Integrated Optimisation of Traffic Signals and Platoon Trajectories with Advanced Forward-looking Planning in the Connected Vehicle Environment
Bibliographic record
Abstract
Traditional traffic control methods at signalised intersections primarily focused on optimising traffic signals without adequately addressing the coordination between signals and vehicles. Fortunately, with the advent of advanced vehicular communication technologies, real-time bidirectional communication between roadside infrastructure and vehicles has become feasible, significantly improving coordination between these elements. This paper presents a bi-level optimisation method for signalised intersections, enhancing arrive-on-green (AOG) performance in connected vehicles. By extending optimal control from a single dimension - whether spatial or temporal - to a two-dimensional spatial-temporal approach, we develop a comprehensive bi-level control framework. The framework includes outer-layer signal optimisation for maximising green utilisation and inner-layer platoon trajectory optimisation. Intermediate parameters and extended planning-time are proposed to improve solution finding. The effectiveness of the proposed joint optimisation method was evaluated through simulation case studies conducted in SUMO. The results showed increased efficiency and reduced stops, with stable, accurate control.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".