Implementation of a Double Continuous Flow Intersection in Riyadh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it