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

Amin Karimi Monsefi

Abstract:

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 deepneural-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.