Influence of Traffic Signal Countdown Timers on Safety and Efficiency at Signalized Intersections
Bibliographic record
Abstract
Traffic Signal Countdown Timer (TSCT) displays the remaining times of green, yellow, and red intervals at a traffic signal. While TSCT has widely been implemented in various countries, the effects of TSCT on traffic involving passenger cars and trucks remain unexplored. Thus, this study investigates the impacts of TSCT on traffic safety and efficiency at signalized intersections with high truck volume along the Huron Church Road in Windsor, Ontario, Canada. Driver behavior and traffic flow were predicted using Vissim traffic simulation for the four scenarios: no-timer, Green Signal Countdown Timer (GSCT), Red Signal Countdown Timer (RSCT), and a combination of GSCT and RSCT (GSCT+RSCT). Based on the observational data from previous field studies, changes in driver behaviours in the presence of TSCT were replicated by dynamically adjusting the simulation parameters in different signal phases using Vissim-COM interface. The result shows that the Crash Potential Index (CPI) decreased by 37% while the network-wide speed increased by 6% in the GSCT+RSCT scenario compared to the no-timer scenario. Although the RSCT reduced the speed but it increased the number of vehicles entering the intersection in the first 5 seconds of the green phase by 61% and reduced the CPI by 23%. The increase in speed near the intersection during the green phase was observed in the GSCT scenario whereas smoother deceleration rate of approach vehicles during the red phase was observed in the RSCT scenario. Moreover, the TSCT helped cars avoid rear-end conflicts and increased truck speed. This study demonstrates that TSCT can potentially improve safety and efficiency of car-truck mixed traffic at signalized intersections.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".