Application of Finite Mixture of Regression Model with Varying Mixing Probabilities to Estimation of Urban Arterial Travel Times
Bibliographic record
Abstract
Travel time along an urban arterial is greatly affected by traffic signals. Most studies on urban travel time use statistical models to obtain the distribution directly without incorporating the effects of traffic signal timing. In this study, a finite mixture of regression model with varying mixing probabilities (weights) was proposed to gain a better understanding of urban travel time distribution through consideration of signal timing. Standard finite mixture models with constant mixing probabilities have a limited ability to adapt to underlying random structural changes for observed travel times. The model developed in this study can capture such dynamics by ( a) modeling the mixing probabilities as a function of the explanatory variables associated with signal timing and ( b) establishing a linear regression between the mean of each component and signal timing. The finite mixture of regression model was applied to the travel time data collected by the automatic vehicle identification system on one urban arterial with the Sydney coordinated adaptive traffic system (SCATS). The results demonstrate that the varying mixing probabilities can be used to classify the samples of travel time, and the mean values of components can capture the effects of signal timing. By comparing various types of mixture models, the proposed approach not only has a better statistical fitting performance but also provides useful information about travel time features.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".