  Continuous construction and rehabilitation in urban settings have unavoidable
impacts on arrival times of first responders to emergency locations. Current
research efforts on emergency response assessments focus on case studies, where
specific periods (e.g., super storm Sandy) of emergency response times are
analyzed. Simulation based studies that aim to evaluate response times in
relation to various constraints/fleet sizes also exist. However, they do not
analyze how specific changes (e.g., new and ongoing construction projects) in
urban settings impact emergency response times of first responders. This paper
aims to fill the gap and proposes a novel approach to predict the expected
emergency response time for a given location using the fabric of zones
regarding construction activities. This approach relies on historical records
of emergency response and construction permits issued by city agencies. The
approach first defines the signature of a zone (by zip codes) for construction
activities based on the distribution of historical construction work types
permitted in that zone over time. Then, zones that share similar signatures are
clustered to find if there exists a relationship between construction
signatures and emergency response times. Next, supervised learning algorithms
are deployed to predict the average emergency response times for each cluster.
The approach was tested using New York City's construction permit and emergency
response records, and can be easily replicated for other cities with similar
public datasets. This study serves as the first step towards quantitatively
understanding construction projects' impact on a quality of life (QoL)
indicator (specifically emergency response times) in urban settings.
