Modelling Method on Dynamic Optimal Setting and Associated Control for Intermittent Bus Lane
Bibliographic record
Abstract
A new type of dynamic bus lane not only ensures the strategy of bus priority but also significantly improves the spatiotemporal utilization of road resources and reduces the conflicting demands of buses and social vehicles on road resources. Firstly, the heterogeneity of bus arrival time is analysed according to the process of aggregation and dissipation of vehicle queues at intersections. Considering the correlation between the operating states of social vehicles and buses, a dynamic control strategy for bus lane based on the insertable interval of social vehicles is established. Secondly, combining the setting conditions of the intermittent bus lane control area with the correlation scenario of signal control at intersections and then according to the HCM 2010 vehicle delay equation and the BPR function, the associated optimized control model with the minimum total travel time consumption of the road section as the objective function is constructed. The global optimal control of the intermittent bus lane is realized through the computational experiment. Finally, the setting conditions and benefits of three lane organization schemes (including intersect (i.e., no bus priority), parallel, and intermittent bus lanes) are compared and analysed through case study. The results indicate that the intermittent priority bus lanes have opening hours, which can not only ensure bus priority but also expand the right way for social vehicles and make full use of road space resources, thus improving the overall traffic efficiency.
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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 | 0.001 | 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) | 0.004 | 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".