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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".