基于铁路网络的时空图模型，预测车站列车平均延误
RSTGCN: Railway-centric Spatio-Temporal Graph Convolutional Network for Train Delay Prediction
- 构建铁路中心的时空图网络，融合车次频率注意力机制
- 在印度铁路网数据上，MAE降低18%，误差指标全面领先
- 适合铁路调度优化与智能运维系统研发人员参考
精准预测列车延误对高效铁路运营至关重要。早期方法多聚焦单列车的精确延误预测，而针对车站级延迟预测的研究相对较少。为此，本文提出铁路中心的时空图卷积网络（RSTGCN），用于预测特定时间段内某车站所有进站列车的平均到达延误。该方法引入多项架构创新与新特征融合，包括考虑车次频率的空间注意力机制，显著提升预测性能。为支持研究，我们整理并发布了覆盖整个印度铁路网（IRN）的综合性数据集，包含17个区域共4,735个车站，是迄今研究规模最大、最多样化的铁路网络数据集。通过在多个先进基线上的大量实验验证，RSTGCN在标准指标上均表现优异：相比最佳基线，其在IRN数据集上MAE降低18%，MAPE降低14%，RMSE降低1%-8%。
原文摘要 · Abstract (English)
Accurate prediction of train delays is critical for efficient railway operations. While earlier approaches have largely focused on forecasting the exact delays of individual trains, studies on station-level delay prediction are somewhat sparse. To address this gap, we propose the Railway-centric Spatio-Temporal Graph Convolutional Network (RSTGCN), designed to forecast average arrival delays of all the incoming trains at a particular station for a particular time period. Our approach incorporates several architectural innovations and novel feature integrations, including train frequency-aware spatial attention, which significantly enhance predictive performance. To support this effort, we curate and release a comprehensive dataset for the entire Indian Railway Network (IRN), spanning 4,735 stations across 17 zones - the largest and most diverse railway network studied to date. We conduct extensive experiments using multiple state-of-the-art baselines, demonstrating consistent improvements across standard metrics. Specifically, RSTGCN outperforms the best baseline by 18% in MAE, 14% in MAPE, and 1-8% in RMSE on the IRN.
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