A Study on the contemporary insights of traffic signal design under heterogeneous conditions: a literature review
Bibliographic record
Abstract
Traffic signal design is usually carried out based on one or other design guideline. While the developed countries like UK, USA, Canada and Australia have come up with their own guidelines, developing countries are still using these guidelines developed in the western world with very different traffic conditions. While the conventional guidelines are formed based on lane-based homogeneous car dominant traffic regimes, developing countries have their own traffic characteristics like non-lane based traffic, heavy presence of two wheelers, three wheelers and non-motorized vehicles. Driver behavior in the developing countries are significantly different from that of the developed countries. Recently various researchers have addressed this issue by observing the traffic patterns in such situations and proposing various methods to fine tune the signal design guidelines. Deviations from near homogeneous conditions happen basically due to the heterogeneous nature of the traffic. Heavy presence of motorcycles in the traffic stream has contributed to a distinct behavior in the signalized intersections resulting initial surge and grouping. They also tend to creep between the larger vehicles to occupy the available spaces resulting noncompliance to lanes and making it difficult to model their behavior. This is because most of the contemporary methods for PCU estimation is based on the systematic discharge of vehicles in a queue where a reasonable headway can be observed. Noncompliance to lanes challenges this methodology. When the traffic streams are composed of non-motorized traffic and three wheelers, their impact cannot be compensated through a constant PCU assigned to them. It is observed that PCU values in an intersection under heterogeneous traffic is not a constant but become dynamic based on various factors such as level of saturation, vehicle composition and type of facility. Proof for this phenomenon is strong in heterogeneous traffic conditions. The effects of bicyclists and red violating pedestrians are also having a detrimental effect on the assumptions used in classical design manuals developed in the developed world. Geometrical considerations like shared left lanes, shared right lanes, upstream short lanes and downstream lanes drops also have a significant impact on the performance of intersections under heterogeneous traffic. Therefore, this literature review concludes the necessity of a tailor made method of traffic signal calculations under heterogeneous traffic, especially when reaching saturation levels as witnessed in the developing countries.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".