Open Access
June 2013 Travel time estimation for ambulances using Bayesian data augmentation
Bradford S. Westgate, Dawn B. Woodard, David S. Matteson, Shane G. Henderson
Ann. Appl. Stat. 7(2): 1139-1161 (June 2013). DOI: 10.1214/13-AOAS626


We introduce a Bayesian model for estimating the distribution of ambulance travel times on each road segment in a city, using Global Positioning System (GPS) data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. We simultaneously estimate the paths, travel times, and parameters of each road segment travel time distribution using Bayesian data augmentation. To draw ambulance path samples, we use a novel reversible jump Metropolis–Hastings step. We also introduce two simpler estimation methods based on GPS speed data.

We compare these methods to a recently published travel time estimation method, using simulated data and data from Toronto EMS. In both cases, out-of-sample point and interval estimates of ambulance trip times from the Bayesian method outperform estimates from the alternative methods. We also construct probability-of-coverage maps for ambulances. The Bayesian method gives more realistic maps than the recently published method. Finally, path estimates from the Bayesian method interpolate well between sparsely recorded GPS readings and are robust to GPS location errors.


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Bradford S. Westgate. Dawn B. Woodard. David S. Matteson. Shane G. Henderson. "Travel time estimation for ambulances using Bayesian data augmentation." Ann. Appl. Stat. 7 (2) 1139 - 1161, June 2013.


Published: June 2013
First available in Project Euclid: 27 June 2013

zbMATH: 1288.62044
MathSciNet: MR3113504
Digital Object Identifier: 10.1214/13-AOAS626

Keywords: emergency medical services , Global Positioning System , map-matching , Markov chain Monte Carlo , Reversible jump

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 2 • June 2013
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