The primary objective of this study is to predict the short-term metro passenger flow using the proposed hybrid spatiotemporal deep learning neural network (HSTDL-net). The metro passenger flow data is collected from line 2 of Nanjing metro system to illustrate the study procedure. A hybrid spatiotemporal deep learning model is developed to predict both inbound and outbound passenger flows for every 10 minutes. The results suggest that the proposed HSTDL-net achieves better prediction performance on suburban stations than on urban stations, as well as generating the best prediction accuracy on transfer stations in terms of the lowest MAPE value. Moreover, a comparative analysis is conducted to compare the performance of proposed HSTDL-net with other typical methods, such as ARIMA, MLP, CNN, LSTM, and GBRT. The results indicate that, for both inbound and outbound passenger flow predictions, the HSTDL-net outperforms all the compared models on three types of stations. The results suggest that the proposed hybrid spatiotemporal deep learning neural network can more effectively and fully discover both spatial and temporal hidden correlations between stations for short-term metro passenger flow prediction. The results of this study could provide insightful suggestions for metro system authorities to adjust the operation plans and enhance the service quality of the entire metro system.

中文翻译：

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混合时空深度学习模型用于短期地铁客流预测

这项研究的主要目的是使用提出的混合时空深度学习神经网络（HSTDL-net）预测短期地铁乘客流量。从南京地铁系统的2号线收集地铁乘客流量数据，以说明研究过程。开发了一种混合时空深度学习模型来预测每10分钟的入站和出站旅客流量。结果表明，所提出的HSTDL-net在郊区站比城市站具有更好的预测性能，并且以最低的MAPE值在换乘站上产生了最佳的预测精度。此外，进行了比较分析，以将提议的HSTDL-net与其他典型方法（例如ARIMA，MLP，CNN，LSTM和GBRT）进行比较。结果表明，对于入站和出站客流预测，HSTDL-net在三种类型的车站上均优于所有比较模型。结果表明，提出的混合时空深度学习神经网络可以更有效，更全面地发现车站之间的时空隐藏相关性，以进行短期地铁客流预测。研究结果可为地铁系统管理部门调整运营计划，提高整个地铁系统的服务质量提供有益的建议。结果表明，提出的混合时空深度学习神经网络可以更有效，更全面地发现车站之间的时空隐藏相关性，以进行短期地铁客流预测。研究结果可为地铁系统管理部门调整运营计划，提高整个地铁系统的服务质量提供有益的建议。结果表明，提出的混合时空深度学习神经网络可以更有效，更全面地发现车站之间的时空隐藏相关性，以进行短期地铁客流预测。研究结果可为地铁系统管理部门调整运营计划，提高整个地铁系统的服务质量提供有益的建议。