对比10种模型预测波士顿地铁客流与延误，发现时间特征比天气更重要。
Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models
- 用10种统计与机器学习模型预测地铁进出站量和系统延误数。
- 周几或季节信息比天气数据更能提升预测准确率，天气反而易导致过拟合。
- 首次将自激点过程用于MBTA延误建模，为交通预测提供新思路。
马萨诸塞湾交通局（MBTA）是波士顿主要公共交通运营商，运营铁路、地铁和公交等多种交通方式。然而，系统常面临延误及客流波动，影响运行效率与乘客满意度。本文比较了现有及新型方法，以确定预测地铁站闸机进出量（作为地铁使用量代理指标）和整个MBTA系统延误次数的最佳方案。研究考虑了星期、季节、气压、风速、平均温度和降水等影响公共交通的因素。评估了10种统计与机器学习模型在预测次日地铁使用量上的表现；在预测延误数量时，扩展至每日报错11个模型，引入自激点过程模型，首次将点过程框架应用于MBTA延误建模。通过选择性特征加入实验，分析特征重要性，并以均方根误差（RMSE）衡量模型精度。结果显示，提供星期或季节信息对预测精度的提升远超天气数据，且天气数据通常降低性能，表明模型存在过拟合倾向。
原文摘要 · Abstract (English)
The Massachusetts Bay Transportation Authority (MBTA) is the main public transit provider in Boston, operating multiple means of transport, including trains, subways, and buses. However, the system often faces delays and fluctuations in ridership volume, which negatively affect efficiency and passenger satisfaction. To further understand this phenomenon, this paper compares the performance of existing and unique methods to determine the best approach in predicting gated station entries in the subway system (a proxy for subway usage) and the number of delays in the overall MBTA system. To do so, this research considers factors that tend to affect public transportation, such as day of week, season, pressure, wind speed, average temperature, and precipitation. This paper evaluates the performance of 10 statistical and machine learning models on predicting next-day subway usage. On predicting delay count, the number of models is extended to 11 per day by introducing a self-exciting point process model, representing a unique application of a point-process framework for MBTA delay modeling. This research involves experimenting with the selective inclusion of features to determine feature importance, testing model accuracy via Root Mean Squared Error (RMSE). Remarkably, it is found that providing either day of week or season data has a more substantial benefit to predictive accuracy compared to weather data; in fact, providing weather data generally worsens performance, suggesting a tendency of models to overfit.
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