Article
A Two-Phase Approach for Predicting Highway
Passenger Volume
Yun Xiang
1
, Jingxu Chen
2,
*, Weijie Yu
2,
* , Rui Wu
3
, Bing Liu
4
, Baojie Wang
5
and Zhibin Li
2
Citation: Xiang, Y.; Chen, J.; Yu, W.;
Wu, R.; Liu, B.; Wang, B.; Li, Z. A
Two-Phase Approach for Predicting
Highway Passenger Volume. Appl.
Sci. 2021, 11, 6248. https://doi.org/
10.3390/app11146248
Academic Editor: Paola Pellegrini
Received: 29 May 2021
Accepted: 1 July 2021
Published: 6 July 2021
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1
College of City Construction, Jiangxi Normal University, No. 99 Ziyang Avenue, Nanchang 330022, China;
yunxiang@jxnu.edu.cn
2
School of Transportation, Southeast University, No. 2 Southeast University Road, Nanjing 211189, China;
lizhibin@seu.edu.cn
3
School of Computer Science and Engineering, Southeast University, No. 2 Southeast University Road,
Nanjing 211189, China; rhys@seu.edu.cn
4
School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia,
Brisbane, QLD 4072, Australia; bing.liu@uq.edu.au
5
College of Transportation Engineering, Chang’an University, Xi’an 710054, China; wangbj2@163.com
* Correspondence: chenjingxu@seu.edu.cn (J.C.); 213153324@seu.edu.cn (W.Y.)
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 prereq-
uisite 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 pas-
senger 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.
Keywords:
intercity transportation; highway passenger volume; urban attributes; two-phase
approach
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 [3–6].
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 signif-
icant 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 models
Appl. Sci. 2021, 11, 6248. https://doi.org/10.3390/app11146248 https://www.mdpi.com/journal/applsci
