Important Variables Identification and Proactive Evaluation of Real-time Ship Traffic Sailing Risk in Waterway

DOI

10.2991/icoeme-19.2019.71How to use a DOI?

Keywords

waterway; sailing risk; proactive evaluation; random forest; Bayesian network

Abstract

In order to further improve accident prediction accuracy of real-time ship traffic in waterway, based on ship detector data and traffic accident data collected on two downstream waterways of Yangtze River, important variables were sifted with random forest (RF) model from the initial data of waterway status within 20-40min before the traffic accident, then new Bayesian network(BN)model was established with 4 most important variables combined with Gaussian mixture model (GMM) and maximum expectation (EM) algorithm. Compared with BN model previous studied built with direct initial data, the new models complexity is not only reduced and its prediction effect is improved, with the accident prediction correct rate of 81.29%.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Moyang Zhao
AU  - Shukui Zhang
PY  - 2019/06
DA  - 2019/06
TI  - Important Variables Identification and Proactive Evaluation of Real-time Ship Traffic Sailing Risk in Waterway
BT  - Proceedings of the 2nd International Conference on Economy, Management and Entrepreneurship (ICOEME 2019)
PB  - Atlantis Press
SP  - 379
EP  - 382
SN  - 2352-5428
UR  - https://doi.org/10.2991/icoeme-19.2019.71
DO  - 10.2991/icoeme-19.2019.71
ID  - Zhao2019/06
ER  -