按区域分组建模，提升公交载客量预测精度。
Comparative Analysis of Polygon-Based and Global Machine Learning Models for Bus Occupancy Prediction
- 将城市划分为共享相似客流特征的区域，为每个区域训练独立模型。
- 局部模型预测精度与全局模型相当，且更适应区域差异。
- 适合城市交通管理者和智能调度系统开发者参考。
精准预测公交乘客数量对优化公共交通系统至关重要。传统模型常将整个城市视为单一均质区域，难以捕捉不同城区的独特动态。本文提出一种新框架，通过空间聚类结合多维特征分析，提升公交客流预测性能。该框架融合了按路线、时间与站点划分的客流数据，以及开放源数据，如地理吸引力点、气象条件（温度、降雨）和时间模式（时段、星期几）。基于邻近公交站具有相似客流特征的原则，将城市划分为若干区域，并为每个区域训练独立的局部预测模型。结果表明，这种空间感知的局部建模策略在精度上可媲美全局模型，证明其在应对城市差异方面的有效性，为实现更精准、高效的公交服务改进提供了新路径。
原文摘要 · Abstract (English)
Accurate forecasting of bus ridership (passengers numbers) is crucial for efficient management and optimization of public transport systems. Traditional forecasting models often fail to capture the unique and localized dynamics of different urban areas by treating the entire city as a single, homogeneous region. This paper introduces a novel framework that enhances bus ridership prediction by integrating a spatial clustering methodology with multi-dimensional feature analysis. The proposed framework utilizes a diverse set of data, including bus ridership data (by route number, time, and bus stop) complemented by a variety of open source data, such as spatial features (e.g., attractive destinations), meteorological conditions (e.g., temperature, rainfall), and temporal patterns (e.g., time of day, day of week). By clustering the urban area into distinct regions, based on the principle that bus stops in close proximity share similar ridership characteristics, a separate local forecasting model is trained for each of these clusters. This localized approach demonstrates an accuracy comparable to that of global models. The findings suggest that a spatially-aware, localized modeling strategy is effective for public transport prediction, paving the way for more targeted and efficient service improvements.
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