cST ML: Continuous Spatial Temporal Meta Learning for Traffic Dynamics Prediction
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 paper, 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; 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.
2020 IEEE International Conference on Data Mining (ICDM)