Evidence map›Paper›PMID 36415748›Full record

ArticleTransport policy2023

Evaluation and improvement of the resilience of a transportation system against epidemic diseases: A system dynamics approach.

Mohammad-Ali Gorji, Seyyed-Nader Shetab-Boushehri, Meisam Akbarzadeh

Abstract read
In one paragraph

Article in Transport policy, 2023. 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

3 authors.

Mohammad-Ali GorjiDepartment of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Seyyed-Nader Shetab-BoushehriDepartment of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.
Meisam AkbarzadehDepartment of Transportation Engineering, Isfahan University of Technology, Isfahan, 84156-83111, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The influential role of health protocols in preventing the spread of the COVID-19 disease has led governments to seek effective methods for implementing these protocols in the society. Considering the importance of public transportation system in spread of viruses, this paper introduces and analyzes some methods of inspecting urban public transportation companies using system dynamics approach. First, the base model, which represents the status of a public transportation terminal, was created and validated using a system dynamics simulation approach. Then the impact of two penalty policies, including fixed penalty policy (FPP) and variable penalty policy (VPP) on the violations within the terminal was investigated. The simulation results show that the variable penalty policy significantly reduces the violations of passenger terminal drivers. Next, the extended model was developed which considered several terminals. Finally, by presenting two policies of fixed inspector assignment (FIA) and variable inspector assignment (VIA), the effect of four scenarios of combining inspection and penalties policies was investigated. The simulation results showed that combining the variable penalty and variable inspector assignment policies could significantly reduce terminal violations. Also, the implementation of this policy does not require an additional inspector. The results can help city managers to adopt appropriate inspection policies.

Indexed as

EpidemicsInspectionPolicy makingPublic transportationSystem dynamics

Identifiers

PMID36415748
PMCPMC9673069

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.