  Work zone is one of the major causes of non-recurrent traffic congestion and
road incidents. Despite the significance of its impact, studies on predicting
the traffic impact of work zones remain scarce. In this paper, we propose a
data integration pipeline that enhances the utilization of work zone and
traffic data from diversified platforms, and introduce a novel deep learning
model to predict the traffic speed and incident likelihood during planned work
zone events. The proposed model transforms traffic patterns into 2D space-time
images for both model input and output and employs an attention-based
multi-context convolutional encoder-decoder architecture to capture the
spatial-temporal dependencies between work zone events and traffic variations.
Trained and validated on four years of archived work zone traffic data from
Maryland, USA, the model demonstrates superior performance over baseline models
in predicting traffic speed, incident likelihood, and inferred traffic
attributes such as queue length and congestion timings (i.e., start time and
duration). Specifically, the proposed model outperforms the baseline models by
reducing the prediction error of traffic speed by 5% to 34%, queue length by
11% to 29%, congestion timing by 6% to 17%, and increasing the accuracy of
incident predictions by 5% to 7%. Consequently, this model offers substantial
promise for enhancing the planning and traffic management of work zones.
