Evidence map›Paper›PMID 35821739›Full record

ArticleEngineering applications of artificial intelligence2022

PITS: An Intelligent Transportation System in pandemic times.

Enrique Brazález, Hermenegilda Macià, Gregorio Díaz, Valentín Valero, Juan Boubeta-Puig

Abstract read
In one paragraph

Article in Engineering applications of artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Enrique BrazálezAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Castilla-La Mancha, Spain.
Hermenegilda MaciàAlbacete Research Institute of Informatics, Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Castilla-La Mancha, Spain.
Gregorio DíazDepartment of Computer Science, Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Castilla-La Mancha, Spain.
Valentín ValeroDepartment of Computer Science, Universidad de Castilla-La Mancha, Campus Univ. s/n, Albacete, 02071, Castilla-La Mancha, Spain.
Juan Boubeta-PuigDepartment of Computer Science and Engineering, University of Cadiz, Avda. de la Universidad de Cádiz 10, Puerto Real, 11519, Cadiz, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The control of the pandemic caused by SARS-CoV-2 is a challenge for governments all around the globe. To manage this situation, countries have adopted a bundle of measures, including restrictions to population mobility. As a consequence, drivers face with the problem of obtaining fast routes to reach their destinations. In this context, some recent works combine Intelligent Transportation Systems (ITS) with big data processing technologies taking the traffic information into account. However, there are no proposals able to gather the COVID-19 health information, assist in the decision-making process, and compute fast routes in an all-in-one solution. In this paper, we propose a Pandemic Intelligent Transportation System (PITS) based on Complex Event Processing (CEP), Fuzzy Logic (FL) and Colored Petri Nets (CPN). CEP is used to process the COVID-19 health indicators and FL to provide recommendations about city areas that should not be crossed. CPNs are then used to create map models of health areas with the mobility restriction information and obtain fast routes for drivers to reach their destinations. The application of PITS to Madrid region (Spain) demonstrates that this system provides support for authorities in the decision-making process about mobility restrictions and obtain fast routes for drivers. PITS is a versatile proposal which can easily be adapted to other scenarios in order to tackle different emergency situations.

Indexed as

Colored Petri netComplex event processingFuzzy logicIntelligent transportation systemPandemic

Identifiers

PMID35821739
PMCPMC9264037

What OpenQuestion holds

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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.