Abstract: Traffic forecasting approaches are critical to develop adaptive strategies for mobility. Near-term traffic forecasting is a foundational component of these strategies. ​Traffic patterns have complex spatial and temporal dependencies that make accurate forecasting on large highway networks a challenging task​ for machine learning models​. ​ ​We present a​ deep learning​ approach ​for ​large scale traffic forecasting using graph-partitioning-based diffusion convolutional recurrent neural networks (DCRNNs). This approach uses a graph-partitioning method to decompose a large highway network into smaller networks and trains them independently. We demonstrate the efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11,160 sensor locations.