Protecting Critical Infrastructure by Blocking Suspicious Vehicle with Probabilistic Route Choice Behavior
Bibliographic record
Abstract
This paper formulates an emergency response strategy for the efficient blocking of a suspicious vehicle that may have been hijacked by terrorists as a potential weapon of terrorism attack in urban area. Two primary cases are considered. The first case aims to minimize the resources required to thoroughly block a suspicious vehicle whereas the second case aims to maximize the effectiveness of the blocking scheme given limited resources. In the first case, an enhanced cutset algorithm is devised to locate all the junctions/links to be blocked while reliability analysis is employed in the second case to obtain an optimal blocking scheme. Several criteria relating to the route choice behavior of the suspicious vehicle, including travel time, optimal path and road configuration, are considered. Analytical hierarchy process is used to derive the probability matrix for probabilistic analysis. Two approaches for calculating junction selection probability, namely candidate route search approach and Monte Carlo method, are provided and compared. Optimal dispatching schemes are generated based on the shortest path algorithm. A GIS-based intelligent emergency response prototype system is then developed by incorporating the proposed algorithms into widely available GIS software (ArcGIS). The system is tested using the road network of Calgary. The case study shows that the proposed system is able to generate an effective strategy for blocking a suspicious vehicle from critical infrastructure and automatically derive minimal travel time routes for emergency response units.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".