  Highway traffic modeling and forecasting approaches are critical for
intelligent transportation systems. Recently, deep-learning-based traffic
forecasting methods have emerged as state of the art for a wide range of
traffic forecasting tasks. However, these methods require a large amount of
training data, which needs to be collected over a significant period of time.
This can present a number of challenges for the development and deployment of
data-driven learning methods for highway networks that suffer from lack of
historical data. A promising approach to address this issue is transfer
learning, where a model trained on one part of the highway network can be
adapted for a different part of the highway network. We focus on diffusion
convolutional recurrent neural network (DCRNN), a state-of-the-art graph neural
network for highway network forecasting. It models the complex spatial and
temporal dynamics of the highway network using a graph-based diffusion
convolution operation within a recurrent neural network. DCRNN cannot perform
transfer learning, however, because it learns location-specific traffic
patterns, which cannot be used for unseen regions of the network. To that end,
we develop a new transfer learning approach for DCRNN, where a single model
trained on data-rich regions of the highway network can be used to forecast
traffic on unseen regions of the highway network. We evaluate the ability of
our approach to forecast the traffic on the entire California highway network
with one year of time series data. We show that TL-DCRNN can learn from several
regions of the California highway network and forecast the traffic on the
unseen regions of the network with high accuracy. Moreover, we demonstrate that
TL-DCRNN can learn from San Francisco region traffic data and can forecast
traffic on the Los Angeles region and vice versa.

Machine Learning;Machine Learning