Traffic and Weather Data Fusion for Traffic Prediction in Sustainable Cities
Bibliographic record
Abstract
Anticipating traffic patterns is a vital component in efficiently managing traffic within smart cities. This proactive approach contributes to alleviating traffic congestion, promoting environmental preservation, reducing commute times, and strengthening overall safety measures. Numerous studies have verified that external factors, such as weather conditions, exert an influence on traffic patterns. Therefore, utilizing these factors as supplementary variables can improve traffic prediction accuracy. This paper presents a novel prediction model called the multi‐input sequential multihead attention (MI‐SMHA) model. This model integrates weather condition information into traffic prediction tasks, aiming to enhance prediction accuracy and computational efficiency. It utilizes advanced techniques from sequential modeling and attention mechanisms, specifically tailored to handle traffic and weather data such as temperature, wind speed, precipitation, visibility, and humidity. This integration aims to leverage the complementary nature of weather conditions in forecasting traffic patterns, yet it remains flexible enough to be generalized to support a wide range of multivariate time series prediction tasks. Data from real‐life traffic detectors are utilized to perform experiments and comparisons with two baseline models and three state‐of‐the‐art models to validate and assess the efficiency of the proposed model. The MI‐SMHA model was efficient and reliable in forecasting future traffic flow, significantly reducing errors compared to the other models.
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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 | 0.001 |
| 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 | 0.001 |
| 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".