Public Bicycle Traffic Flow Prediction based on a Hybrid Model

In China, Hangzhou is the first city to set up the Public Bicycle System. Now, the system has been the largest bike sharing program in the world. The software of Hangzhou Public Bicycle System was developed by our team. Accurate and precise prediction of public bicycle traffic flow is important in traffic planning, design, operation s, etc. According to the highly complexity, nonlinearity and uncertainty of traffic flow, a single prediction model is difficult to ensure th e prediction accuracy and efficiency. To overcome the lack of the single prediction method, this paper uses a hybrid model that combining clustering with support vector machine, by exploiting complementary advantages of both approaches. Firstly, this method uses improved k-means algorithm to cluster the original sample set. Secondly, the subset whose character is the most similar to the sample set to be forecasted is chosen. Finally, a polynomial smooth support vector machine uses the subset to forecast the public bicycle tr affic flow. The experimental results show that the hybrid model performs higher forecasting accuracy and better generalization ability.