Investigating the Affecting Factors of Speed Dispersion for Suburban Arterial Highways in Nanjing, China
Bibliographic record
Abstract
Suburban arterial highways are usually characterized by mixed traffic environments, which is a major contributor to traffic crashes. It has been known that speed dispersion as a surrogate safety measure has a strong correlation with safety. The objective of this study is to identify the influencing factors of speed dispersion for suburban arterial highways. Two definitions of speed dispersion are proposed for comparison: (1) an individual vehicle speed variation along a highway segment and (2) a vehicle speed variation at a cross-section. Vehicle speeds, traffic composition, and driving interference data were obtained from high-resolution videos from 20 segments of the G205 highway in Nanjing, China. An exploratory factor analysis was used to detect initial relationships between latent influencing factors and 13 candidate variables selected based on traffic condition, road condition, and driving behavior. A multivariable regression model was applied to identify the impacts of latent influencing factors on speed dispersion. The results from the two models showed substantial differences. The road condition factor was not significant in the cross-sectional speed dispersion model, but was interpretive in the segmented speed dispersion model. Driving interferences and illegal driving behaviors had a greater effect on the segmented speed dispersion. Consequently, segmented speed dispersion showed a better performance for the analysis of suburban arterial highways. On the other hand, traffic disturbance caused by driving interferences and illegal driving behaviors is the greatest contributor to high speed dispersion on suburban arterial highways, which may be mitigated by effective traffic management measures. It is expected that this work will help traffic managers better understand speed dispersion in mixed traffic environments and to develop effective safety improvement strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".