Implementing Dynamic Travel Time Calculation in EMS Simulations : Impacts on Prehospital Stroke Care and Transportation
(2025) 16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024 256. p.781-788- Abstract
Preparing travel time data can be a time-consuming process, which greatly limits the flexibility of transport simulation models. In the current paper, we present an approach to integrate a routing engine locally in an existing modeling framework, hence enabling to dynamically calculate travel times in the constructed emergency medical services (EMS) simulation models. This integration eliminates the need for the pre-calculation typically required to prepare travel time data. Using the extended framework, we developed an EMS simulation model for stroke patients, which we applied in a scenario study to southern Sweden. This allowed us to evaluate the potential benefits of using dynamic travel time calculations in prehospital stroke care.... (More)
Preparing travel time data can be a time-consuming process, which greatly limits the flexibility of transport simulation models. In the current paper, we present an approach to integrate a routing engine locally in an existing modeling framework, hence enabling to dynamically calculate travel times in the constructed emergency medical services (EMS) simulation models. This integration eliminates the need for the pre-calculation typically required to prepare travel time data. Using the extended framework, we developed an EMS simulation model for stroke patients, which we applied in a scenario study to southern Sweden. This allowed us to evaluate the potential benefits of using dynamic travel time calculations in prehospital stroke care. The experimental results, supported by comparisons with pre-calculated travel times, confirm the effectiveness of our approach in integrating dynamic travel time calculations into the framework. Moreover, the results of our evaluation indicate that including this functionality in simulation models can provide more realistic results. Finally, our approach for local implementation of dynamic travel time calculations is faster and less restricted compared to using online services.
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- author
- Mahdiraji, Saeid Amouzad ; Juninger, Marcus ; Narvell, Nicholas ; Holmgren, Johan ; Mihailescu, Radu Casian and Petersson, Jesper LU
- organization
- publishing date
- 2025
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Dynamic travel time, EMS, Framework, Simulation model, Travel data calculation
- host publication
- Procedia Computer Science
- volume
- 256
- pages
- 8 pages
- publisher
- Elsevier
- conference name
- 16th International Conference on ENTERprise Information Systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information Systems and Technologies, HCist 2024
- conference location
- Hybrid, Madeira, Portugal
- conference dates
- 2024-11-13 - 2024-11-15
- external identifiers
-
- scopus:105001922863
- DOI
- 10.1016/j.procs.2025.02.179
- language
- English
- LU publication?
- yes
- id
- 50c94dab-2845-4a5a-b35d-6783c9dbb00c
- date added to LUP
- 2025-09-03 10:44:55
- date last changed
- 2025-09-03 10:46:02
@inproceedings{50c94dab-2845-4a5a-b35d-6783c9dbb00c,
abstract = {{<p>Preparing travel time data can be a time-consuming process, which greatly limits the flexibility of transport simulation models. In the current paper, we present an approach to integrate a routing engine locally in an existing modeling framework, hence enabling to dynamically calculate travel times in the constructed emergency medical services (EMS) simulation models. This integration eliminates the need for the pre-calculation typically required to prepare travel time data. Using the extended framework, we developed an EMS simulation model for stroke patients, which we applied in a scenario study to southern Sweden. This allowed us to evaluate the potential benefits of using dynamic travel time calculations in prehospital stroke care. The experimental results, supported by comparisons with pre-calculated travel times, confirm the effectiveness of our approach in integrating dynamic travel time calculations into the framework. Moreover, the results of our evaluation indicate that including this functionality in simulation models can provide more realistic results. Finally, our approach for local implementation of dynamic travel time calculations is faster and less restricted compared to using online services.</p>}},
author = {{Mahdiraji, Saeid Amouzad and Juninger, Marcus and Narvell, Nicholas and Holmgren, Johan and Mihailescu, Radu Casian and Petersson, Jesper}},
booktitle = {{Procedia Computer Science}},
keywords = {{Dynamic travel time; EMS; Framework; Simulation model; Travel data calculation}},
language = {{eng}},
pages = {{781--788}},
publisher = {{Elsevier}},
title = {{Implementing Dynamic Travel Time Calculation in EMS Simulations : Impacts on Prehospital Stroke Care and Transportation}},
url = {{http://dx.doi.org/10.1016/j.procs.2025.02.179}},
doi = {{10.1016/j.procs.2025.02.179}},
volume = {{256}},
year = {{2025}},
}