Urban fire station location planning using predicted demand and service quality index

Arnab Dey, Andrew Heger, Darin England

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this article, we propose a systematic approach for fire station location planning. We develop machine learning models, based on Random Forest and Extreme Gradient Boosting, for demand prediction and utilize the models further to define a generalized index to measure quality of fire service in urban settings. Our model is built upon spatial data collected from multiple different sources. Efficacy of proper facility planning depends on choice of candidates where fire stations can be located along with existing stations, if any. Also, the travel time from these candidates to demand locations need to be taken care of to maintain fire safety standard. Here, we propose a travel time-based clustering technique to identify suitable candidates. Finally, we develop an optimization problem to select best locations to install new fire stations. Our optimization problem is built upon maximum coverage problem, based on integer programming. We further develop a two-stage stochastic optimization model to characterize the confidence in our decision outcome. We present a detailed experimental study of our proposed approach in collaboration with city of Victoria Fire Department, MN, USA. Our demand prediction model achieves true positive rate of 80% and false positive rate of 20% approximately. We aid Victoria Fire Department to select a location for a new fire station using our approach. We present detailed results on improvement statistics by locating a new facility, as suggested by our methodology, in the city of Victoria.

Original languageEnglish (US)
Pages (from-to)33-48
Number of pages16
JournalInternational Journal of Data Science and Analytics
Volume15
Issue number1
DOIs
StatePublished - Jan 2023

Bibliographical note

Funding Information:
No funds, grants, or other support was received.

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Keywords

  • Extreme gradient boosting
  • Facility planning
  • Fire risk
  • Optimization
  • Random forest
  • Risk prediction

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