A Dynamic Traffic Awareness System for Urban Driving
Bibliographic record
Abstract
This is the era of Artificial Intelligence (AI) and Internet of Things (IoT). Smart technologies with AI have gained dominance in mobile devices. In recent years, it is paving its way to solve some of the challenging problems in Intelligent Transportation System (ITS). In this paper, we try to answer the following question: How does congestion caused by an unfortunate event on a road segment affect other roads not necessarily close in proximity to the congested road segment? We take advantage of the capabilities of the IoT and propose a dynamic traffic awareness system for urban driving. The system finds all the road points affected by the traffic at some road point at some time, groups them together to predict the effect of traffic on this group of nodes. Grouping the nodes is nothing but clustering since they have similar features, in this case traffic flow. We develop a traffic aware system using IoT technologies and sensors around road points, that dynamically collects and analyzes the traffic flow data to compute the similarity function between road points. We use the concepts from network theory, in particular maximum flow and shortest path algorithms, and a distributed, message passing algorithm to cluster the nodes that is executed continuously to capture up to date information about traffic. We evaluate the system during peak and non-peak hours and against static clustering algorithms and show the performance of our dynamic clustering algorithm.
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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 | 0.000 | 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 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".