Utilizing Partial Least-Squares Path Modeling to Analyze Crash Risk Contributing Factors for Shanghai Urban Expressway System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently, frequent crash occurrences significantly influence traffic operation conditions and travel reliability for urban expressway systems. Therefore, it is vital to understand the crash occurrence mechanisms and then introduce safety improvement countermeasures. Emerging studies have been conducted to unveil the relationships between traffic operation conditions and crash occurrence with advanced traffic-sensing data. However, the majority of previous studies have only identified correlation relationships, which are insufficient for traffic-safety improvement. On the other hand, existing crash causal investigations have limitations of utilizing aggregated traffic-flow data and considering the crash occurrence mechanisms only in a reflective way (in contrast to the formative way). In this study, the confounding impacts among crash risk contributing factors and the crash causal relationships were revealed through the partial least-squares path modeling (PLS-PM) analysis approach. Data from the Shanghai urban expressway system in China were utilized for the empirical analyses. First, random forest models were adopted to rank the variable importance, and a total of six contributing factors were selected as inputs that feed into the PLS path models. Then, two different causal relationship structures (formative and reflective) were established, and the best-fitted model structures were identified. The results showed that average operation speed has negative impacts on crash occurrence, and the variables indicated that disturbed traffic flows have positive causal relationships. Finally, the analysis results shed some light on proactive safety management strategies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it