提出新方法提升公交到站预测精度，避免过度标准化导致信息丢失。
Exploring Over-stationarization in Deep Learning-based Bus/Tram Arrival Time Prediction: Analysis and Non-stationary Effect Recovery
- 分两阶段处理：先标准化提升可预测性，再恢复非平稳特征
- 在德累斯顿数据上，模型误差降低超1.7%以上
- 适合关注真实交通动态的智能调度与出行应用
公共交通到站时间预测对提升乘客体验和交通管理至关重要。深度学习因能捕捉非线性与时序动态，在此任务中表现优异。多步预测中，变量联合分布随时间变化会导致数据非平稳性，损害模型性能。以往研究主要通过归一化消除非平稳性以提升可预测性，但可能掩盖非平稳性中蕴含的有用特征，即过度标准化问题。为此，本文提出一种名为非平稳到站预测（NSATP）的新方法，包含两个阶段：序列平稳化与非平稳效应恢复。前者旨在提升可预测性；后者将先进的一维模型扩展至二维，捕捉时间序列中的隐藏周期性，并设计补偿模块，从原始数据中学习缩放与偏移因子以恢复过标准化损失的信息。基于德累斯顿125天的公交运营数据验证，实验结果表明，相比基线方法，所提NSATP在电车预测中分别降低RMSE、MAE、MAPE 2.37%、1.22%、2.26%，在公交车预测中分别降低1.72%、0.60%、1.17%。
原文摘要 · Abstract (English)
Arrival time prediction (ATP) of public transport vehicles is essential in improving passenger experience and supporting traffic management. Deep learning has demonstrated outstanding performance in ATP due to its ability to model non-linear and temporal dynamics. In the multi-step ATP, non-stationary data will degrade the model performance due to the variation in variables' joint distribution along the temporal direction. Previous studies mainly applied normalization to eliminate the non-stationarity in time series, thereby achieving better predictability. However, the normalization may obscure useful characteristics inherent in non-stationarity, which is known as the over-stationarization. In this work, to trade off predictability and non-stationarity, a new approach for multi-step ATP, named non-stationary ATP ( NSATP), is proposed. The method consists of two stages: series stationarization and non-stationarity effect recovery. The first stage aims at improving the predictability. As for the latter, NSATP extends a state-of-the-art method from one-dimensional to two dimensional based models to capture the hidden periodicity in time series and designs a compensation module of over-stationarization by learning scaling and shifting factors from raw data. 125 days' public transport operational data of Dresden is collected for validation. Experimental results show that compared to baseline methods, the proposed NSATP can reduce RMSE, MAE, and MAPE by 2.37%, 1.22%, and 2.26% for trams and by 1.72%, 0.60%, and 1.17% for buses, respectively.
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