Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon

Ana Sofia Afonso; João Moura Pires; Nuno Datia; Fernando Birra

doi:10.1109/IV56949.2022.00067

Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon

Ana Sofia Afonso, João Moura Pires, Nuno Datia, Fernando Birra

NOVALincs

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.

Original language	English
Title of host publication	Proceedings
Subtitle of host publication	2022 26th International Conference Information Visualisation, IV 2022
Place of Publication	New Jersey
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	366-372
Number of pages	7
ISBN (Electronic)	978-1-6654-9007-8
ISBN (Print)	978-1-6654-9008-5
DOIs	https://doi.org/10.1109/IV56949.2022.00067
Publication status	Published - 2022
Event	26th International Conference Information Visualisation, IV 2022 - Vienna, Austria Duration: 19 Jul 2022 → 22 Jul 2022

Publication series

Name	Proceedings of the International Conference on Information Visualisation
Volume	2022-July
ISSN (Print)	1550-6037
ISSN (Electronic)	2375-0138

Conference

Conference	26th International Conference Information Visualisation, IV 2022
Country/Territory	Austria
City	Vienna
Period	19/07/22 → 22/07/22

Keywords

Bicycle Demand Forecasting
Interactive Data Visualization
Rebalancing
Shared Bicycle Systems
Time Series

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IV56949.2022.00067

Cite this

Afonso, A. S., Pires, J. M., Datia, N., & Birra, F. (2022). Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon. In Proceedings: 2022 26th International Conference Information Visualisation, IV 2022 (pp. 366-372). (Proceedings of the International Conference on Information Visualisation; Vol. 2022-July). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IV56949.2022.00067

Afonso, Ana Sofia ; Pires, João Moura ; Datia, Nuno et al. / Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon. Proceedings: 2022 26th International Conference Information Visualisation, IV 2022. New Jersey : Institute of Electrical and Electronics Engineers (IEEE), 2022. pp. 366-372 (Proceedings of the International Conference on Information Visualisation).

@inproceedings{11e9a33d15d6402d83bed2c0d0bcdab6,

title = "Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon",

abstract = "With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.",

keywords = "Bicycle Demand Forecasting, Interactive Data Visualization, Rebalancing, Shared Bicycle Systems, Time Series",

author = "Afonso, {Ana Sofia} and Pires, {Jo{\~a}o Moura} and Nuno Datia and Fernando Birra",

note = "Funding Information: This work is supported by NOVA LINCS (UIDB / 04516/2020) with financial support from FCT— Funda{\c c}{\~a}o para a Ci{\^e}ncia e a Tecnologia, through national funds. Publisher Copyright: {\textcopyright} 2022 IEEE.; 26th International Conference Information Visualisation, IV 2022 ; Conference date: 19-07-2022 Through 22-07-2022",

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Afonso, AS , Pires, JM , Datia, N & Birra, F 2022, Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon. in Proceedings: 2022 26th International Conference Information Visualisation, IV 2022. Proceedings of the International Conference on Information Visualisation, vol. 2022-July, Institute of Electrical and Electronics Engineers (IEEE), New Jersey, pp. 366-372, 26th International Conference Information Visualisation, IV 2022, Vienna, Austria, 19/07/22. https://doi.org/10.1109/IV56949.2022.00067

Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon. / Afonso, Ana Sofia ; Pires, João Moura ; Datia, Nuno et al.
Proceedings: 2022 26th International Conference Information Visualisation, IV 2022. New Jersey: Institute of Electrical and Electronics Engineers (IEEE), 2022. p. 366-372 (Proceedings of the International Conference on Information Visualisation; Vol. 2022-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon

AU - Afonso, Ana Sofia

AU - Pires, João Moura

AU - Datia, Nuno

AU - Birra, Fernando

N1 - Funding Information: This work is supported by NOVA LINCS (UIDB / 04516/2020) with financial support from FCT— Fundação para a Ciência e a Tecnologia, through national funds. Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.

AB - With urban development in cities, shared bicycle systems are increasingly used as a way to avoid traffic caused by cars, promoting sustainable mobility and contributing for traffic and pollution reduction in urban areas. The imbalance in the availability of bicycles and docks at the stations of the systems makes it impossible to rent and return bicycles, making it necessary to redistribute them across the network. However, this process has flaws, mainly during rush hours. In this paper, we analyse data provided by the Lisbon City Council regarding their bike sharing system, which has the rebalancing operations' influence. Since the original data was contaminated with the rebalancing operations, an analysis was conducted in an attempt to remove this influence from the data. Following this analysis, a new dataset was created using only the trip data to enable model development for each station and predict the bicycle demand. The plateaus in the created dataset were then analysed to determine if they're due to lack of demand from costumers, or due to stations being full or empty.

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KW - Shared Bicycle Systems

KW - Time Series

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ER -

Afonso AS , Pires JM , Datia N , Birra F. Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon. In Proceedings: 2022 26th International Conference Information Visualisation, IV 2022. New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 2022. p. 366-372. (Proceedings of the International Conference on Information Visualisation). doi: 10.1109/IV56949.2022.00067

Bicycle Demand Prediction to Optimize the Rebalancing of a Bike Sharing System in Lisbon

Abstract

Publication series

Conference

Keywords

UN SDGs

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