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Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning.
Mehdizadeh Dastjerdi, Aliasghar; Morency, Catherine.
  • Mehdizadeh Dastjerdi A; Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada.
  • Morency C; Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada.
Sensors (Basel) ; 22(3)2022 Jan 29.
Article in English | MEDLINE | ID: covidwho-1667287
ABSTRACT
An important question in planning and designing bike-sharing services is to support the user's travel demand by allocating bikes at the stations in an efficient and reliable manner which may require accurate short-time demand prediction. This study focuses on the short-term forecasting, 15 min ahead, of the shared bikes demand in Montreal using a deep learning approach. Having a set of bike trips, the study first identifies 6 communities in the bike-sharing network using the Louvain algorithm. Then, four groups of LSTM-based architectures are adopted to predict pickup demand in each community. A univariate ARIMA model is also used to compare results as a benchmark. The historical trip data from 2017 to 2021 are used in addition to the extra inputs of demand related engineered features, weather conditions, and temporal variables. The selected timespan allows predicting bike demand during the COVID-19 pandemic. Results show that the deep learning models significantly outperform the ARIMA one. The hybrid CNN-LSTM achieves the highest prediction accuracy. Furthermore, adding the extra variables improves the model performance regardless of its architecture. Thus, using the hybrid structure enriched with additional input features provides a better insight into the bike demand patterns, in support of bike-sharing operational management.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22031060

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Deep Learning / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Year: 2022 Document Type: Article Affiliation country: S22031060