Traffic Flow Prediction Based on Cascaded Artificial Neural Network
Bibliographic record
Abstract
The prediction of traffic flow is of great significance for the prevention of accidents, the avoidance of congestion and the dispatch of command center. Considering the complexity of traffic data in reality, it is an extraordinarily challenging task to forecast accurately from historical patterns. In this paper, we propose a method based on the cascaded artificial neural network (CANN) to predict traffic flow at positions. In order to express the spatial correlation of traffic data, the actual road network distance is introduced in our model. The realworld data derived from video surveillance cameras in Xiamen is used in the experiment which is compared with five baselines. To the best of our knowledge, this is the first time that CANN is applied to forecast traffic flow. The experimental results demonstrate that the CANN method has superior performance. In addition, We also discuss the impact of some external factors such as temperature, weather and holidays on the prediction results.
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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 | 0.000 | 0.000 |
| 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".