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.

引用

页数：5

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