Optimization of Traffic Flow by Allowing Private Cars to Merge into the Bus Lane at Specific Locations
Bibliographic record
Abstract
A dedicated bus lane is designed to give priority to buses over private cars on the road. However, this approach might waste road traffic capacity when bus demand is low and private car demand is high. Thus, it is essential to utilize bus lanes more effectively. Previous work focuses on allowing private cars to use bus lanes which may cause bus delays. To strike a balance, some approaches involve mandating private cars to leave bus lanes and limiting the number of private cars entering bus lanes. However, these highly rely on transportation infrastructure and emerging technologies. Hence, we propose a simple method of a partially space‐shared bus lane, which allows private cars to merge into the bus lane at specific locations. Lane‐changing decision is modeled as a result of speed difference between adjacent lanes. Traffic flow is simulated by the LWR model in the Lagrangian coordinate system. Two scenarios are set up—dedicated bus lane and partially space‐shared bus lane—to evaluate the partially space‐shared system. The only optimization variable is the location that allows private cars to conduct lane‐changing. We use the genetic algorithms to optimize the system. Finally, simulation results show that the partially space‐shared bus lane reduces private‐car delays and total delays of the system. The buses would experience an increase of delays, inevitably. But the bus delay increase could be limited in a small range, which maintains the bus priority. In addition, a multiple lane‐changing position strategy is better than the single position one. This approach is suitable for traffic situations with low bus demand and high private car demand. Our work is expected to contribute to the design of future urban bus lanes and improve overall traffic operations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".