Long-Term Term Ship Speed Prediction for Intelligent Traffic Signaling
Abstract: Yangtze River is probably the world's busiest inland waterway. Ships need to be guided when passing through a controlled waterway based on their long-term long speed prediction. Inaccurate ship speed prediction leads to nonoptimal traffic signaling, which may cause ause a significant traffic jam. For the existing intelligent traffic signaling system, the ship speed is assumed to be constant, which has caused many problems and issues. This paper proposes a novel algorithm to construct an improved multilayer perceptron (MLP) network for accurate longlong term ship speed prediction, in which the hidden neurons of the MLP are optimized by the particle swarm optimization method. The effectiveness and efficiency of the method are guaranteed by using the orthogonal least squares method, which is the fast approach for the construction of the MLP network in a stepwise forward procedure. The model is driven by easily acquired dynamic data of the ships, including the speed and the position. The effectiveness of the proposed method is further confirmed by comparing with several traditional modeling techniques. To the best of our knowledge, this is the first time that a ship speed model is built for long long-term term prediction. The experimental results show that the developed model is in good agreement with the real-life life data, with more than 97% accuracy. It will help to generate the optimal traffic commands for Yangtze River in an intelligent traffic signaling system.