Predicting freeway non-recurring congestion via a spatio-temporal deep learning approach
Bibliographic record
Abstract
Freeway unexpected events, such as car crashes, result in non-recurring congestion, which does not follow repetitive patterns in space and/or time and increases the challenge of freeway management. Existing prediction approaches of Freeway Non-recurring Congestion (FNC) are either qualitative with information loss or single-step quantitative for a short period. To address these limitations, this paper develops a quantitative, interpretability-enhanced, and spatial-temporal approach using a 2-Encoder-Decoder Model with an Attention Layer (Att-2ED) for FNC prediction. The model leverages Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) as two encoders to process time-series speed profiles and static crash features independently. It also employs an LSTM-based decoder to generate a spatio-temporal sequence output and an attention mechanism to prioritise the weights of input sequences at each time step. The model has been trained and tested using real-world freeway data. The proposed model demonstrates superior performance in predicting traffic conditions across both spatial and temporal dimensions compared to existing benchmarks, achieving a Mean Absolute Error (MAE) of 3.74 for 15-minute-ahead predictions. Its effectiveness is further underscored through a comparative analysis of prediction accuracy across various congestion levels and crash severities. For instance, the MAE ranges from 2.77 to 7.00 for severe crashes and from 2.68 to 6.47 for peak-hour crashes, with prediction horizons spanning from 5 min to 60 min ahead. Moreover, the attention mechanisms incorporated in the model provide interpretability-enhanced predictions, highlighting the significance of the last 10-min input in the prediction.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".