用稀疏乘客报告预测火车拥堵，提升14.9%准确率
Congestion Forecast for Trains with Railroad-Graph-based Semi-Supervised Learning using Sparse Passenger Reports
- 基于铁路网络图结构的半监督学习，融合少量标签与大量无标签数据
- 在标签稀疏条件下，相比顶尖方法提升14.9%预测性能
- 适合交通调度、智能出行系统开发者参考
预测铁路拥堵对提升交通系统效率至关重要。本文提出一种基于乘客通过出行应用上报的拥堵信息进行铁路拥堵预测的方法。尽管乘客报告受到研究关注，但因乘客意愿不足，报告数量有限，导致拥堵标签稀疏，影响预测模型稳定性。为此，我们提出半监督拥堵预测方法SURCONFORT。核心思路有二：首先，采用半监督学习，利用稀疏标注数据与大量未标注数据；其次，构建面向铁路网络的图结构，通过图正则化补充邻近站点的未标注数据信息。基于真实上报数据的实验证明，在标签稀疏条件下，SURCONFORT相较当前最优方法性能提升14.9%。
原文摘要 · Abstract (English)
Forecasting rail congestion is crucial for efficient mobility in transport systems. We present rail congestion forecasting using reports from passengers collected through a transit application. Although reports from passengers have received attention from researchers, ensuring a sufficient volume of reports is challenging due to passenger's reluctance. The limited number of reports results in the sparsity of the congestion label, which can be an issue in building a stable prediction model. To address this issue, we propose a semi-supervised method for congestion forecasting for trains, or SURCONFORT. Our key idea is twofold: firstly, we adopt semi-supervised learning to leverage sparsely labeled data and many unlabeled data. Secondly, in order to complement the unlabeled data from nearby stations, we design a railway network-oriented graph and apply the graph to semi-supervised graph regularization. Empirical experiments with actual reporting data show that SURCONFORT improved the forecasting performance by 14.9% over state-of-the-art methods under the label sparsity.
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