Department of Information Management, School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.
Department of Decision Sciences and Information Management, Faculty of Economics and Business, KU Leuven, 3000 Leuven, Belgium.
Sensors (Basel). 2021 Sep 4;21(17):5950. doi: 10.3390/s21175950.
The COVID-19 pandemic is a significant public health problem globally, which causes difficulty and trouble for both people's travel and public transport companies' management. Improving the accuracy of bus passenger flow prediction during COVID-19 can help these companies make better decisions on operation scheduling and is of great significance to epidemic prevention and early warnings. This research proposes an improved STL-LSTM model (ISTL-LSTM), which combines seasonal-trend decomposition procedure based on locally weighted regression (STL), multiple features, and three long short-term memory (LSTM) neural networks. Specifically, the proposed ISTL-LSTM method consists of four procedures. Firstly, the original time series is decomposed into trend series, seasonality series, and residual series through implementing STL. Then, each sub-series is concatenated with new features. In addition, each fused sub-series is predicted by different LSTM models separately. Lastly, predicting values generated from LSTM models are combined in a final prediction value. In the case study, the prediction of daily bus passenger flow in Beijing during the pandemic is selected as the research object. The results show that the ISTL-LSTM model could perform well and predict at least 15% more accurately compared with single models and a hybrid model. This research fills the gap of bus passenger flow prediction under the influence of the COVID-19 pandemic and provides helpful references for studies on passenger flow prediction.
新冠疫情是全球范围内一个重大的公共卫生问题，给人们的出行和公共交通公司的管理都带来了困难和困扰。提高新冠疫情期间公交车客流量预测的准确性，可以帮助这些公司更好地进行运营调度决策，对于疫情防控和预警具有重要意义。本研究提出了一种改进的 STL-LSTM 模型（ISTL-LSTM），该模型结合了季节性趋势分解程序（STL）、多种特征和三个长短期记忆（LSTM）神经网络。具体来说，所提出的 ISTL-LSTM 方法包括四个步骤。首先，通过实施 STL，将原始时间序列分解为趋势序列、季节性序列和残差序列。然后，将每个子序列与新特征拼接。此外，每个融合的子序列由不同的 LSTM 模型分别进行预测。最后，将来自 LSTM 模型的预测值组合成最终的预测值。在案例研究中，选择新冠疫情期间北京的日公交车客流量预测作为研究对象。结果表明，与单一模型和混合模型相比，ISTL-LSTM 模型的性能更好，预测准确率至少提高了 15%。本研究填补了新冠疫情影响下公交车客流量预测的空白，为客流量预测研究提供了有益的参考。
