关键词: 交通工程, 行程时间预测, 支持向量机, 城市快速路

Abstract: With the development of the Urban Expressway Traffic Information Collection System, in particular, the development of video license plate capture system, it is possible to access real-time dynamic expressway travel time, and at the same time, the demand of theoretical research and practical applications of high accuracy travel time prediction is raised. Based on mass of historical time series data detected by license plate recognition system on expressway, four periods of data before forecast period are selected as the input features. In order to achieve dynamic travel time prediction, support vector regression machine is adopted, and model parameters optimization algorithm is created, then trained model is gotten. Finally, three typical sections from Shanghai expressway systems are chosen and the SVM model is used. Compared with the traditional exponential smoothing method, the multiple regression method and the ARIMA method, the mean abs. percent error of SVM model is under 5% and Theil inequality coefficient is very close to 0. It can be seen that the SVM model in this article shows higher prediction accuracy.

Key words: traffic engineering, travel time prediction, SVM, urban expressway