Managing Trams and Traffic at Intersections with Hook Turns
Bibliographic record
Abstract
Signalized intersections with trams in mixed traffic are problematic because of conflicts with traffic making opposing turns and the delays that they cause for trams. In Melbourne, Australia, an approach called a hook turn, used for more than 50 years, removes this barrier because turning traffic must wait in curbside lanes to make turns when signals for the side road turn green. This paper presents a review of the hook turn and explores operations and safety impacts. Hook-turn styles of maneuvers are in use for buses in Australia, traffic in China and the United States (Illinois), and bikes and motorcycles in several countries. In each case, hook turns relocate opposing turns to improve intersection safety and efficiency. Operational analysis of the traffic impacts of hook turns in Melbourne suggest that they act to reduce congestion because turning traffic does not delay through vehicles. In addition, 38% of drivers tend to avoid hook turns; that decision acts to increase intersection capacity. An analysis of tram delay suggests a savings of 11.25 s to 15.64 s per tram and of three to five trams being added to the fleet compared with the number needed in an operation without hook turns, or between Aust$15 million (US$12.9 million, in 2010 dollars) and Aust$25 million (US$21.5 million) in fleet capital costs. A series of safety analyses with crash data and conflict point analysis demonstrates that intersections with hook turns have better safety performance than conventional intersections. Hook turns act to improve intersection operations and safety with trams in the Melbourne context. However, challenges with driver understanding and compliance are major barriers to adoption of hook turns in other areas.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".