aiqbench analysis
This paper applies machine learning to ambulance travel time estimation, using historical trip data from Skåne, Sweden, to challenge static lookup tables and the common assumption that ambulances are a fixed percentage faster than passenger cars. Its findings that travel times vary by hour, day, and trip priority, and that commercial navigation misses emergency-specific dynamics, matter for anyone building dispatch or routing tools. The SHAP and permutation-importance analysis is a useful model for interpretability in safety-relevant settings, though the paper's real-world impact will depend on out-of-region validation and on how dispatchers actually use the estimates.
Beyond Heuristics and Commercial Navigation: An AI-Augmented Decision-Support for Ambulance Travel Time Estimation
arXiv:2610.31968v1 Announce Type: new Abstract: Accurate ambulance travel time estimation is critical for emergency medical dispatch, yet existing decision-support practices often rely on static lookup tables or simplified assumptions regarding how ambulance travel time differs from ordinary passenger-car travel. For instance, they assume ambulances simply travel a fixed percentage (e.g., 5%) faster than ordinary passenger cars. However, such assumptions are rarely validated using real ambulance operational data and may fail to capture the effects of trip priority, temporal context, and origin-destination. We address these limitations using historical ambulance trips from the Sk{\aa}ne region in Sweden. Our analysis reveals that ambulance travel times vary substantially by hour, day, and trip priority, and that commercial navigation systems fail to capture emergency-specific dynamics. We contribute a machine learning system to more accurately estimate ambulance travel time. To make the system interpretable, we explored SHapley Additive exPlanations and permutation importance techniques to ensure that our system captures operationally meaningful patterns.
Abstract:Accurate ambulance travel time estimation is critical for emergency medical dispatch, yet existing decision-support practices often rely on static lookup tables or simplified assumptions regarding how ambulance travel time differs from ordinary passenger-car travel. For instance, they assume ambulances simply travel a fixed percentage (e.g., 5%) faster than ordinary passenger cars. However, such assumptions are rarely validated using real ambulance operational data and may fail to capture the effects of trip priority, temporal context, and origin-destination. We address these limitations using historical ambulance trips from the Skåne region in Sweden. Our analysis reveals that ambulance travel times vary substantially by hour, day, and trip priority, and that commercial navigation systems fail to capture emergency-specific dynamics. We contribute a machine learning system to more accurately estimate ambulance travel time. To make the system interpretable, we explored SHapley Additive exPlanations and permutation importance techniques to ensure that our system captures operationally meaningful patterns.
Machine Learning (cs.LG)
arXiv:2610.31968 [cs.LG]
(or arXiv:2610.31968v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.31968 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 245 words