Bibliographic record
Abstract
For commuters, vehicular traffic is an important planning concern. People have access to the weather forecast and the current traffic situation, but there is no application available to estimate traffic congestion and flow in the near future. Thus, we design and develop a machine learning approach which can predict vehicular traffic density and flowrate up to two days in the future based on the weather, calendar and special events data.First, Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) networks are utilized to predict the number of new vehicles and the total number of vehicles in images captured by a Nova Scotia Webcams (NS Webcams) video camera. The best models provide a Mean Absolute Percentage Error (MAPE) of 20.38% for the number of new vehicles and 18.56% for the total number of vehicles. These values are used to estimate traffic flowrate and density for hourly records over a three-month period.The hourly traffic data is combined with observed and forecasted weather data, and special event data to create a time series data. A Multiple Task Learning (MTL) - LSTM model is trained and tested using these data and a K-fold cross-validation approach. The Mean Absolute Error (MAE) and MAPE are used to evaluate the model performance. The MTL-LSTM model achieves a MAPE of 19.35% and 27.50% for flowrate and density using observed weather data, respectively. In the case of forecasted weather data, the MAPE for flowrate and density increases to 20.51% and 31.10%, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.001 |
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".