A Two-Phase Approach for Predicting Highway Passenger Volume

Abstract

With the continuous process of urbanization, regional integration has become an inevitable trend of future social development. Accurate prediction of passenger volume is an essential prerequisite for understanding the extent of regional integration, which is one of the most fundamental elements for the enhancement of intercity transportation systems. This study proposes a two-phase approach in an effort to predict highway passenger volume. The datasets subsume highway passenger volume and impact factors of urban attributes. In Phase I, correlation analysis is conducted to remove highly correlated impact factors, and a random forest algorithm is employed to extract significant impact factors based on the degree of impact on highway passenger volume. In Phase II, a deep feedforward neural network is developed to predict highway passenger volume, which proved to be more accurate than both the support vector machine and multiple regression methods. The findings can provide useful information for guiding highway planning and optimizing the allocation of transportation resources.