TEAM: Topological Evolution-aware Framework for Traffic Forecasting

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Abstract

Due to the global trend towards urbanization, people increasingly move to and live in cities that then continue to grow. Traffic fore-move to and live in cities that then continue to grow. Traffic forecasting plays an important role in the intelligent transportation systems of cities as well as in spatio-temporal data mining. State-systems of cities as well as in spatio-temporal data mining. Stateof-the-art forecasting is achieved by deep-learning approaches due to their ability to contend with complex spatio-temporal dynam-to their ability to contend with complex spatio-temporal dynamics. However, existing methods assume the input is fixed-topology road networks and static traffic time series. These assumptions fail to align with urbanization, where time series are collected continuously and road networks evolve over time. In such set-continuously and road networks evolve over time. In such settings, deep-learning models require frequent re-initialization and re-training, imposing high computational costs. To enable much more efficient training without jeopardizing model accuracy, we propose the T opological E volution- a ware Fra m ework ( TEAM ) for traffic forecasting that incorporates convolution and attention. This combination of mechanisms enables better adaptation to newly col-combination of mechanisms enables better adaptation to newly collected time series while being able to maintain learned knowledge from old time series. TEAM features a continual learning module based on the Wasserstein metric that acts as a buffer that can iden-based on the Wasserstein metric that acts as a buffer that can identify the most stable and the most changing network nodes. Then, only data related to stable nodes is employed for re-training when consolidating a model. Further, only data of new nodes and their adjacent nodes as well as data pertaining to changing nodes are used to re-train the model. Empirical studies with two real-world traffic datasets offer evidence that TEAM is capable of much lower re-training costs than existing methods are, without jeopardizing forecasting accuracy.