Temporal Fusion Transformer for Real-Time Intersection Turning Movement Flow Forecasting Incorporating Exogenous Factors
Bibliographic record
Abstract
Real-time intersection turning movement flow (TMF) forecasting is vital for traffic management systems to optimize traffic flow and signal timing. Recent studies have introduced advanced machine learning models for TMF forecasting to capture intricate patterns and relationships present in the data. However, most models rely solely on past observed traffic flow data, neglecting the influence of exogenous factors. This paper addresses these limitations by investigating the potential exogenous factors that could affect TMF and quantifying their impact on forecasting accuracy. To achieve this, a case study is conducted that involves real-world traffic data from 76 intersections with 17 exogenous factors. These factors encompass static covariates (e.g., speed zone, road type), prior-known future time-dependent inputs (e.g., hour of day, temperature), and past observed TMF. Experimental results show that, by incorporating these factors, the TFT model achieved superior performance with lower forecasting errors across various data subsets and whole dataset compared to other models that solely rely on time series data. The modeling results also reveal that speed zone and road category are the most influential static covariates. Among the time-dependent covariates, hour of day and temperature have the strongest influence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".