A study on traffic signal patterns and performance measures accounting for pedestrian and vehicle flows
Bibliographic record
Abstract
Traffic control systems are crucial for managing traffic flows. Their main function is to reduce interactions among users for safety reasons while minimizing travel times. Traffic signal control optimization is primarily concerned with determining the cycle length and the signal pattern. Researchers often concentrate on the cycle length, whose impact on travel times is directly measurable. However, the choice of signal pattern may also have a great potential to reduce travel times and unsafe situations. This potential is yet to be thoroughly investigated. In this work, we are interested in comparing different signal patterns in terms of the number of potential conflicts and delay time for both drivers and pedestrians. To this end, we first select three commonly adopted signal patterns, namely the Exclusive Pedestrian Phase (EPP), the Leading Through Interval (LTI), and the Two-Way Crossing (TWC). We then generalize existing methods for measuring user delay and safety for these three signal patterns. Moreover, we investigate a hypothetical hybrid pattern obtained by dynamically adapting the signal pattern to real-time data. The proposed methodology is applied to a case study considering an isolated intersection in Montreal, Canada. We perform computational experiments geared towards determining the best pattern according to ad hoc performance indicators and user flows. Results show that the EPP and LTI patterns generally perform better than TWC. EPP outperforms LTI when measuring the number of potential conflicts, while LTI outperforms EPP when considering delay times. Furthermore, the hypothetical hybrid pattern shows a positive but overall limited impact.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".