Implementation of a Double Continuous Flow Intersection in Riyadh
Bibliographic record
Abstract
The continuous growth of population in the capital, coupled with increased auto ownership and dependence has worsened traffic conditions on Riyadh's road network. Conventional methods to address this increased demand could be costly and insufficient. There has been greater interest in using alternative measures to improve the performance and safety characteristics on main corridors, particularly those that arrive at signalized intersections. Heavy left turning traffic at these intersections is one of the main causes for delays. Previous research has investigated several types of alternative designs termed "unconventional" arterial intersection designs that could minimize the effect of left turning traffic. This paper provides decision makers with an objective assessment on the efficiency of implementing an unconventional intersection design, the Double Continuous Flow Intersection (DCFI) configuration, to improve the operational and safety characteristics of an existing major signalized arterial intersection in Saudi Arabia. In this study, the Kingdom Hospital Intersection in Riyadh was selected, as it is one of the most congested intersections in Riyadh. Using the collected traffic data, the micro-simulation program VISSIM was used to analyze and compare the efficiency of both configurations. When compared to the existing conventional signalized intersection design, it was found that the proposed Double Continuous Flow Intersection (DCFI) unconventional intersection design decreased the average delay per vehicle by 99 seconds. The proposed Double Continuous Flow Intersection configuration also improved the Level of Service at the intersection from level F (152 sec/veh average delay) to level D (53 sec/veh average delay).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".