Long Term Time Series Prediction of Bike Sharing Trips: A Cast Study of Budapest City

被引：4
|
作者：
Jaber, Ahmed [1 ]
Csonka, Balint [1 ]
Juhasz, Janos [2 ]
机构：
[1] Budapest Univ Technol & Econ, Fac Transportat Engn & Vehicle Engn, Dept Transportat Technol & Econ, Muegyet Rkp 3, H-1111 Budapest, Hungary
[2] Budapest Univ Technol & Econ, Fac Civil Engn, Muegyet Rkp 3, H-1111 Budapest, Hungary
来源：
2022 SMART CITIES SYMPOSIUM PRAGUE (SCSP) | 2022年
关键词：
ARIMA; ANN; Bike Sharing; Forecasting; Time Series; COVID-19; BEHAVIOR;
D O I：
10.1109/SCSP54748.2022.9792540
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires more service planning based on the behavior of bike-sharing users. The Time Series models Seasonal Auto-Regressive Integrated Moving Average, Artificial Neural Network, and Exponential Smoothing have been investigated to reveal bike-sharing use for five years. Results show that weekends are attracting more trips. Summer is the most season influencing more demand. The model is predicted within a seasonal trend with a three-day lag. Compared to the Exponential Smoothing Model, SARIMA and ANN provide better predictions. Similarities are obtained in the periods of COVID-19 and after that, in the lags and highest days having bike-sharing trips. This study helps decision-makers in forecasting bike-sharing trips.
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页数：5
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