With the continuous advancement of socialist construction in China, our society and various transportation modes have also developed rapidly, and a comprehensive transportation system has been formed gradually. The constant expansion of the expressway network has brought difficulties for expressway traffic management and data statistics. The traditional sampling method can hardly adapt to the prediction of expressway transportation volume which has the characteristics of uncertainty, non-stationary and time-correlated; and it has ultimately resulted in inaccurate prediction results of the transportation volume. This situation has gradually improved with the rise of big data analysis technology. Based on Fruit fly Optimization Algorithm (FOA) and grey neural network, this paper proposes a combined model, analyzes the data of expressway network transportation in a province in China, and uses the model to predict the expressway transportation volume combining with actual situations. The research results show that, in the prediction of regional expressway transportation volume, the combined prediction model and algorithm have the advantages of good operability, excellent stability and high accuracy. The research in this paper can provide data support and decision-making basis for the planning and construction of the national expressway network.
Keywords: big data analysis; expressway; total transportation volume; grey prediction; fruit fly optimization algorithm (FOA)