ReFOCUS+: Multi-Layers Real-Time Intelligent Route Guidance System With Congestion Detection and Avoidance
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Bibliographic record
Abstract
Due to random nature of traffic and unpredictability of human behaviors, one of challenging problems in transportation engineering is traffic congestion which has a direct impact on the economy and environment with the increase in traveling time, fuel consumption and emissions. One of approaches to reduce traffic congestions is the advance Route Guidance Systems (RGSs) which can propose alternative optimal routes for vehicles, which are in or will be entering congested roads or areas. Advanced RGSs, usually employ real-time and predicted traffic information of the roads to find the best possible route for vehicles in a way that total traffic congestions will be reduced. In this paper, The ReFOCUS+, a dynamic semi-distributed, multi-layer, and Fog-Cloud based advance route guidance system architecture has been introduced. The ReFOCUS+ architecture, employ Road Side Units (RSUs), to calculate different traffic-related factors such as current and predicted road congestions, area congestions, traveling time, etc. Then, the ReFOCUS+ uses traffic factors to proposes a novel method to detect congested roads in an area and, apply re-routing to vehicles to ease the traffic congestion within each area using a multi-metric fitness function, called Road Weight Measurement (RWM). To evaluate the performance of ReFOCUS+, a new open-source Python-based program has been developed which is able to connect to SUMO traffic simulator and control the simulation. The simulation results demonstrate that ReFOCUS+ outperforms existing solutions and improve traveling time, fuel consumption and gas emissions. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The developed program and software in this paper available at https://www.github.com/hamednoori/ReFOCUS+
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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 | 0.000 | 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 it