Evaluating the impact of new congestion charging scheme using smartphone-based data: a spatial change detection study
Bibliographic record
Abstract
Traffic congestion in urban areas is a challenging issue in transportation planning. Policy options have been proposed to evaluate the impacts of interventional action through change detection or before–after studies. In this research, low-cost traffic image data collected by smartphone-based application have been employed and the impact of new congestion charging scheme (CCS) upon congestion within congestion charging zone (CCZ) as well as the entire network in Tehran, the capital of Iran has been investigated. Applying statistical tests indicated the significance of change in congestion within CCZ by applying the new CCS. Differential Moran’s I as spatial autocorrelation index specified the spatial patterns of congestion between the critical time of changing the scheme on weekdays (17:00–19:00) and weekend (6:00–13:00) after implementing the new CCS. The approach in this paper can be used with a low-cost appropriate instrument to monitor the probable change in traffic congestion by introducing any new scheme or sudden change.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".