2019/06/21 by Zhengbo Zou, Zou, Zhengbo, Semiha Ergan +1
Business, Management and Accounting · Engineering · #Computers and Society (cs.CY) #Evacuation and Crowd Dynamics #FOS: Computer and information sciences #Facility Location and Emergency Management #Underground infrastructure and sustainability
paper · pdf · doi:10.48550/arxiv.1906.08910
openalex publication_date 2019/06/21 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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.