For Travel Time Estimation for Ambulances Using Bayesian Data Augmentation
نویسندگان
چکیده
There are several constants and hyperparameters to be specified in the Bayesian model. To set the GPS position error covariance matrix Σ, we calculate the minimum distance from each GPS location in the data to the nearest arc. Assuming that the error is radially symmetric, that the vehicle was on the nearest arc when it generated the GPS point, and approximating that arc locally by a straight line, this minimum distance should equal the absolute value of one component of the 2-dimensional error, i.e. the absolute value of a random variable E 1 ∼ N (0, σ 2), where Σ =
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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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Travel Time Estimation for Ambulances using Bayesian Data Augmentation
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 ...
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