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arxiv: 2604.24394 · v1 · submitted 2026-04-27 · 📡 eess.SY · cs.SY

A Realistic Discrete Event Simulation model for Ambulance Location and Deployment within a regional Emergency Medical Service

Alberto De Santis , Stefania Iannazzo , Fabio Ingravalle , Stefano Lucidi , Massimo Maurici , Giulia Riccardi , Massimo Roma , Antonio Vinci This is my paper

Pith reviewed 2026-05-08 01:57 UTC · model grok-4.3

classification 📡 eess.SY cs.SY
keywords discrete event simulationambulance deploymentemergency medical servicesstochastic modelingregional EMSambulance locationsimulation modelcase study
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The pith

A general discrete event simulation model integrates multi-source data to accurately reproduce the stochastic behavior of regional ambulance emergency systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a Discrete Event Simulation model for ambulance location and deployment in regional Emergency Medical Services. It aims to show that integrating information from various sources and modeling key stochastic processes allows the simulation to closely match real-world operations. This realistic reproduction supports detailed analysis of system performance under different scenarios. The approach is demonstrated through a case study in a mixed urban-rural area of the Lazio region in Italy, where it can guide improvements in response effectiveness and speed.

Core claim

The proposed general Discrete Event Simulation model captures the stochastic behaviour and workflow of regional ambulance emergency systems by incorporating and integrating information collected from different sources, reproducing very accurately the operation of the ambulance system and thereby allowing a more comprehensive and realistic analysis, as illustrated by scenario analyses in the Lazio region case study.

What carries the argument

Discrete Event Simulation model that integrates multi-source data and applies probability distributions to stochastic events such as emergency calls, travel times, and service durations.

If this is right

  • Managers can use the model to conduct scenario analyses that evaluate different ambulance home base locations and deployment strategies.
  • The model enables improvements in the effectiveness and quickness of the entire regional EMS system.
  • The general model applies to territories that include both medium-size cities and sparsely populated areas.
  • It provides a more comprehensive analysis than approaches that do not combine data from multiple sources.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same data-integration approach could be applied to EMS systems in other regions by substituting local data sources.
  • Scenario outputs could support decisions on whether to add bases or adjust staffing in urban versus rural zones.
  • The model structure allows testing of policy changes like new response protocols before implementation.

Load-bearing premise

The integration of multi-source data and the chosen probability distributions for stochastic events sufficiently capture the real ambulance system's behavior without significant unmodeled discrepancies or biases.

What would settle it

A comparison showing large mismatches between the model's simulated response times or utilization rates and the actual historical performance data from the ARES 118 system in the Lazio region would indicate the model does not reproduce the operation accurately.

Figures

Figures reproduced from arXiv: 2604.24394 by Alberto De Santis, Antonio Vinci, Fabio Ingravalle, Giulia Riccardi, Massimo Maurici, Massimo Roma, Stefania Iannazzo, Stefano Lucidi.

Figure 1
Figure 1. Figure 1: Flowchart of the ambulance emergency system processes view at source ↗
Figure 2
Figure 2. Figure 2: Map of ambulance bases • selected ED; • timestamps of ambulance departure from the scene, arrival at ED, beginning/end of ambu￾lance ramping (if any), mission end; • outcome. All these data have been used for an accurate input analysis. In particular, by calculating the time differences between pairs of timestamps, we determined all the needed service times indi￾cated in Section 3.4.3. Once these service t… view at source ↗
Figure 3
Figure 3. Figure 3: Map of the Lazio Region and the set of potential destination EDs (the numbers in the red circles are in view at source ↗
Figure 4
Figure 4. Figure 4: Total number of calls the biggest city in the area (Rieti), while in the remaining zones, rarely populated areas associated to a low number (or even no call) are highlighted. 4.4.1. Demand points To define the demand points, i.e., the locations of the simulated emergency calls, Rieti and its province was partitioned into 446 call squares, each with an area of approximately 10 km2 . For each square, we sele… view at source ↗
Figure 5
Figure 5. Figure 5: Demand points 21 view at source ↗
Figure 6
Figure 6. Figure 6: Comparison between historical data (in blue) and simulated output (in red) in terms of view at source ↗
Figure 7
Figure 7. Figure 7: Comparison between historical data (in blue) and simulated output (in red) in terms of view at source ↗
Figure 8
Figure 8. Figure 8: Comparison between percentage of calls assigned at each base from real data (blue) and from the simulation (red). 4.6. Design of Experiments and the “as-is” status Once the DES model has been verified and validated, we performed an accurate design of experiments, so that the model output can be used as representative of the current “as-is” status. We confirm the simulation parameters adopted in the validat… view at source ↗
Figure 9
Figure 9. Figure 9: Map of ambulance bases and Fire Stations (the red triangles denoted by VVFF) view at source ↗
Figure 10
Figure 10. Figure 10: Coverage for 20 minutes threshold value for view at source ↗
Figure 11
Figure 11. Figure 11: Coverage for 20 minutes threshold value for view at source ↗
read the original abstract

The objective of Emergency Medical Services (EMSs) is to promptly respond to calls from citizens for first aid, providing pre-hospital care and, if necessary, to transfer patients to an appropriate Emergency Department (ED) by ambulance. The efficiency of such a system strongly depends on the deployment of ambulance home bases, i.e., locations where ambulances and their crews are strategically positioned, ready to respond to emergency calls. This paper presents a general Discrete Event Simulation (DES) model designed to capture the stochastic behaviour and workflow of regional ambulance emergency systems. The proposed model incorporates and integrates information collected from different sources, reproducing very accurately the operation of the ambulance system, thus allowing a more comprehensive and realistic analysis. To show the applicability and reliability of the proposed general model, a case study provided by the Azienda Regionale Emergenza Sanitaria - ARES 118 (an Italian Regional Emergency Medical Services Authority - ARES~118}) is presented. It concerns a territory within the Lazio region of Italy, including a medium-size city along with sparsely populated areas. The reported results about scenario analyses highlight how the model we propose can be fruitfully used by the managers to improve effectiveness and quickness of the entire regional EMS system.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper presents a general Discrete Event Simulation (DES) model for regional ambulance emergency systems that integrates multi-source data to capture stochastic behaviors including call arrivals, travel times, and service durations. It claims the model reproduces real EMS operations 'very accurately' and demonstrates applicability through scenario analyses in a Lazio region case study provided by ARES 118.

Significance. A validated DES model of this type could enable EMS managers to evaluate ambulance deployment and base-location changes in a risk-free environment, supporting data-driven improvements to response times and coverage. The explicit integration of real operational data sources is a positive feature that distinguishes it from purely synthetic models.

major comments (2)
  1. [Abstract and §1] Abstract and §1 (Introduction): the central claim that the model 'reproducing very accurately the operation of the ambulance system' is unsupported by any reported quantitative validation. No goodness-of-fit statistics, Kolmogorov-Smirnov or chi-squared tests for the chosen probability distributions, or error metrics (e.g., mean absolute percentage error on response times or utilization) comparing simulated versus observed KPIs are provided for the Lazio case study.
  2. [§4 and §5] §4 (Case Study) and §5 (Results): the scenario analyses rest on the unverified assumption that the fitted distributions and multi-source integration capture all relevant stochasticity and dependencies (time-of-day effects, spatial correlations, crew scheduling). No sensitivity analysis or robustness checks are described, so it is impossible to bound the discrepancy between simulated and real-system behavior.
minor comments (2)
  1. [Figures and Tables] Figure captions and table headings could more explicitly state the number of replications and warm-up period used in the DES runs.
  2. [Model Description] The description of how travel-time and service-duration distributions were selected (e.g., empirical histograms versus parametric fits) is terse and would benefit from an additional paragraph or appendix table.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We are grateful to the referee for the constructive and detailed feedback. The comments correctly identify opportunities to strengthen the quantitative support for our claims and the robustness of the scenario analyses. We address each major comment below and will incorporate the suggested revisions in the next version of the manuscript.

read point-by-point responses

  1. Referee: [Abstract and §1] Abstract and §1 (Introduction): the central claim that the model 'reproducing very accurately the operation of the ambulance system' is unsupported by any reported quantitative validation. No goodness-of-fit statistics, Kolmogorov-Smirnov or chi-squared tests for the chosen probability distributions, or error metrics (e.g., mean absolute percentage error on response times or utilization) comparing simulated versus observed KPIs are provided for the Lazio case study.

    Authors: We thank the referee for highlighting this point. The manuscript describes the integration of real operational data and presents case-study outputs that align with observed EMS behavior, but we acknowledge that the claim of reproducing operations 'very accurately' would be better supported by explicit quantitative metrics. In the revised manuscript we will add Kolmogorov-Smirnov goodness-of-fit tests for the fitted distributions together with mean absolute percentage error (MAPE) and other error metrics comparing simulated and observed key performance indicators such as response times and ambulance utilization. revision: yes

  2. Referee: [§4 and §5] §4 (Case Study) and §5 (Results): the scenario analyses rest on the unverified assumption that the fitted distributions and multi-source integration capture all relevant stochasticity and dependencies (time-of-day effects, spatial correlations, crew scheduling). No sensitivity analysis or robustness checks are described, so it is impossible to bound the discrepancy between simulated and real-system behavior.

    Authors: We agree that additional robustness checks are needed to bound uncertainty in the scenario results. In the revised manuscript we will include a dedicated sensitivity-analysis subsection that systematically varies key parameters (call-arrival rates by time of day, travel-time distributions, and service durations) and reports the resulting variation in the scenario outcomes, thereby demonstrating the stability of the findings. revision: yes

Circularity Check

0 steps flagged

No circularity: model built from external multi-source data and standard simulation methods

full rationale

The paper constructs a DES model by integrating information from different external sources (including the Lazio regional EMS authority) and selecting probability distributions for calls, travel times, and service durations. The claim of reproducing the system 'very accurately' follows directly from this data-driven parameterization and standard DES workflow rather than any self-referential reduction. No equations or steps equate outputs to inputs by construction, no fitted parameters are relabeled as independent predictions, and no load-bearing premises rest on self-citations or uniqueness theorems from the same authors. The case-study scenario analyses therefore remain independent of the model's own fitted values.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The model rests on standard assumptions of discrete event simulation for stochastic processes in EMS; no free parameters, invented entities, or non-standard axioms are explicitly described in the abstract.

axioms (1)
  • domain assumption Stochastic elements of EMS operations (call arrivals, travel times, service durations) can be adequately represented by probability distributions within a discrete event framework.
    Invoked implicitly by the choice of DES modeling approach for capturing system behavior.

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