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Statistical Methods for Ambulance Fleet Management

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Advanced Monte Carlo Methods for Complex Inference Problems

We introduce statistical methods to address two problems arising in the management of ambulance fleets: (1) predicting the distribution of ambulance driving time between arbitrary start and end locations in a road network; and (2) space-time forecasting of ambulance demand. These predictions are critical for deciding how many ambulances should be deployed at a given time and where they should be stationed, which ambulance should be dispatched to an emergency, and whether and how to schedule ambulances for non-urgent patient transfers. We demonstrate the accuracy and operational impact of our methods using ambulance data from Toronto Emergency Medical Services.

For driving time prediction the relevant data are Global Positioning System (GPS) recordings from historical lights-and-sirens ambulance trips. Challenges include the typically large size of the road network and dataset (70,000 network links and 160,000 historical trips for Toronto); the lack of trips in the historical data that follow precisely the route of interest; and the strong temporal, weather, and other effects on driving time. We introduce a model of the travel time at the trip level, as well as a computationally efficient procedure for two-stage estimation in that model. For space-time forecasting of demand we develop integer time-series factor models and spatio-temporal mixture models, which capture the complex weekly and daily patterns in demand as well as changes in the spatial demand density over time.

Related Links: http://people.orie.cornell.edu/woodard/publications.html – Links to Woodard’s publications on ambulance fleet management

This talk is part of the Isaac Newton Institute Seminar Series series.

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