Scalability evaluation of forecasting methods applied to bicycle sharing systems

Cortez-Ordoñez, Alexandra; Vázquez, Pere-Pau; Sanchez-Espigares, José Antonio

doi:10.1016/j.heliyon.2023.e20129

Cited by 2 publications

(1 citation statement)

References 41 publications

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“…Meanwhile, Cortez-Ordoñez et al evaluated the significant distinctions among bike-sharing systems with diverse scales, characteristics, or usage patterns. They also conducted a detailed analysis of the performance of existing predictive algorithms, including ARIMA, Linear Models, and others, in each scenario [ 19 ]. Developments in Deep Learning have led to the widespread use of various deep learning models to extract spatio-temporal correlations for predicting bike-sharing demand, such as Convolutional Neural Network (CNN) [ 20 ], Long Short-Term Memory (LSTM) [ 21 ], and Recurrent Neural Network (RNN) with its variants [ 22 , 23 ].…”

Section: Literature Reviewmentioning

confidence: 99%

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Yang,

Wu,

Wang

et al. 2024

PLoS ONE

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Accurate bike-sharing demand prediction is crucial for bike allocation rebalancing and station planning. In bike-sharing systems, the bike borrowing and returning behavior exhibit strong spatio-temporal characteristics. Meanwhile, the bike-sharing demand is affected by the arbitrariness of user behavior, which makes the distribution of bikes unbalanced. These bring great challenges to bike-sharing demand prediction. In this study, a usage pattern similarity-based dual-network for bike-sharing demand prediction, called FF-STGCN, is proposed. Inter-station flow features and similar usage pattern features are fully considered. The model includes three modules: multi-scale spatio-temporal feature fusion module, bike usage pattern similarity learning module, and bike-sharing demand prediction module. In particular, we design a multi-scale spatio-temporal feature fusion module to address limitations in multi-scale spatio-temporal accuracy. Then, a bike usage pattern similarity learning module is constructed to capture the underlying correlated features among stations. Finally, we employ a dual network structure to integrate inter-station flow features and similar usage pattern features in the bike-sharing demand prediction module to realize the final prediction. Experiments on the Citi Bike dataset have demonstrated the effectiveness of our proposed model. The ablation experiments further confirm the indispensability of each module in the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Yang,

Wu,

Wang

et al. 2024

PLoS ONE

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Are We Back to Normal? A Bike Sharing Systems Mobility Analysis in the Post-COVID-19 Era

Cortez-Ordoñez,

Tulcanaza-Prieto

2024

Sustainability

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In recent years, numerous cities around the world have adopted bike sharing systems (BSSs). The increasing popularity of these transportation means is directly related to their eco-friendly and health advantages. Several factors affect how citizens make use of BSS, for instance, the size and configuration of a city, where docking stations are located, the associated prices, and others. Moreover, these systems have different usage patterns that vary according to the month, day, or hour but remain constant when compared yearly. However, the onset of the COVID-19 pandemic modified mobility behaviors as various governments around the world implemented mobility restrictions to avoid the spread of the virus. The objective of this investigation is to determine if the usage patterns of BSSs have changed permanently or if we have recovered pre-pandemic levels and usage patterns. Given the special characteristics of each BSS, this study focuses on Barcelona’s BSS, called Bicing. To understand the impact of other BSSs, the further analysis of each system’s unique characteristics is necessary. The study employs bike usage information from the public open data service maintained by Barcelona City Hall, namely, the Open Data BCN website, from January 2019 to December 2023, and it covers mechanical and electrical bikes with more than 4 million records per month. The results show that usage patterns were similar before and after the pandemic; the usage increased in 2021 and 2022 and stabilized in 2023, registering a rise of 17.5% in Bicing usage from 2021 to 2023. However, bike type preferences have changed for reasons unrelated to the pandemic restrictions. The main limitation of this investigation is the lack of continuity in the data due to a change in the company that provides the service in Barcelona. For future research, data from other transportation means can be used to analyze all communication behavior in this city. Additionally, if data are available, a study by gender and age can be performed and used to improve the system for certain groups.

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Scalability evaluation of forecasting methods applied to bicycle sharing systems

Cited by 2 publications

References 41 publications

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

FF-STGCN: A usage pattern similarity based dual-network for bike-sharing demand prediction

Are We Back to Normal? A Bike Sharing Systems Mobility Analysis in the Post-COVID-19 Era

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