Performance-based intersection layout under a flyover for heterogeneous traffic
Bibliographic record
Abstract
Flyovers are constructed to manage heavy through movement. However, traffic operations underneath a flyover remain unmanaged and often pose a major concern in developing countries with non-lane-based heterogeneous traffic. This may reduce the overall benefit of a flyover. An alternative intersection layout is proposed to improve traffic operations at the intersection underneath a flyover. The proposed layout segregates the traffic movements through effective channelization. A traffic island is also proposed in the middle of the intersection to facilitate concurrent right-turning movements. This layout helps in eliminating a signal phase and cuts down traffic cycle time by 40 %. A microsimulation-based traffic simulation model is developed for the evaluation of the proposed layout. The simulation model demonstrates effectiveness of the proposed layout. Average delay and average queue length are compared to measure the effectiveness. Traffic volume sensitivity analysis is conducted to estimate the capacity of the proposed layout. An intersection underneath a flyover along the Eastern Expressway in Mumbai is considered for the case study. The effectiveness of the proposed layout at the study location for varying flow level is evaluated by comparing average delay, average stop delay, average number of stops per vehicle, average queue length, and maximum queue length.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".