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Chengpeng Wan

Researcher at Wuhan University of Technology

Publications -  25
Citations -  714

Chengpeng Wan is an academic researcher from Wuhan University of Technology. The author has contributed to research in topics: Computer science & Risk analysis. The author has an hindex of 8, co-authored 14 publications receiving 354 citations. Previous affiliations of Chengpeng Wan include Liverpool John Moores University.

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Resilience in transportation systems: a systematic review and future directions

TL;DR: The Belt and Road (B&R) initiative was introduced by the Chinese government to promote the worldwide economic development and multilateral cooperation between China and the associated countries as mentioned in this paper. But it has not yet been implemented in practice.
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An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks

TL;DR: In this paper, a fuzzy belief rule approach with Bayesian networks is proposed to assess the risk factors of maritime supply chains by incorporating fuzzy belief rules into a Bayesian network, which has the capability of improving result accuracy under a high uncertainty in risk data.
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A framework to identify factors influencing navigational risk for Maritime Autonomous Surface Ships

TL;DR: A framework for the identification of factors that influence the navigational risk of remotely controlled MASS without crews on board is proposed and may assist the processes of design and operational planning of maritime transportation systems accommodating MASS and its remote-control center, e.g., the shore control center.
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A novel model for the quantitative evaluation of green port development - A case study of major ports in China

TL;DR: An evaluation model for quantitative measurement of green port development is established based on the Drivers, Pressures, States, Impacts and Responses (DPSIR) framework, and the evaluation results of the investigated ports with respect to each index are aggregated using an evidential reasoning approach as discussed by the authors.
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AIS data-driven approach to estimate navigable capacity of busy waterways focusing on ships entering and leaving port

TL;DR: This paper proposes a novel data-driven approach to estimate the navigable capacity of busy waterways, focusing on ships entering and leaving port, based on the structural characteristics of traffic flow driven by the Automatic Identification System (AIS).