Reactive emergency vehicles dispatching based real-time information dissemination
Bibliographic record
Abstract
Emergency situations require accurate and timely decisions in order to reduce delay and the additional impact of incidents on human lives and civil properties. However, decision-making in urban emergency situations is a challenging task due to the number of variables influencing the process and the unpredictable change of some of them. This process characteristic requires that decision-makers monitor and adjust their decisions almost permanently. Therefore, information availability and exchange are essential to improve the decision-making process. Despite sophisticated optimization contributions in dynamic emergency vehicle dispatching problem, few of them have considered their integration in Intelligent Transportation Systems (ITS). In this context, dynamic events and non-recurrent congestion could be better addressed. In this paper, we consider the dynamic emergency vehicles dispatching problem using realtime information (DEVDP-RT). We propose a reactive approach for vehicle dispatching based on a context-aware and reconfigurable architecture. Our approach is intended to help allocate emergency vehicles to urban emergencies and adjust their routes according to upcoming changes and unforeseeable events. Simulation results highlight the benefits of the proposed dispatching model in improving the emergency response time and reducing delay.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".