Scalable Real-time Prediction and Analysis of San Francisco Fire Department Response Times

Liu, Xu; Melancon, Sarah; Presta, Jon-Ross; Reevesman, Adam; Spiering, Brian J.; Woodbridge, Diane

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00154

Cited by 9 publications

(18 citation statements)

References 14 publications

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“…In addition, by analyzing models trained with original data, while the smaller RMSE for LASSO is about 5.65, for more complex ML-based models, RMSE is less than 5.6, achieving 5.54 with XGBoost and LGBM. In comparison with the results of existing literature, lower R 2 scores and similar RMSE and MAE results were achieved in [11] to predict ART while using original location data only. With more details, Table A2 in Appendix A numerically exhibits the results from Figure 4.…”

Section: Privacy-preserving Art Predictionsupporting

confidence: 66%

“…The medical literature has mainly focused attention on the analysis of ART [3,34,43] and its association with trauma [2,30] and cardiac arrest [1,4,6], for example. To reduce ART, some works propose reallocation of ambulances [5,44], operation demand forecasting [5,7,8,22,45], travel time prediction [12], simulation models [35,46], and EMS response time predictions [11,12]. The work in [11] propose a real-time system for predicting ARTs for the San Francisco fire department, which closely relates to this paper.…”

Section: Discussionmentioning

confidence: 99%

“…To reduce ART, some works propose reallocation of ambulances [5,44], operation demand forecasting [5,7,8,22,45], travel time prediction [12], simulation models [35,46], and EMS response time predictions [11,12]. The work in [11] propose a real-time system for predicting ARTs for the San Francisco fire department, which closely relates to this paper. The authors processed about 4.5 million EMS calls using original location data to predict ART using four ML models, namely linear regression, linear regression with elastic net regularization, decision-tree regression, and random forest.…”

Section: Discussionmentioning

confidence: 99%

“…However, no privacy-preserving experiment was performed because the main objective of their paper was proposing a scalable, MLbased, and real-time system for predicting ART. Besides, we also included weather data that the authors in [11] did not consider in their system, which could help to recognize high ARTs due to bad weather conditions, for example.…”

Section: Discussionmentioning

confidence: 99%

“…One important factor of ART is the location of the intervention [2,3,[10][11][12], e.g., in dense urban areas, the distance may be short, but the travel time may be longer due to traffic congestion. On the other hand, travel distance and travel time may be longer for rural areas.…”

Section: Introductionmentioning

confidence: 99%

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Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.

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Section: Privacy-preserving Art Predictionsupporting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Arcolezi

Cerna

Guyeux

et al. 2021

MCA

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How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

Guyeux

Makhoul

Bahi

2022

Lecture Notes in Networks and Systems

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It has recently been shown that pre-emergency transport, whether performed by firefighters or private ambulances, has a predictive character due to the fact that rescue is directly related to human activity, which is itself predictable. XGBoost has emerged as the best tool to predict the number of interventions by type, but how to design an optimal and operational knowledge base has not been discussed so far. We propose to explain how to make such a base with a content that is both relevant and can be continuously updated, making possible the industrialization of the process, and thus a better operational response of the concerned services. We show that three feature selection tools custom-built for XGBoost are mature enough to allow the optimization of such a database, and a good accuracy in predictions. We also show what these tools can bring in terms of business knowledge, and discuss the organizational and efficiency consequences that such an optimized predictive model could bring.

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Forest fire prevention part I : Prediction and web-based analysis

Bharol

Sai

Bora

et al. 2022

Journal of Information and Optimization Sciences

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Scalable Real-time Prediction and Analysis of San Francisco Fire Department Response Times

Cited by 9 publications

References 14 publications

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

How to Build an Optimal and Operational Knowledge Base to Predict Firefighters’ Interventions

Forest fire prevention part I : Prediction and web-based analysis

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