Use of Support Vector Machine Models for Real-Time Prediction of Crash Risk on Urban Expressways
Bibliographic record
Abstract
This study adopted a novel methodology—a support vector machine (SVM) with two penalty parameters—for the evaluation of real-time crash risk on urban expressway segments by using dual-loop detector data. The purpose of this study was to develop a model that can effectively identify traffic conditions prone to crashes and support implementation of proactive traffic safety management. On the basis of crash data and the corresponding detector data collected on expressways of Shanghai, China, different combinations of dual-loop detector data and time segments before crashes were used to develop the optimal crash risk estimation model by SVM. The transferability of the SVM model was assessed by examining whether the model developed on one expressway was applicable to other similar ones. In addition, the prediction results and transferability of the SVM model were compared with those given by other frequently used classification algorithms, including logistic regression, Bayesian networks, naïve Bayes classifier, k-nearest neighbor, and back propagation neural network. The results showed that the SVM model was more suitable to the prediction of real-time crash risk with small-scale data than other algorithms, with its accuracy in classifying crashes reaching a best of 80%. A comparison to similar studies by other researchers implied that the proposed model achieved better prediction accuracy.
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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 | 0.005 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".