Deep survival analysis model for incident clearance time prediction
Bibliographic record
Abstract
Incident clearance time prediction is a key task for traffic incident management. A hazard-based duration model is a prevalent approach for predicting and analyzing the incident clearance time, which considers “duration dependence” of which the probability of an incident clearance ending depends on the time the clearance has lasted. However, the performance is limited due to its model assumptions for clearance time distribution, linear relationship, and the time-invariant effects of influential factors. This study proposes a deep survival analysis model that relaxes the assumptions of the hazard-based duration model while considering duration dependence based on a multi-task deep neural network (MTDNN). The MTDNN can consider the duration dependence when predicting incident clearance time by simultaneously estimating the survival function based on the concept of multi-task learning. The effects of influential factors on the prediction of MTDNN are also investigated using a post-analysis method. The proposed model is evaluated by its predictive performance and the estimated effects of influential factors using the freeway incident data collected in Korea from 2014 to 2019. These evaluations show that, compared to the baseline hazard-based duration model, the proposed MTDNN improves the predictive performance by 29.7% in terms of mean absolute percent error, and outperforms all statistical and machine learning models for both incident clearance time prediction and the survival function estimation. The analysis of the influential factors reveals that the hazard-based duration model and MTDNN had major influencing factors in common, but the impact of some factors is considerably different.
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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 | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".