  Accurate and reliable prediction of traffic measurements plays a crucial role
in the development of modern intelligent transportation systems. Due to more
complex road geometries and the presence of signal control, arterial traffic
prediction is a level above freeway traffic prediction. Many existing studies
on arterial traffic prediction only consider temporal measurements of flow and
occupancy from loop sensors and neglect the rich spatial relationships between
upstream and downstream detectors. As a result, they often suffer large
prediction errors, especially for long horizons. We fill this gap by enhancing
a deep learning approach, Diffusion Convolutional Recurrent Neural Network,
with spatial information generated from signal timing plans at targeted
intersections. Traffic at signalized intersections is modeled as a diffusion
process with a transition matrix constructed from the phase splits of the
signal phase timing plan. We apply this novel method to predict traffic flow
from loop sensor measurements and signal timing plans at an arterial
intersection in Arcadia, CA. We demonstrate that our proposed method yields
superior forecasts; for a prediction horizon of 30 minutes, we cut the MAPE
down to 16% for morning peaks, 10% for off peaks, and even 8% for afternoon
peaks. In addition, we exemplify the robustness of our model through a number
of experiments with various settings in detector coverage, detector type, and
data quality.
