Travel Time Estimation for Ambulances using Bayesian Data Augmentation
نویسندگان
چکیده
Estimates of ambulance travel times on road networks are critical for effective ambulance base placement and real-time ambulance dispatching. We introduce new methods for estimating the distribution of travel times on each road segment in a city, using Global Positioning System (GPS) data recorded during ambulance trips. Our preferred method uses a Bayesian model of the ambulance trips and GPS data. Due to sparseness and error in the GPS data, the exact ambulance paths and travel times on each road segment are unknown. To estimate the travel time distributions using the GPS data, we must also estimate each ambulance path. This is known as the map-matching problem. We simultaneously estimate the unknown paths, travel times, and the parameters of each road segment travel time distribution using Bayesian data augmentation. We also introduce two alternative estimation methods based on GPS speed data that are simple to implement in practice. We compare the predictive accuracy of the three methods to a recently-published 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, which are essential for ambulance providers. The Bayesian method gives more reasonable maps than the competing method. Finally, map-matching estimates from the Bayesian method interpolate well between sparsely recorded GPS readings and are robust to GPS location errors.
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Time Estimation for Ambulances Using Bayesian Data Augmentation
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 distributio...
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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 distributio...
متن کاملSupplementary Material for Travel Time Estimation for Ambulances Using Bayesian Data Augmentation
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For Travel Time Estimation for Ambulances Using Bayesian Data Augmentation
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