  Road construction projects maintain transportation infrastructures. These
projects range from the short-term (e.g., resurfacing or fixing potholes) to
the long-term (e.g., adding a shoulder or building a bridge). Deciding what the
next construction project is and when it is to be scheduled is traditionally
done through inspection by humans using special equipment. This approach is
costly and difficult to scale. An alternative is the use of computational
approaches that integrate and analyze multiple types of past and present
spatiotemporal data to predict location and time of future road constructions.
This paper reports on such an approach, one that uses a
deep-neural-network-based model to predict future constructions. Our model
applies both convolutional and recurrent components on a heterogeneous dataset
consisting of construction, weather, map and road-network data. We also report
on how we addressed the lack of adequate publicly available data - by building
a large scale dataset named "US-Constructions", that includes 6.2 million cases
of road constructions augmented by a variety of spatiotemporal attributes and
road-network features, collected in the contiguous United States (US) between
2016 and 2021. Using extensive experiments on several major cities in the US,
we show the applicability of our work in accurately predicting future
constructions - an average f1-score of 0.85 and accuracy 82.2% - that
outperform baselines. Additionally, we show how our training pipeline addresses
spatial sparsity of data.
