Evidence map›Paper›PMID 40096064›Full record

ArticlePloS one2025

Cooperative longevity of interaction model for addressing paused handoff problem in smart city intelligent transportation systems.

Abdullah Faiz Al Asmari, Tariq Alqubaysi, Fayez Alanazi, Ahmed Almutairi, Ammar Armghan

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdullah Faiz Al AsmariCivil Engineering Department, College of Engineering, King Khalid University, Abha, Saudi Arabia.ORCID https://orcid.org/0000-0001-5949-8155
Tariq AlqubaysiDepartment of Civil Engineering, College of Engineering, Northern Border University, Arar, Saudi Arabia.
Fayez AlanaziCivil Engineering Department, College of Engineering, Jouf University, Sakaka, Saudi Arabia.ORCID https://orcid.org/0000-0003-2975-3711
Ahmed AlmutairiDepartment of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah, Saudi Arabia.
Ammar ArmghanDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakaka, Saudi Arabia.ORCID https://orcid.org/0000-0002-9062-7493

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Smart cities use Intelligent Transportation Systems (ITS) to manage traffic by continuously communicating with roadside infrastructure and nearby vehicles. Paused handoff interrupts grounded congestion, signal supervision, and path-shifting knowledge. Paused handoffs occur when cars wait to interact owing to volatile neighbours or heavily crowded roadside units. In congested metropolitan areas, ITS vehicle communication interruptions are a significant issue. This research addresses this issue. Hence, the research introduces the Cooperative Longevity of Interaction Model (CLoIM) to enhance communication reliability by minimizing the impact of paused handoff. The model employs a hybrid trained herd optimization algorithm to improve the longevity for interaction between vehicles and roadside units, minimizing handoff interruptions. The approach dynamically adjusts search strategies to prioritize high longevity interactions, improving communication stability. Results show that CLoIM increases longevity by 10.81% and reduces the paused handoff lag by 9.17%, effectively addressing the challenges of vehicle density and mobility in ITS scenarios.

Indexed as

Artificial IntelligenceModels, TheoreticalTransportationAlgorithmsCitiesHumans

Identifiers

PMID40096064
PMCPMC11913310

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.