  Traffic forecasting approaches are critical to developing adaptive strategies
for mobility. Traffic patterns have complex spatial and temporal dependencies
that make accurate forecasting on large highway networks a challenging task.
Recently, diffusion convolutional recurrent neural networks (DCRNNs) have
achieved state-of-the-art results in traffic forecasting by capturing the
spatiotemporal dynamics of the traffic. Despite the promising results, however,
applying DCRNNs for large highway networks still remains elusive because of
computational and memory bottlenecks. We present an approach for implementing a
DCRNN for a large highway network that overcomes these limitations. Our
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. We develop an
overlapping nodes approach for the graph-partitioning-based DCRNN to include
sensor locations from partitions that are geographically close to a given
partition. Furthermore, we demonstrate that the DCRNN model can be used to
forecast the speed and flow simultaneously and that the forecasted values
preserve fundamental traffic flow dynamics. Our approach to developing DCRNN
models that represent large highway networks can be a potential core capability
in advanced highway traffic monitoring systems, where a trained DCRNN model
forecasting traffic at all sensor locations can be used to adjust traffic
management strategies proactively based on anticipated future conditions.
