  This paper develops a data-driven toolkit for traffic forecasting using
high-resolution (a.k.a. event-based) traffic data. This is the raw data
obtained from fixed sensors in urban roads. Time series of such raw data
exhibit heavy fluctuations from one time step to the next (typically on the
order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of
traffic conditions are critical for traffic operations applications (e.g.,
adaptive signal control). But traffic forecasting tools in the literature deal
predominantly with 3-5 minute aggregated data, where the typical signal cycle
is on the order of 2 minutes. This renders such forecasts useless at the
operations level. To this end, we model the traffic forecasting problem as a
matrix completion problem, where the forecasting inputs are mapped to a higher
dimensional space using kernels. The formulation allows us to capture both
nonlinear dependencies between forecasting inputs and outputs but also allows
us to capture dependencies among the inputs. These dependencies correspond to
correlations between different locations in the network. We further employ
adaptive boosting to enhance the training accuracy and capture historical
patterns in the data. The performance of the proposed methods is verified using
high-resolution data obtained from a real-world traffic network in Abu Dhabi,
UAE. Our experimental results show that the proposed method outperforms other
state-of-the-art algorithms.
