Cycle Maximum Queue Length Estimation: An Integrated Deep Learning and Adaptive Neuro-Fuzzy Inference System Framework
Bibliographic record
Abstract
Queue Length Estimation (QLE) at signalized intersections is vital for enhancing urban mobility and managing congestion effectively. This study introduces a novel QLE framework that diverges from traditional methods, which rely on sophisticated sensor settings and shockwave models, by focusing on the kinetic features of vehicles captured at the stopline by sensors. Developed systematically in four stages, the framework begins with a binary classification of vehicles as queuing or non-queuing. This is followed by a lane-based analysis of sequential vehicles to examine following patterns, refining the initial classifications through a label modification strategy, and culminating in the construction of queue sequences for accurate length calculation. A conventional deep learning (DL) method, Multi-Layer Perceptron (MLP), alongside a spatiotemporal approach, Convolutional Neural Network-Long Short-Term Memory-Attention (CNN-LSTM-Attention, C-L-A) were employed for the initial binary classification. Empirical results show the MLP model outperforms C-L-A in this task. Additionally, integrating an Adaptive Neuro-Fuzzy Inference System (ANFIS) with sequential modification enhances the label modification process, facilitating accurate queue sequence generation. Refined vehicle labels are then used for queue length calculation, using a proposed Non-queuing Tolerance Technique (NQTT) and Longest Continuous Queuing Sequence (LCQS). The framework outperforms conventional methods under both oversaturated and undersaturated conditions, with statistically significant improvements validated by one-sided t-tests. Furthermore, in accident-specific scenarios such as rear-end collisions (REC), the framework demonstrates robust performance, effectively handling complexities like increased headway and lane-changing behaviors. This integration of DL and traditional traffic measurement techniques highlights the system’s adaptability in improving QLE accuracy across diverse traffic conditions.
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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 | 0.000 |
| 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".