Bike-Sharing Demand Prediction at Community Level under COVID-19 Using Deep Learning

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.

A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities

“…deep learning models) that can capture future traffic patterns more accurately than statistical models ( Abduljabbar et al, 2021a , Abduljabbar et al, 2021b , Abduljabbar et al, 2021c ; Abduljabbar and Dia, 2019a , Abduljabbar and Dia, 2019b ). A study by Mehdizadeh Dastjerdi and Morency (2022) highlighted the importance of using deep learning methods for short term prediction of travel demands to understand the bike-sharing demand changes before and after lockdown.…”

Enhancing Sustainable Transportation: AI-Driven Bike Demand Forecasting in Smart Cities

“…The experimental findings showed that the spatiotemporal deep neural network model, with hyper-clustering, surpassed previous approaches in accurately predicting bike demand. Dastjerdi and Morency [22] researched short-term forecasting, specifically predicting shared bike demand in Montreal 15 min ahead, using a deep learning approach. The study started by identifying six communities within the bike-sharing network using the Louvain algorithm based on a set of bike trips.…”

Probabilistic Forecasting for Demand of a Bike-Sharing Service Using a Deep-Learning Approach

“…Recent studies have widely used machine-learning methodologies for bike-sharingsystem demand prediction [1,[20][21][22]. Gao and Chen [20] applied linear regression, k-nearest neighbors, random forest, and support vector machine (SVM) methods to predict customer demand for bike-sharing systems.…”

“…Mehdizadeh and Morency [22] adopted a hybrid convolutional neural network (CNN) and LSTM model to provide short-term bike-demand forecasts. The stations were grouped by their connection strength, which was defined by the number of trips within each group, and they predicted the demand for each group over the next 15 min.…”