Pedestrian Traffic Prediction using Deep Learning
Why this work is in the frame
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Bibliographic record
Abstract
Pedestrian traffic information offers useful insights when developing or maintaining a business.This research combines image processing and machine learning methods to predictpedestrian traffic flowrate and density for up to two days into the future, based on weatherdata, calendar data, and special events. To obtain the traffic flowrate and density, we firstdeveloped a neural network model to predict the number of new people and the total numberof people in each sequence of images captured by a Nova Scotia Webcams camera. Thesecounts of people are used to calculate the pedestrian traffic flowrate and density labels forhourly intervals. These labels are then combined with hourly weather data, calendar data,and special event data from the same period to train a recurrent neural network to predictthe traffic flowrate and density for up to two days in advance.We try two different approaches, CNN-LSTM and dual input CNN to predict the numberof new people and the total number of people from the images and compare how well eachapproach performs. The results show that the dual image input CNN models are moreeffective at predicting the number of new people and the total number of people than the CNN- LSTM models. Tested on independent test sets of images using K-fold cross-validation, theMTL CNN model achieved a test accuracy of 72% for the number of new people and 78%accuracy for the total number of people.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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