ReFOCUS: A hybrid fog-cloud based intelligent traffic re-routing system
Bibliographic record
Abstract
The continuous rapid growth of vehicles and nonexpansion of existing infrastructure in large cities due to space constraints and heavy costs leads to increase the traffic congestion, which causes to increase the travel time of the drivers, fuel consumption, and emissions. In order to overcome the above-mentioned issues, this paper presents a novel method for dynamic rerouting system based on a hybrid FOG-Cloud intelligent control system called ReFOCUS, which is able to dynamically compute the best path for drivers those are in or will be in the congested area, based on the current traffic density of different regions with considering the road future congestion status. The system is implemented in a FOG-Cloud computing environment that can use traffic data of the roads and provide the necessary information in real-time to drivers where significantly decrease the data exchange compared to other cloud-based systems. Mathematical modeling and algorithm for the ReFOCUS has been proposed in this paper and a primary simulation has been done to evaluate the efficiency of the method. The results of the simulation illustrate that the proposed novel method can decrease the average travel time 65%, CO2 emission 36%, and fuel consumption 36%.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".