This week's Statistics Seminar Speaker will be Dawn Woodard from Cornell's Department of Operations Research and Information Engineering (ORIE).
Statistics for Ambulance Fleet Management
We introduce statistical methods to address two estimation problems arising in the management of ambulance fleets: (1) predicting the distribution of ambulance travel 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 travel time estimation 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 uncertainty regarding the route taken in the historical trips (due to sparsity of the GPS recordings). We introduce a model of the travel time at the network link level, assuming independence across links, as well as a model at the trip level, and compare them. We also introduce methods for both joint estimation of the travel time parameters and unknown historical routes, and more computationally efficient two-stage estimation. 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.
Refreshments will be served after the seminar in 1181 Comstock Hall.