TY - GEN
T1 - Daily Passenger Volume Prediction in the Bus Transportation System using ARIMAX Model with Big Data
AU - Su, Yingchen
AU - Ye, Yinna
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - Based on the real data collected from the bus IC card payment database, firstly a time series of daily passenger volumes in a given bus line was obtained and then two kinds of time series models, ARMA with quadratic trend and ARIMAX, were proposed to do the prediction. The experiment results show that both models can make prediction effectively and especially ARIMAX model, which takes daily temperatures in to consideration, performs better in terms of prediction accuracy.
AB - Based on the real data collected from the bus IC card payment database, firstly a time series of daily passenger volumes in a given bus line was obtained and then two kinds of time series models, ARMA with quadratic trend and ARIMAX, were proposed to do the prediction. The experiment results show that both models can make prediction effectively and especially ARIMAX model, which takes daily temperatures in to consideration, performs better in terms of prediction accuracy.
KW - ARIMA model
KW - ARIMAX model
KW - ARMA model
KW - Bus transportation system
KW - Passenger flow volume prediction
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=85100473436&partnerID=8YFLogxK
U2 - 10.1109/CyberC49757.2020.00055
DO - 10.1109/CyberC49757.2020.00055
M3 - Conference Proceeding
AN - SCOPUS:85100473436
T3 - Proceedings - 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2020
SP - 291
EP - 300
BT - Proceedings - 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2020
Y2 - 29 October 2020 through 30 October 2020
ER -