Department of Civil, Geological and Mining Engineering, Polytechnique Montréal, Montreal, QC H3T 1J4, Canada.
Sensors (Basel). 2022 Jan 29;22(3):1060. doi: 10.3390/s22031060.
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.
在规划和设计自行车共享服务时，一个重要的问题是通过以高效和可靠的方式在车站分配自行车来支持用户的出行需求，这可能需要准确的短期需求预测。本研究使用深度学习方法，专注于蒙特利尔共享单车需求的短期预测（提前 15 分钟）。在获得一组自行车行程后，研究首先使用 Louvain 算法在自行车共享网络中识别出 6 个社区。然后，采用四组基于 LSTM 的架构来预测每个社区的取车需求。还使用了单变量 ARIMA 模型作为基准进行比较结果。除了需求相关工程特征、天气条件和时间变量等额外输入外，还使用了 2017 年至 2021 年的历史行程数据。选择的时间段允许预测 COVID-19 大流行期间的自行车需求。结果表明，深度学习模型的表现明显优于 ARIMA 模型。混合 CNN-LSTM 实现了最高的预测精度。此外，无论架构如何，添加额外变量都可以提高模型性能。因此，使用混合结构并添加额外的输入特征可以更好地洞察自行车需求模式，从而支持自行车共享运营管理。
