Evidence map›Paper›PMID 36811033›Full record

ArticleTransportation research. Part A, Policy and practice2023

Performance evaluation model of transportation infrastructure: Perspective of COVID-19.

Aijun Liu, Zengxian Li, Wen-Long Shang, Washington Ochieng

Abstract read
In one paragraph

Article in Transportation research. Part A, Policy and practice, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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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

4 authors.

Aijun LiuDepartment of Management Engineering, School of Economics & Management, Xidian University, Xi'an 710071, China.
Zengxian LiDepartment of Management Engineering, School of Economics & Management, Xidian University, Xi'an 710071, China.
Wen-Long ShangBeijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China.
Washington OchiengCentre for Transport Studies, Imperial College London, SW7 2AZ London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transportation systems are facing major challenges due to changes social environment caused by the COVID-19 pandemic. How to construct a suitable evaluation criterion system and suitable assessment method to evaluate the status of the urban transportation resilience has become a predicament nowadays. Firstly, the criteria for evaluating the current state of transportation resilience involve many aspects. New features of transportation resilience under epidemic normalization are exposed, and previous summaries focusing on resilience characteristics under natural disasters can hardly reflect the current state of urban transportation resilience comprehensively. Based on this, this paper attempts to incorporate the new criteria (Dynamicity, Synergy, Policy) into the evaluation system. Secondly, the assessment of urban transportation resilience involves numerous indicators, which make it difficult to obtain quantitative figures for the criteria. With this background, a comprehensive multi-criteria assessment model based on q-rung orthopair 2-tuple linguistic sets is constructed to evaluate the status of transportation infrastructure from perspective on the COVID-19. Then, an example of urban transportation resilience is given to demonstrate the feasibility of the proposed approach. Subsequently, sensitivity analysis about parameters and global robust sensitivity analysis are conducted, and comparative analysis of existing method is given. The results reveal that the proposed method is sensitive to global criteria weights, so it is suggested that more attention should be paid to the rationality of the weight of criteria to avoid the influence on the results when solving MCDM problems. Finally, the policy implications regarding transport infrastructure resilience and appropriate model development are given.

Indexed as

CoCoSo methodEmergencyFuzzy evaluation modelTransportation resilience

Identifiers

PMID36811033
PMCPMC9935300

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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.