Empirical Analysis of Ambulance Travel Times: The Case of Calgary Emergency Medical Services

Abstract

Using administrative data for high-priority calls in Calgary, Alberta, we estimate how ambulance travel times depend on distance. We find that a logarithmic transformation produces symmetric travel-time distributions with heavier tails than those of a normal distribution. Guided by nonparametric estimates of the median and coefficient of variation, we demonstrate that a previously proposed model for mean fire engine travel times is a valid and useful description of median ambulance travel times. We propose a new specification for the coefficient of variation, which decreases with distance. We illustrate how the resulting travel-time distribution model can be used to create probability-of-coverage maps for diagnosis and improvement of system performance.ambulance service, probability-of-coverage map, nonparametric, travel

An efficient heuristic for real-time ambulance redeployment

“…However, we want to point out that emergency services do not always experience the impact of the time of day on their response velocities. For example, empirical evidence shows only a minor impact for fire fighters in New York [10] and ambulances in Calgary [2]. Furthermore, even if one is certain that the time of day is relevant for the response velocities, the task remains to estimate the different velocities accurately.…”

Hypercube Queuing Models in Emergency Service Systems: A State-of-the-Art Review

“…Step 3) Calculate h ij f and normalize these probabilities using Budge et al [41] used this algorithm to find the relationship between travel time and distance. They concluded that a logarithmic transformation makes symmetric travel-time distribution.…”

“…Additionally, environmental measures can be regarded.  Due to the significance of travel time as a portion of service time, many studies have tried to analyze this time more accurately [12,18,30,41]; however, travel time is always evaluated as a part of the service time. Therefore, it can be very helpful to define a measure to examine the travel time solely.…”

Efficient Estimation of an Additive Quantile Regression Model

“…For instance, reaching 90% of urgent urban calls in 9 minutes is a common target in North America and the National Health Service in the U.K. sets targets of 75% in 8 minutes and 95% in 14 minutes for urgent urban calls (Budge, Ingolfsson and Zerom, 2008). Note that these performance targets correspond to quantiles of the response time distribution.…”

“…In 2003, Calgary EMS responded to n = 7457 high priority calls that involves heart problems, breathing problems, traffic accident, building fire, unconsciousness, house fire, fall, convulsions and seizures, hemorrhage and lacerations, traumatic injuries, and unknown problem. Budge et al (2008) assume that the conditional median of travel time to be additive,…”