用因果关系提升数据少城市的出行流量预测精度
Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities
- 通过强化学习挖掘发达城市中的普遍因果规律，构建城市特征因果图
- 在数据稀疏城市中利用因果图重建特征，使预测误差降低最高11%
- 适合交通规划、智慧城市等需要跨城市迁移知识的研究者
准确的起讫点（OD）流量预测对城市发展至关重要，有助于优化城市结构与布局。然而，由于区域特征缺失和OD流量数据不足，在发展中国家城市中进行预测极具挑战。为此，本文提出一种新型因果增强型OD流量预测框架（CE-OFP），旨在实现城市间城市知识迁移，并显著提升数据稀疏城市中的预测准确性。具体地，我们设计了一种新颖的强化学习模型，用于发现数据丰富城市中城市特征间的普遍因果关系，并构建相应的因果图；随后，构建因果增强变分自编码器（CE-VAE），将因果图融入以实现数据稀疏城市中的有效特征重构；最后，借助图注意力网络设计知识蒸馏方法，将数据丰富城市的OD预测模型迁移至数据稀疏城市。在两组真实数据集上的大量实验表明，所提方法显著优于现有先进基线，可使数据稀疏城市的OD流量预测均方根误差（RMSE）最高降低11%。
原文摘要 · Abstract (English)
Accurate origin-destination (OD) flow prediction is of great importance to developing cities, as it can contribute to optimize urban structures and layouts. However, with the common issues of missing regional features and lacking OD flow data, it is quite daunting to predict OD flow in developing cities. To address this challenge, we propose a novel Causality-Enhanced OD Flow Prediction (CE-OFP), a unified framework that aims to transfer urban knowledge between cities and achieve accuracy improvements in OD flow predictions across data-scarce cities. In specific, we propose a novel reinforcement learning model to discover universal causalities among urban features in data-rich cities and build corresponding causal graphs. Then, we further build Causality-Enhanced Variational Auto-Encoder (CE-VAE) to incorporate causal graphs for effective feature reconstruction in data-scarce cities. Finally, with the reconstructed features, we devise a knowledge distillation method with a graph attention network to migrate the OD prediction model from data-rich cities to data-scare cities. Extensive experiments on two pairs of real-world datasets validate that the proposed CE-OFP remarkably outperforms state-of-the-art baselines, which can reduce the RMSE of OD flow prediction for data-scarce cities by up to 11%.
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