Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach
Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach
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DOI：
10.1145/3474837
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发表时间：
2022-01
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通讯作者：
Yingxue Zhang;Yanhua Li;Xun Zhou;Jun Luo;Zhi-Li Zhang
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作者：
Yingxue Zhang;Yanhua Li;Xun Zhou;Jun Luo;Zhi-Li Zhang
城市交通状况（例如，交通速度和交通量）在性质上是高度动态的，即在空间上变化并随时间演变。因此，预测这种交通动态对城市发展和交通管理具有重要意义。然而，这是非常具有挑战性的解决这个问题，由于时空依赖性和交通的不确定性。在这篇文章中，我们从贝叶斯元学习的角度解决了交通动态预测问题，并提出了一种新的连续时空元学习器（cST-ML），它是在由历史交通数据分割的交通预测任务分布上训练的，目标是学习一种策略，可以快速适应相关但看不见的交通预测任务。cST-ML通过以下新点推进贝叶斯黑盒元学习框架来应对交通动态预测挑战：（1）cST-ML使用变分推理捕获交通预测任务的动态，并且为了更好地捕获任务内的时间不确定性，cST-ML在每个任务内执行滚动窗口;（2）cST-ML在架构上有新颖的设计，其中嵌入了CNN和LSTM来捕获交通状态和交通相关特征之间的时空依赖性;（3）设计了cST-ML的新训练和测试算法。我们还对两个真实世界的交通数据集（出租车流入和交通速度）进行了实验，以评估我们提出的cST-ML。实验结果表明，cST-ML能够显著提高城市交通预测性能，尤其是在存在明显的交通动态和时间不确定性时，其预测性能优于所有基线模型。
Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.