# A Two-Phase Approach for Predicting Highway Passenger Volume
## 1 Introduction  
Recently, 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]. The primary concern of passenger volume prediction is to extract relevant impact factors and build appropriate models. Firstly, multiple impact factors related to urban attributes, such as gross domestic product (GDP) and population, determine the absolute value and spatial distribution of passenger volume [7, 8]. Consequently, extracting significant impact factors and further analyzing their relationship with passenger volume is recognized as a prerequisite for accurately predicting the passenger volume. Secondly, the prediction models attracted wide attention and the performance of different models was evaluated in past research. Some typical models, including multiple logit models, machine learning models, and deep learning models have been developed based on the historical passenger volume [9, 10]. Nevertheless, the predicted accuracy of the existing models was largely affected by the dataset size of historical passenger volume [11]. Hence, the modelswith historical data cannot perform an accurate prediction if lacking sufficient data, which is quite common for intercity transportation. 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. Thus, 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. As the highway is always an important intercity mode of transport with a high mode share, this study took the highway as the research object. Phase I made a correlation analysis to remove the highly correlated impact factors and developed a random forest (RF) algorithm to extract the significant impact factors of highway passenger volume; Then, Phase II developed a deep feedforward neural network (DFNN) to predict highway passenger volume. To overcome the existing limitations on predicting intercity passenger volume, the primary contributions of this study are as follows:  
1. 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;
2. Multiple urban attributes, including urban economy, population, industry, income and consumption, and resource and environment, were modeled together. Furthermore, A total of 30 significant impact factors of highway passenger volume were extracted by the RF algorithm, which improves the traditional process based on subjective experience and avoids the omission of significant factors;
3. A deep learning method, DFNN, was developed to predict highway passenger volume, which proved to be more accurate than the SVM and multiple regression methods and can provide more reliable information for optimizing traffic structure and reducing waste of traffic resources. The remainder of this study is organized as follows. Section 2 gives as overview of the related literature. In Section 3, the data source is introduced, and the impact factors of urban attributes are collected and presented. Section 4 presents the underlying principle of the RF and DFNN algorithm. Section 5 presents the process of extracting the significant impact factors. In Section 6, the DFNN is developed to predict highway passenger volume, which is further compared with two benchmark methods.