A Novel Smart Ambulance System—Algorithm Design, Modeling, and Performance Analysis
Bibliographic record
Abstract
Smart ambulance is a novel system where modern communication, computation, and sensing technologies are employed to revolutionize ambulance and emergency systems. We propose a smart system that aims to minimize the ambulance response time, travel time from patient’s location to the hospital, and the waiting time at the hospital. We utilize the road traffic conditions and hospital loading information (collected in real-time basis) to make optimal decisions (which hospital responds to the patient’s request and which ambulance it sends, which route the ambulance takes to reach the patient, which hospital the ambulance heads to after picking up the patient, and which route it should take to the selected hospital). The first two decisions are used to minimize the response time while the last two decisions are employed to minimize the door-to-needle time. We analyze the performance of the proposed algorithm; both analytically and by simulation for verification. The results showed very good consistency between simulation results and analytical results, which confirms the correctness and accuracy of the analysis. In addition, we compare the performance of our proposed smart algorithm with a previous algorithm that is reported in the literature and that minimizes the drop-off delay. The results confirmed the superiority of our smart algorithm under considered operating conditions and scenarios.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".