Proactive Early warning on Sailing Risk of Real-time Ship Traffic in Waterway

In order to access sailing risk of real-time ship traffic in waterway and give accident warning in advance, Bayesian network was built by using Gaussian mixture model and maximum expectation algorithm. Based on ship detector data and traffic accident data collected on caoxiexia waterway of Yangtze River, the structure and parameters of Bayesian network were studied and trained, and the segregate was also established. Eight group ship traffic data were adopted to develop traffic accident risk prediction models of BN. Results show that correct rate of accident prediction is 78.13%. At last, a comparative research was done among BN, BP neural network and K nearest neighbor and the results show that the BN prediction model is better than the other two and is a good evaluation method on real-time ship traffic sailing risk.