Dynamic Nonrecurrent Congestion Event Detection and Tracking Method With DBSCAN on Speed Watersheds
Bibliographic record
Abstract
Nonrecurrent congestion (NRC) events caused by incidents bring unexpected delays and affect normal traffic operations. Imprecise NRC event detection methods can trigger false alarms and repetitive incident alerts for the same congestion event. The speed watershed from the historical profile based on DBSCAN can provide a reference for identifying NRC. This paper proposes a DBSCAN‐based dynamic NRC tracking (DyNRTrac) algorithm to detect and track NRC events. By comparing real‐time spatial–temporal patterns of the speed contour diagram against the historical speed contour diagram along a corridor, this method effectively distinguishes NRC events from regular traffic patterns. The proposed algorithm applies the Rauch–Tung–Striebel smoother for speed noise reduction and establishes a historical congestion profile for each recurrent congestion event within a corridor by each day of the week and season. A new event‐profile–based 3D speed volume comparison method is proposed to detect NRC events that do not significantly overlap with recurrent congestions in the historical profile. Finally, a bilevel congestion confirmation process is introduced for NRC persistency checking and filtering. The proposed algorithm was evaluated by using field travel time data and with the New Jersey Department of Transportation OpenReach incident database. Overall, the proposed model shows up to 88.3% detection rate for NRC that can match the incident in the database, and it shows superior detection rates on NRC events at a similar false alarm rate level when compared with three prior models over the same datasets. Furthermore, a detailed spatial–temporal map analysis is provided to show the capability of the proposed method in distinguishing NRC and RC and identifying nonaccidental NRC events, providing its potential for traffic operation management systems to assist traffic operators to be alerted about NRC events.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".