基于改进LSTNet模型的地铁车站客运量预测算法研究
许玲管剑波许锡伟班勇
Metro Station Passenger Volume Prediction Algorithm Based on Improved LSTNet Model
XU LingGUAN JianboXU XiweiBAN Yong
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作者信息:宁波市轨道交通集团有限公司， 315101， 宁波
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Affiliation:Ningbo Rail Transit Group Co., Ltd., 315101, Ningbo, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.20245803
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中图分类号/CLCN:U293.13； U231.4
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栏目/Col:运营管理
摘要:
［目的］为了有效应对地铁线路高峰时段进出站客运量压力，需构建精准的客运量预测模型，以掌握地铁车站进出站量的时空分布规律，提升地铁线路运营调度决策的科学性。［方法］选取了杭州地铁的客流数据，介绍了数据的类型，以及数据预处理、数据分析的要求。在LSTNet模型基础上引入了Bi-LSTM模型及注意力机制，建立了改进LSTNet预测模型，进而提出了一种融合多尺度时序特征的地铁客流预测方法。选取了杭州地铁6个车站的客流数据，分别采用LSTM模型、LSTNet模型、改进LSTNet模型进行预测。基于预测结果，对改进LSTNet模型的性能进行评估。［结果及结论］与采用LSTM模型、LSTNet模型相比，采用改进LSTNet模型后，地铁车站总客运量预测的平均绝对百分比误差分别降低了5.3%、2.4%。改进LSTNet模型可以显著提升地铁客流预测的精度与稳定性。
Abstracts:
［Objective］ To effectively address the pressure of inbound/outbound passenger volume on metro lines during peak hours, it is necessary to develop an accurate passenger volume prediction model to understand the spatiotemporal distribution patterns of metro station inbound/outbound volumes and enhance the scientific basis for operational and scheduling decisions of metro lines. ［Method］ Passenger volume data from Hangzhou Metro is selected, with an introduction to the types of data and the requirements for data preprocessing and analysis. Building upon the LSTNet (long- and short-term time-series network) model, a Bi-LSTM (bidirectional long- and short-term memory) model and the attention mechanism are incorporated to establish an improved LSTNet prediction model. Furthermore, a metro passenger volume prediction method integrating multi-scale temporal sequence features is proposed. Passenger flow data from 6 Hangzhou Metro stations are selected, and predictions are carried out using the LSTM model, the LSTNet model, and the improved LSTNet model respectively. Based on the prediction results, the performance of the improved LSTNet model is evaluated. ［Result & Conclusion］ Compared with the adopted LSTM and LSTNet models, the improved LSTNet model reduces the mean absolute percentage error (MAPE) of total passenger volume prediction at metro stations by 5.3% and 2.4%, respectively. The improved LSTNet model significantly enhances the accuracy and stability of metro passenger flow prediction.
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