Prediction of Taxi Quantity in Hangzhou Based on Principal Component Regression Analysis
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
The congestion of a large city is closely related to the growth of the number of taxis. As an important part of urban traffic, it is particularly important to control traffic congestion to master the number of taxis. In order to assess the impact of taxis on the traffic flow in the future, a scientific method is needed to predict the number of taxis efficiently and accurately. Through qualitative analysis and quantitative correlation analysis, eight influential factors with high correlation were extracted as predictors. The multicollinearity among influencing factors was eliminated by principal component analysis. Based on the single regression prediction of principal component, the regression prediction model of taxi number is constructed. The accuracy test results show that the model has high precision and can be used to predict the number of taxis quickly. It can provide an important basis for urban traffic management departments to effectively control congestion and make accurate traffic decisions.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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