A Machine Learning Approach to Traffic Congestion Hotspot Identification and Prediction
Bibliographic record
Abstract
Travel-time delays due to recurring congestion cause productivity loss, increase the likelihood of accidents, and lead to environmental pollution due to greenhouse gas emissions. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most newer vehicles. While these technologies can help reduce the likelihood of traffic-related accidents, they do little to reduce recurring congestion in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion also enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical, which limits their practical applicability. In this paper, we develop a Machine Learning (ML) approach that integrates geospatial data with artificial neural networks to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The framework is validated using live loop detector data (speed and volume) from Maryland freeways, particularly I-495 and I-95. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".