Urban rail transit provides significant comprehensive benefits such as large traffic volume and high speed, serving as one of the most important components of urban traffic construction management and congestion solution. Using real passenger flow data of an Asian subway system from April to June of 2018, this work analyzes the space-time distribution of the passenger flow using short-term traffic flow prediction. Stations are divided into four types for passenger flow forecasting, and meteorological records are collected for the same period. Then, machine learning methods with different inputs are applied and multivariate regression is performed to evaluate the improvement effect of each weather element on passenger flow forecasting of representative metro stations on hourly basis. Our results show that by inputting weather variables the precision of prediction on weekends enhanced while the performance on weekdays only improved marginally, while the contribution of different elements of weather differ. Also, different categories of stations are affected differently by weather. This study provides a possible method to further improve other prediction models, and attests to the promise of data-driven analytics for optimization of short-term scheduling in transit management.

翻译：城市轨道交通具有运量大、速度快等显著综合效益，是城市交通建设管理及缓解拥堵的关键组成部分之一。本研究利用2018年4月至6月亚洲某地铁系统的真实客流数据，通过短期交通流预测分析客流的时空分布特征。将车站划分为四类进行客流预测，并采集同期的气象记录数据。随后应用不同输入的机器学习方法，通过多元回归评估各气象要素对代表性地铁站点小时级客流预测的改进效果。结果表明：加入天气变量后，周末预测精度显著提升，而工作日的预测性能仅小幅改善，且不同天气要素的贡献存在差异；此外，不同类型车站受天气影响程度各不相同。本研究为其他预测模型的进一步优化提供了可行方法，并验证了数据驱动分析在城市交通管理短期调度优化中的应用潜力。