Evidence map›Paper›PMID 34744331›Full record

ArticleTransport policy2022

The impact of metro-based underground logistics system on city logistics performance under COVID-19 epidemic: A case study of Wuhan, China.

Yuanxian Xu, Jianjun Dong, Rui Ren, Kai Yang, Zhilong Chen

Abstract read
In one paragraph

Article in Transport policy, 2022. 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
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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

The trial behind it

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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.

Yuanxian XuSchool of Defense Engineering, Army Engineering University of PLA, Nanjing, 210007, China.
Jianjun DongSchool of Civil Engineering, Nanjing Tech University, Nanjing, 211816, China.
Rui RenSchool of Defense Engineering, Army Engineering University of PLA, Nanjing, 210007, China.
Kai YangState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China.
Zhilong ChenSchool of Defense Engineering, Army Engineering University of PLA, Nanjing, 210007, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global outbreak of COVID-19 has further exposed deficiencies in city logistics based on human and ground roads, such as poor emergency response capacity and high risk of infection during transportation. Metro-based underground logistics system (M-ULS) may be an innovative approach to deal with this city-level disaster due to its efficient operation, contactless and driverless characteristics. However, the market evolution process and the quantitative calculation framework of comprehensive benefits after the application of M-ULS are still unclear, which has become a problem of mutual restriction with the extensive application of M-ULS. This paper attempts to use the system dynamics method, based on the real-world simulation, to analyze the quantitative relationship between the M-ULS implementation and the city logistics performance under epidemic outbreaks. Wuhan city in China was selected as the empirical background, and five simulation scenarios were set under different implementation strategies of M-ULS in response to the epidemic. Six variables were selected to measure city logistics performance and M-ULS operation status, including demand fill-rate, unit delivery time, total deprivation cost, total transportation cost, total number of susceptible people, and utilization rate of M-ULS. The results show that M-ULS is effective in improving the performance of city logistics and responding to the epidemic. The delivery time and transportation cost have a strong impact on the market share of M-ULS. Finally, a set of incentive policies was designed to promote the adoption of M-ULS. The findings not only provide a method for evaluating the overall performance of M-ULS, but also provide a unique perspective for promoting the implementation of M-ULS and responding to the transportation challenges brought by the epidemic.

Indexed as

Benefit analysisCity logisticsCOVID-19Metro-based underground logistics systemPolicy simulation

Identifiers

PMID34744331
PMCPMC8556189

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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.