A DMAIC-based methodology for improving urban traffic quality with application for city of Montreal
Bibliographic record
Abstract
Traffic congestion is a severe problem in cities. Drivers spend hours going through the traffic. This results not only in driver fatigue but also vehicular emissions, noise and pollutants into the environment. Controlling the situation of congestion in cities is of vital importance to city transport organisations and traffic decision-makers. In this paper, we propose a DMAIC-based methodology for improving the quality of urban traffic. The proposed methodology comprises of three steps. In Step 1, we conduct a survey study to collect traffic congestion data in the city. In Step 2, DMAIC methodology is applied to analyse the survey data collected from Step 1. The techniques used are Descriptive statistics, Pareto analysis, Cause-Effect Diagram, Factor Analysis, and Control Charts (Individual and Multivariate). In the third step, we generate recommendations for reducing traffic congestion and ameliorating transportation service quality in cities based on results of Steps 1 and 2. The proposed work is novel and has practical applicability in managing the situation of traffic congestion in cities. An application of the proposed approach for city of Montreal is provided.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".