Evidence map›Paper›PMID 36212805›Full record

ArticleOcean & coastal management2022

Quantitative analysis of the impact of COVID-19 on ship visiting behaviors to ports- A framework and a case study.

Xinyu Wang, Zhao Liu, Ran Yan, Helong Wang, Mingyang Zhang

Open access · hybridAbstract read
In one paragraph

Article in Ocean & coastal management, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
6.6field-weighted citation impact, top 3% of its field
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

4 citing papers in PubMed, 53 citations in OpenAlex.

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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 at 4 institutions in 3 countries.

Xinyu WangHubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Hubei, China.
Zhao LiuHubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Hubei, China.
Ran YanDepartment of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hong Kong, China.
Helong WangNAPA Ltd., Helsinki, Finland.
Mingyang ZhangDepartment of Mechanical Engineering, Marine Technology Group, Aalto University, Espoo, Finland.
Wuhan University of Technology · CNAalto University · FIHong Kong Polytechnic University · HKNapa (Finland) · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Corona Virus Disease 2019 (COVID-19) outbreak leads to a significant downturn in the global economy and supply chain. In the maritime sector, trade volume slumped by 3.8% in 2020 compared with 2019. To explore the impacts of COVID-19 on ship visiting behaviors, a framework is proposed to analyze the impact of COVID-19 on port traffic using Automatic Identification System (AIS) data. Firstly, a ship travel behavior-based model is proposed to identify the vessel anchoring and berthing. Then, the diversity in vessel anchoring and berthing time are analyzed, reflecting the impact of COVID-19. The port congestion caused by COVID-19 is quantified by accounting for the number of visiting ships and their residence time. Finally, a case study is carried out on vessels in the Beibu Gulf, China, operating from 2019 to 2020. The results show that the average anchoring time and berthing time increase by 62% and 11% for cargo ships and by 112% and 63% for oil tankers after the outbreak of COVID-19 compared with that before COVID-19. And the density of ships increases in the port area in 2020. Accordingly, the relevant improvements and countermeasures are proposed to reduce the adverse impact of the epidemic on the port navigation system. The paper has the potential to provide a reference for port management and improving port navigation efficiency in the post-pandemic era.

Indexed as

AIS dataCOVID-19Maritime transportationPattern recognitionPort managementStatistical analysis

Identifiers

PMID36212805
PMCPMC9527221
OpenAlexW4300963046

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.