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

共 50 条

[21] Short-term prediction for bike-sharing service using machine learning
Wang, Bo
Kim, Inhi
INTERNATIONAL SYMPOSIUM OF TRANSPORT SIMULATION (ISTS'18) AND THE INTERNATIONAL WORKSHOP ON TRAFFIC DATA COLLECTION AND ITS STANDARDIZATION (IWTDCS'18) - EMERGING TRANSPORT TECHNOLOGIES FOR NEXT GENERATION MOBILITY, 2018, 34 : 171 - 178
[22] Long-term & short-term bike sharing demand predictions using contextual data
Tabandeh, Mirfamam
Antoniou, Constantinos
Cantelmo, Guido
2023 8TH INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS, MT-ITS, 2023,
[23] A long-term multivariate time series prediction model for dissolved oxygen
Hu, Jingzhe
Wang, Peixuan
Li, Dashe
Liu, Shue
ECOLOGICAL INFORMATICS, 2024, 82
[24] Long-term time series prediction with the NARX network: An empirical evaluation
Menezes, Jose Maria P., Jr.
Barreto, Guilherme A.
NEUROCOMPUTING, 2008, 71 (16-18) : 3335 - 3343
[25] VAECGAN: a generating framework for long-term prediction in multivariate time series
Xiang Yin
Yanni Han
Zhen Xu
Jie Liu
Cybersecurity, 4
[26] Multi-Model Integration for Long-Term Time Series Prediction
Huang, Zifang
Shyu, Mei-Ling
Tien, James M.
2012 IEEE 13TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION (IRI), 2012, : 116 - 123
[27] VAECGAN: a generating framework for long-term prediction in multivariate time series
Yin, Xiang
Han, Yanni
Xu, Zhen
Liu, Jie
CYBERSECURITY, 2021, 4 (01)
[28] Deep Learning with Long Short-Term Memory for Time Series Prediction
Hua, Yuxiu
Zhao, Zhifeng
Li, Rongpeng
Chen, Xianfu
Liu, Zhiming
Zhang, Honggang
IEEE COMMUNICATIONS MAGAZINE, 2019, 57 (06) : 114 - 119
[29] Novel robust time series analysis for long-term and short-term prediction
Okamura, Hiroshi
Osada, Yutaka
Nishijima, Shota
Eguchi, Shinto
SCIENTIFIC REPORTS, 2021, 11 (01)
[30] Sequential input selection algorithm for long-term prediction of time series
Tikka, Jarkko
Hollmen, Jaakko
NEUROCOMPUTING, 2008, 71 (13-15) : 2604 - 2615

← 1 2 3 4 5 →