Statistical Methods for Ambulance Fleet Management

Duration: 34 mins 11 secs

Share this media item:

Embed this media item:

<iframe width="_width_" height="_height_" src="https://sms.cam.ac.uk/media/1704645/embed" frameborder="0" scrolling="no" allowfullscreen></iframe>

Choose size:

About this item

Available Formats

About this item

Description:	Woodard, DB (Cornell University) Wednesday 23 April 2014, 14:20-14:55


Created:	2014-04-25 13:50
Collection:	Advanced Monte Carlo Methods for Complex Inference Problems
Publisher:	Isaac Newton Institute
Copyright:	Woodard, DB
Language:	eng (English)


Abstract:	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

Abstract:

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

Available Formats

Format	Quality	Bitrate	Size
MPEG-4 Video	640x360	1.94 Mbits/sec	497.86 MB	View	Download
WebM	640x360	483.38 kbits/sec	121.08 MB	View	Download
iPod Video	480x270	522.08 kbits/sec	130.71 MB	View	Download
MP3	44100 Hz	249.78 kbits/sec	62.60 MB	Listen	Download
Auto *	(Allows browser to choose a format it supports)