# A Two-Phase Approach for Predicting Highway Passenger Volume
## 2 Literature Review  
This section concludes the existing research on the above two phases: (1) extracting the significant impact factors of intercity passenger volume, (2) developing models to achieve an accurate prediction. Furthermore, the limitations of existing research are itemized at the end. The first phase is to extract the significant impact factors. Multiple impact factors related to urban attributes, including urban economic level, urban industrial structure, population, etc., were widely studied to understand their relationship with intercity passenger volume. Firstly, the urban economic level proved to be one of the necessary impact factors of intercity passenger volume [12; 13; 14]. Traffic demand for business and tourism in intercity transportation increases with the development of the urban economy. The impact factors reflecting the urban economic level were found to be per-capita gross domestic product (GDP), per-capita income, industrial structure, etc., and it was verified that they had a strong correlation with intercity passenger volume [15; 16]. Moreover, both population structure and population size affect the intercity passenger volume significantly. [PERSON] et al. [17] took population density and land use as variables and found that a higher population density and mixed degree of land use have a positive impact on passenger volume of public modes in medium- and long-distance trips. A similar conclusion was also reached by related research [18]. Although the impact factors related to economic level and population have been widely studied in the existing research, those related to the quality of residents' lives, resources, and the environment were rarely studied because they are hard to be quantified with one or several indicators and the corresponding dataset is difficult to obtain [19; 20; 21]. This problem indicates that the relative research on extracting significant impact factors of intercity passenger volume is incomplete and causes the inaccurate prediction of intercity passenger volume, especially for some tourism-driven cities and resource-driven cities. The second phase is to develop a model to achieve an accurate prediction of intercity passenger volume. In the existing studies, multiple logit models, such as the multinomial logit model [22; 23], Box-Cox logit model [24], and nested logit model [25], were developed to study the mode choice of intercity trips and deduce the intercity passenger volume of various modes by calculating the intercity travel rate of surveyed samples [26; 27]. Moreover, intercity passenger volume was predicted by introducing the impact factors. [PERSON] et al. [28] proposed a network equilibrium model with considerations of market price and economic mechanism to predict the intercity freight volume. [PERSON] et al. [29] predicted the passenger volume of intercity railway with multiple indicators of passenger demand, regional economy, and regional traffic infrastructure, with an average predicted error of 3.37%. Another practical approach to predicting intercity passenger volume is based on the historical passenger volume. [PERSON] et al. [30] analyzed the spatiotemporal characteristics of intercity passenger volume and predicted intercity passenger volume on holiday, with a predicted error of 6.43%. Recently, deep learning and machine learning algorithms, represented by various neural networks, have become remarkable at predicting intercity passenger volume by using cellular signaling data and location-based data [4; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Numerous studies have shown that predicted accuracy can be significantly improved by deep learning algorithms [33]. It is noted that the difficulties in obtaining the dataset of intercity passenger volume have been widely emphasized in past studies, especially for some intercity passenger modes of transportation that have additional requirements for an urban population, geographical location, or urban scale, such as airways, railways, and waterways.