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Cited by 45Traffic control and managementEnergy Efficient Wireless Sensor NetworksTraffic Prediction and Management Techniques

An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization

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Naveed, Quadri Noorulhasan, Alqahtani, Hamed, Khan, Riaz Ullah, Almakdi, Sultan, Alshehri, Mohammed, Abdul Rasheed, Mohammed Aref·2022·Sensors
DOI 10.3390/s22093333PMC PMC9099745
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Abstract

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.

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  • Existing traffic management architectures are not intelligent enough to control signals in real-time according to live street conditions and vehicle movement patterns.

    “Furthermore, existing traffic management architectures are not intelligent enough to control signals in real-time according to live street conditions and vehicle movement patterns [15,16].”

    From Multi-agent Reinforcement Learning for Real-time Traffic Control in 6G-Enabled VANET Environments (2025) · cites this paper as [16]

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Cited by

Sorted by citations

Authors
Naveed, Q. N., Alqahtani, H., Khan, R. U., Almakdi, S., Alshehri, M., & Abdul Rasheed, M. A.
Affiliations
  • King Khalid University (SA)
  • University of Electronic Science and Technology of China (CN)
  • Najran University (SA)
  • Department of MIS, College of Commerce & Business Administration, Dhofar University, Salalah 211, Oman
Corresponding author
  • Naveed, Quadri Noorulhasan
  • Khan, Riaz Ullah
  • Alshehri, Mohammed
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2022-04-27
Volume
22
Issue
9
Pages
3333
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s22093333
PMC
PMC9099745
OpenAlex
W4224981654
Cited by
45
References
0
Citations by year
2026: 7 · 2025: 14 · 2024: 13 · 2023: 8 · 2022: 3
FWCI
3.83
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Deanship of Scientific Research at Najran University (NU/RC/SERC/11/8, RGP. 2/61/43)
  • Deanship of Scientific Research at King Khalid University (NU/RC/SERC/11/8, RGP. 2/61/43)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
  • Physical Sciences › Computer Science › Computer Networks and Communications › Energy Efficient Wireless Sensor Networks
  • Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
Keywords
traffic monitoring, wireless sensor networks, traffic congestion, traffic management, computer vision, visual analytics, smart cities, network energy consumption, network latency
MeSH terms
Accidents, Artificial Intelligence, Cities, Transportation

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