Cited by
31
References
5
Citations by year
2026: 3 · 2025: 1 · 2024: 7 · 2023: 6 · 2022: 14
FWCI
17.43
Type
book chapter
Language
en
Open access
closed
Locations
Advances in human and social aspects of technology book series (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
  • Physical Sciences › Engineering › Civil and Structural Engineering › Road Transport and Highway Systems
  • Social Sciences › Transportation › Traffic Prediction and Management Techniques
Keywords
smart transportation, Internet of Things, machine learning, route optimization, parking management, street lighting, urban infrastructure, smart cities

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

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