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SWIFT: A non-emergency response prediction system using sparse Gaussian Conditional Random Fields
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DOI：10.1016/j.pmcj.2020.101317.png)
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Cities have limited resources that must be used efficiently to maintain their smooth operation. To facilitate efficient resource allocation and management in cities, in this paper, we study one such important problem: how long does it take to resolve non-emergency 311 service requests? We present SWIFT, a Non-emergency Response prediction system based on a recently developed structured regression model, sparse Gaussian Conditional Random Fields (GCRFs), that successfully captures the dependencies between historical and future response times. Through extensive experimentation on 311 service requests from New York City (NYC), Kansas City and Baltimore over a three and a half year period between January 2015 to June 2018, we demonstrate that our trained system is able to accurately predict future response times one week in advance using just the previous two weeks data at test time. SWIFT achieves superior prediction performance across all agencies, complaint types, and locations for all the three cities when compared to linear regression, ARIMA and Seasonal ARIMA baselines (up to a factor of 2X). The trained SWIFT system requires low computational resources and data at test time, thus making it an attractive system that can be readily deployed in practice. (C) 2020 Elsevier B.V. All rights reserved.
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