A Data-Driven Stochastic Optimization Framework for Multi-Agency Emergency Vehicle Allocation and Assignment in Response to Road Incidents
Bibliographic record
Abstract
Urban emergency response systems must operate in highly dynamic environments, where spatial and temporal variability in incident risk and traffic congestion challenge static resource deployment strategies. This research develops a scenario-based dynamic stochastic optimization framework for multi-agency emergency response, integrating police, ambulance, and fire services. The model explicitly captures uncertainty across 16 distinct scenarios defined by combinations of season, day type, time-of-day, and traffic conditions, using real-world historical incident and travel time data. Three core modeling layers are presented: (1) a deterministic baseline with fixed deployment, (2) a stochastic police-only deployment model, and (3) a full multi-agency stochastic model with severity-weighted response objectives. A cost-aware extension further examines resource trade-offs by allowing the number of deployable units to vary within budget constraints. High-resolution travel times derived from INRIX data are fused with incident frequencies and severity classifications from Calgary’s collision database, underpinning the modeling framework. Results demonstrate that scenario-based dynamic deployments substantially reduce severity-weighted response times relative to static plans, particularly during critical periods such as peak periods and winter conditions. Optimal deployments shift meaningfully across timeblocks, aligning police and ambulance locations with evolving risk patterns, while fire services remain fixed due to operational constraints. The cost-benefit analysis reveals diminishing returns from adding emergency units beyond a critical threshold, supporting current fleet levels while highlighting the operational value of flexible deployments. This study contributes to the growing literature on resilient urban emergency planning by bridging stochastic optimization with real-world data, multi-agency coordination, and cost-performance evaluation. Findings underscore the potential for scenario-based planning as a practical middle ground between static resource allocation and fully dynamic dispatching systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".