Capability of Intermittent Bus Lane Utilization for Regular Vehicles
Bibliographic record
Abstract
Intermittent bus lanes (IBLs) can improve road capacity by allowing other regular vehicles to drive in the idle space of a dedicated bus lane. However, excessive vehicles in the IBL will cause additional bus delays. To avoid such problems, this study proposes a method to determine the capability of IBL permitted for regular vehicles first, and then use it as the total amount restriction of lane-borrowing vehicles to implement a bus lane control strategy that will improve road capacity and avoid additional bus delays. A model for calculating the capability of IBL is also provided. Vehicles between two buses are designated as potentially lane-borrowing vehicles that could follow the buses to leave the road section. The evolution process of these vehicles in the unit is analyzed using kinematic wave theory to obtain the formed traffic queue length. Using the rear bus trajectory to set the length limit on the traffic queue, the estimated total amount of lane-borrowing vehicles is corrected to establish the final capability of the IBL. The applicability of the method was evaluated from three perspectives: bus departure interval, road traffic saturation, and near-side bus stop. The simulation results showed that the proposed method can guarantee no additional bus delay compared to the situation of a dedicated bus lane. It can also improve road capacity more than traditional IBL under any degree of saturation and bus departure interval. Compared with traditional IBL, the average travel time of regular vehicles is shorter, except when the degree of saturation is high and the bus departure interval is large.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".