Citation: Cheng, C.-H.; Tsai, M.-C.;
Cheng, Y.-C. An Intelligent
Time-Series Model for Forecasting
Bus Passengers Based on Smartcard
Data. Appl. Sci. 2022, 12, 4763.
https://doi.org/10.3390/app12094763
Academic Editor: Feng Guo
Received: 12 March 2022
Accepted: 4 May 2022
Published: 9 May 2022
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Article
An Intelligent Time-Series Model for Forecasting Bus
Passengers Based on Smartcard Data
Ching-Hsue Cheng
1
, Ming-Chi Tsai
2,
* and Yi-Chen Cheng
1
1
Department of Information Management, National Yunlin University of Science & Technology, Touliou,
Yunlin 640, Taiwan; chcheng@yuntech.edu.tw (C.-H.C.); hsue6771@gmail.com (Y.-C.C.)
2
Department of Business Administration, I-Shou University, Kaohsiung City 84001, Taiwan
* Correspondence: mct@isu.edu.tw; Tel.: +886-7-6577711 (ext. 5923)
Abstract:
Public transportation systems are an effective way to reduce traffic congestion, air pollution,
and energy consumption. Today, smartcard technology is used to shorten the time spent board-
ing/exiting buses and other types of public transportation; however, this does not alleviate all traffic
congestion problems. Accurate forecasting of passenger flow can prevent serious bus congestion
and improve the service quality of the transportation system. To the best of the current authors’
knowledge, fewer studies have used smartcard data to forecast bus passenger flow than on other
types of public transportation, and few studies have used time-series lag periods as forecast variables.
Therefore, this study used smartcard data from the bus system to identify important variables that
affect passenger flow. These data were combined with other influential variables to establish an
integrated-weight time-series forecast model. For different time data, we applied four intelligent
forecast methods and different lag periods to analyze the forecasting ability of different daily data
series. To enhance the forecast ability, we used the forecast data from the top three of the 80 combined
forecast models and adapted their weights to improve the forecast results. After experiments and
comparisons, the results show that the proposed model can improve passenger flow forecasting based
on three bus routes with three different series of time data in terms of root-mean-square error (RMSE)
and mean absolute percentage error (MAPE). In addition, the lag period was found to significantly
affect the forecast results, and our results show that the proposed model is more effective than other
individual intelligent forecast models.
Keywords:
passenger flow; integrated-weight time-series model; public transportation systems; long
short-term memory network
1. Introduction
Public transportation is considered to be an effective solution to traffic congestion
and environmental pollution. The Federal Transit Administration (FTA) also believes that
public transportation is an effective way to reduce traffic congestion, air pollution, energy
consumption, and private vehicle use [
1
]. The use rate of buses accounted for 46% of all
public transportation use in 2016 by people aged over 15 years according to the Taiwan
Ministry of Transportation survey [2].
Taiwan’s EasyCard Company promoted the smartcard system in 2002 based on the
idea of “one card in hand, unimpeded travel”. It was the first card to be issued for
Taipei mass rapid transit and was then expanded to the Taiwan railway, Taiwan high-
speed railway, and various other types of public transportation. Smartcards can collect
information about vehicle routes, schedules, and real-time driving conditions through
the automatic fare collection (AFC) system for vehicle monitoring, which can greatly
improve public transportation efficiency and safety. The AFC system, when referring to
the transportation system [
3
], is also called the smartcard system. The smartcard system is
regarded as a dynamic and real-time data source for the public transportation system. It
Appl. Sci. 2022, 12, 4763. https://doi.org/10.3390/app12094763 https://www.mdpi.com/journal/applsci
