Analysis of Traffic Congestion Factors in Typical Sections of Expressways Using Structural Equation Model
Bibliographic record
Abstract
Urban expressways serve as the main arteries of urban transportation. Congestion and traffic disruptions on expressways can easily lead to the paralysis of the entire regional transportation system. Accurately understanding the patterns of congestion formation and influencing factors on expressways is beneficial for improving traffic efficiency and reducing travel costs. This study takes typical sections of expressway diverging and exit sections as examples, introducing traffic data provided by navigation systems to explore the potential influencing factors and formation processes of urban expressway traffic congestion. This study explores the effects of operating conditions, control facilities, road properties, weather, and other factors on the smoothness of exit sections based on navigation and field survey data. The traffic congestion index is used as an indicator of congestion degree to evaluate the smoothness of typical area of urban expressways exit sections and the overall safety of urban roads. A structural equation model is used to construct a traffic congestion impact model. The results show that traffic facilities ( β = 0.462, p < 0.001), road conditions ( β = 0.177, p < 0.001), road location ( β = 0.129, p < 0.001), spatiotemporal characteristics (time of day: β = 0.295, p < 0.001; day of week: β = −0.105, p < 0.001), environment ( β = 0.021, p < 0.001), and driving behavior ( β = 0.326, p < 0.001) have a significant impact on traffic congestion. And driving behavior can be used as an intermediate variable to affect the relationship between transportation facilities, road conditions, road location, spatiotemporal characteristics, environment, and traffic congestion. The research contributes to a precise understanding of the formation patterns and influencing factors of urban expressway traffic congestion, laying the groundwork for the adoption of targeted traffic management measures to improve traffic flow efficiency and reduce accident occurrences.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".