Dynamic accessibility analysis in location-based service using an incremental parallel algorithm
Bibliographic record
Abstract
Accessibility analysis usually requires finding the closest facility within a certain category—for example, the nearest hotel, hospital, or gas station. Along with the development of location-based services, users also wish to find the optimal route to the closest facility, based on network distance. Furthermore, the best route should be adjusted in a dynamic traffic environment. Most traditional methods solve the nearest-neighbor (facility) problem using Euclidian distance or network distance without consideration of dynamic traffic conditions. In this paper we propose a novel incremental parallel Dijkstra's algorithm, IP-Dijkstra, to construct and maintain a dynamic network Voronoi diagram for time-dependent traffic networks. The experimental results demonstrate that the proposed IP-Dijkstra's algorithm considerably outperforms the traditional methods, which recompute the shortest path from scratch without utilization of the previous search results. Consequently, this algorithm is capable of accommodating a large number of mobile clients in search of their respective nearest facilities and the routes to reach such facilities in a dynamic traffic environment, thereby facilitating real-time accessibility analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".