Abstract

Urban streetworks are necessary for infrastructure maintenance and renewal, but they may generate local disruptions that are difficult to observe systematically. This study develops a reproducible public-data-based workflow for prioritizing urban streetwork closure days according to their risk of nearby infrastructure-related 311 service requests (municipal non-emergency reports submitted by residents or other street users). The case study uses New York City data from 2022 to 2024 and integrates recorded street closures, selected 311 complaints, and daily weather variables. A closure day is defined as a recorded street closure active on a single calendar day. The main target identifies whether at least one selected 311 request occurred in the vicinity of an active closure on the same day. The final model is a weather-informed, calibrated Random Forest. The final dataset contained 2,708,620 closure day records. The model achieved an ROC-AUC of 0.6408, a PR-AUC of 0.0799, and a Brier score of 0.0459. Among the top 5% highest-risk closure days, precision reached 11.48%, with a lift of 2.35 over random selection, relative to a 2024 test prevalence of approximately 4.88%. Permutation importance showed that location and calendar features dominated prediction, while continuous weather variables provided a smaller but measurable contribution.

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