Evidence map›Paper›PMID 35458842›Full record

ArticleSensors (Basel, Switzerland)2022

Evaluation of the Effectiveness of Active and Passive Safety Measures in Preventing Ship-Bridge Collision.

Wenqing Ma, Yini Zhu, Manel Grifoll, Guiyun Liu, Pengjun Zheng

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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
–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

5 authors.

Wenqing MaFaculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China.ORCID 0000-0002-7678-5698
Yini ZhuFaculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China.
Manel GrifollBarcelona Innovative Transportation, Barcelona School of Nautical Studies, Universitat Politècnica de Catalunya (UPC-BarcelonaTech), 08003 Barcelona, Spain.ORCID 0000-0003-4260-6732
Guiyun LiuFaculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China.
Pengjun ZhengFaculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China.

Funding

EC H2020 Project 690713National Key Research and Development Program of China 2017YFE9134700National Natural Science Foundation of China No. 61074142
6 · The paper itself

Abstract

The risk of ship-bridge collisions should be evaluated using advanced models to consider different anti-collision and bridge-protection measures. This study aimed to propose a method to evaluate the effectiveness of active and passive safety measures in preventing ship-bridge collision. A novel ship-bridge collision probability formulation taking into consideration different safety measures was proposed. The model was applied at Jintang Bridge in China where the surrounding vessel traffic is ultra-crowded. We calculated the collision probability between the bridge and passing traffic using automatic identification system (AIS) data, Monte Carlo simulation, and Bayesian networks. Results under four different safety measures (i.e., active measures, passive measures, both measures and none) were analyzed and compared. The analysis concluded that both active and passive safety measures are effective in reducing the ship-bridge collision probability. Active measures, if deployed properly, can provide protection at an equivalent level than passive measures against collision risks. However, passive measures, such as setting arresting cables, are necessary in cases where the response time of the active measures is long. The proposed method and the results obtained from the case study may be useful for robust and systematic effectiveness evaluation of safety measures in other cases worldwide.

Indexed as

Accidents, TrafficShipsBayes TheoremComputer SimulationMonte Carlo MethodProbabilitySafetyAIS dataBayesian networksMonte Carlo simulationsafety measureship–bridge collision prevention

Identifiers

PMID35458842
PMCPMC9025040

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.