Will there be a construction?: predicting road constructions based on heterogeneous spatiotemporal data

Road construction projects maintain transportation infrastructures, and range from short- to long-term. Deciding what the next construction project is and when it is to be scheduled is traditionally done through inspection by humans using special equipment, which 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, based 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 road constructions augmented by a variety of spatiotemporal attributes and road-network features, collected in the contiguous United States (US) between 2016 and 2021. Extensive experiments on several major cities in the US show the applicability of our approach to accurately predict future constructions.