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.
Highlights
With the continuous process of urbanization, regional integration has become an inevitable trend of future social development in many developing countries [1,2]
In this situation, establishing a convenient and efficient intercity transportation system is a prerequisite for supporting regional integration, in which accurate prediction of passenger volume is one of the most fundamental elements required for the enhancement of intercity transportation systems [3,4,5,6]
A total of 69 impact factors of urban attributes were collected from 280 administrative districts in China, which provides a macroscopic dataset for the prediction of highway passenger volume and overcomes the limitations of traditional travel surveys and questionnaires that only focus on a single city or single transportation corridor; Multiple urban attributes, including urban economy, population, industry, income and consumption, and resource and environment, were modeled together
Summary
With the continuous process of urbanization, regional integration has become an inevitable trend of future social development in many developing countries [1,2]. There are two key steps in the prediction of intercity passenger volume: (1) extracting the significant impact factors, (2) developing a deep learning model to achieve the prediction. It is practical to develop a two-phase approach to predicting intercity passenger volume based on impact factors reflecting urban attributes and deep learning models. A total of 69 impact factors of urban attributes were collected from 280 administrative districts in China, which provides a macroscopic dataset for the prediction of highway passenger volume and overcomes the limitations of traditional travel surveys and questionnaires that only focus on a single city or single transportation corridor; Multiple urban attributes, including urban economy, population, industry, income and consumption, and resource and environment, were modeled together.
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