On the Modelling of Emergency Ambulance Trips: The Case of the Žilina Region in Slovakia

Abstract

The efficient operation of emergency medical services is critical for any society. Typically, optimisation and simulation models support decisions on emergency ambulance stations’ locations and ambulance management strategies. Essential inputs for such models are the spatiotemporal characteristics of ambulance trips. Access to data on the movements of ambulances is limited, and therefore modelling efforts often rely on assumptions (e.g., the Euclidean distance is used as a surrogate of the ambulance travel time; the closest available ambulance is dispatched to a call; or the travel time estimates, offered by application programming interfaces for ordinary vehicles, are applied to ambulances). These simplifying assumptions are often based on incomplete data or common sense without being fully supported by the evidence. Thus, data-driven research to model ambulance trips is required. We investigated a unique dataset of global positioning system-based measurements collected from seventeen emergency ambulances over three years. We enriched the data by exploring external sources and designed a rule-based procedure to extract ambulance trips for emergency cases. Trips were split into training and test sets. The training set was used to develop a series of statistical models that capture the spatiotemporal characteristics of emergency ambulance trips. The models were used to generate synthetic ambulance trips, and those were compared with the test set to decide which models are the most suitable and to evaluate degrees to which they fit the statistical properties of real-world trips. As confirmed by the low values of the Kullback–Leibler divergence (0.004–0.229) and by the Kolmogorov–Smirnov test at the significance level of 0.05, we found a very good fit between the probability distributions of spatiotemporal properties of synthetic and real trips. A reasonable modelling choice is a model where the exponential dependency on the population density is used to locate emergency cases, emergency cases are allocated to hospitals following empirical probabilities, and ambulances are routed using the fastest paths. The models we developed can be used in optimisations and simulations to improve their validity.

Reorganization of an Emergency Medical System in a Mixed Urban-Rural Area

“…In this research, we have employed rather old estimates from the study [11]. However, we hope that in the future we will be able to obtain up-to-date GPS traces of ambulance trips, and to derive more exact estimates, such as in [24].…”

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

“…While these methods can reduce model complexity, they cannot fully capture the vehicle dynamics and features of EMVs and time-dependent features of road traffic flows. To address these limitations, some studies modeled the vehicle dynamics of EMVs and the variance of traffic conditions by proposing comprehensive formulas, including a segmentation function-based model [29], semi-parametric prediction model [30], regression models [31,32], Bureau of Public Road function (BPR function) [33][34][35], and Bayesian neural network [36].…”