  We propose a Short-term Traffic flow Prediction (STP) framework so that
transportation authorities take early actions to control flow and prevent
congestion. We anticipate flow at future time frames on a target road segment
based on historical flow data and innovative features such as real time feeds
and trajectory data provided by Connected Vehicles (CV) technology. To cope
with the fact that existing approaches do not adapt to variation in traffic, we
show how this novel approach allows advanced modelling by integrating into the
forecasting of flow, the impact of the various events that CV realistically
encountered on segments along their trajectory. We solve the STP problem with a
Deep Neural Networks (DNN) in a multitask learning setting augmented by input
from CV. Results show that our approach, namely MTL-CV, with an average
Root-Mean-Square Error (RMSE) of 0.052, outperforms state-of-the-art ARIMA time
series (RMSE of 0.255) and baseline classifiers (RMSE of 0.122). Compared to
single task learning with Artificial Neural Network (ANN), ANN had a lower
performance, 0.113 for RMSE, than MTL-CV. MTL-CV learned historical
similarities between segments, in contrast to using direct historical trends in
the measure, because trends may not exist in the measure but do in the
similarities.

Networking and Internet Architecture;Artificial Intelligence