利用天气数据预测地铁系统客流

Lijuan Liu, R. Chen, Shunzhi Zhu
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引用次数: 4

摘要

地铁系统在缓解大城市交通拥堵方面发挥着重要作用。在本文中,受天气对客流的潜在影响的启发,我们开发了一个基于rnn的地铁客流预测模型,该模型结合了历史客流数据、相应的时间数据和天气数据。以台北车站客流预测模型为例进行了实证研究。实验结果表明,加入天气数据构建客流预测模型有助于改善预测结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Passenger Flow Prediction Using Weather Data for Metro Systems
Metro systems play an important role in reducing traffic congestion in large cities. In this paper, inspired by the potential impact of weather on passenger flow, we have developed an RNN-based model for metro passenger flow prediction with historical passenger flow data, the corresponding temporal data and weather data. A case study of passenger flow prediction model at Taipei Main Station is performed. The experimental results verify that adding the weather data to construct a passenger flow prediction model is contributory to improve the results.
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