Beyond Heuristics and Commercial Navigation: An AI-Augmented Decision-Support for Ambulance Travel Time Estimation
Authors: Muhammad Adil Abid, Johan Holmgren, Fabian Lorig, Jesper Petersson
Organizations: Department of Computer Science and Media Technology, Malmö University, 21119 Malmö, Sweden · Sustainable Digitalisation Research Centre, Malmö University, Sweden · Department of Health Care Management, Region Skåne, 21428 Malmö, Sweden · Department of Neurology, Lund University, 22242 Malmö, Sweden
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.
Figures & tables
Figure 1: Mean ambulance travel time by (a) hour of the day, (b) day of the week, and (c) priority level.
Figure 2: A high-level overview of the proposed system’s methodological workflow.
Model
MAPE (%) ↓
MAE (min) ↓
RMSE (min) ↓
R2↑
Polynomial LR
18.73
5.24
10.37
0.65
CatBoost native
13.20
4.84
10.72
0.63
CatBoost one-hot
13.90
4.83
10.50
0.64
Random Forest
19.76
5.79
11.18
0.60
ANN
12.51
4.72
10.15
0.67
Table 1: Comparisons of the machine learning models
MAE (min)
Proposed System (Relative Error Reduction)
Trip Priority
Google Maps
5 % Faster Assumption
Proposed System
vs. Google Maps
vs. 5 % Assumption
P 1 (Emergency)
26.83
24.53
5.58
79.2 %
77.3 %
P 2 (Urgent)
19.50
18.59
4.55
76.7 %
75.5 %
P 3 (Routine)
10.11
9.75
4.78
52.7 %
51.0 %
Table 2: Mean absolute error (MAE) of travel time estimates by priority.
Figure 3: Benchmark comparison of mean ambulance travel time by priority level.
Figure 4: Explainability system analysis: (a) permutation feature importance based on MAE increase after block shuffling and (b) SHAP global feature importance and value distribution.
Department of Computer Science and Media Technology, Malmö University, 21119 Malmö, Sweden · Sustainable Digitalisation Research Centre, Malmö University, Sweden · Department of Health Care Management, Region Skåne, 21428 Malmö, Sweden +1