Multi‐objective analysis of using U‐turns as alternatives to direct left turns at two‐way stop‐controlled intersections
Bibliographic record
Abstract
Summary This study aims to propose a method to conduct multi‐objective analysis of traffic treatments by taking into consideration multiple external impacts. To illustrate the procedure, the economic benefit of converting a two‐way stop‐controlled (TWSC) intersection to a right turn followed by U‐turn (RTUT) intersection was calculated, considering not only the safety impacts but also the operational and environmental impacts. First, vissim simulation models were developed to obtain the total travel time, vehicle emissions, and fuel consumption for the intersection both before and after the treatment. The operational impact was calculated as the travel time saving benefits. The environmental impact was calculated as the reduction in vehicle emissions and fuel consumption costs. The safety impact was estimated as the crash reduction benefits for the RTUT treatment using safety performance functions and crash modification factors (CMFs). CMFs were estimated using meta‐analysis methods. Finally, the life‐cycle cost method was used to combine different components in the total benefit. The Monte Carlo simulation method was used to conduct uncertainty analysis by using random sampling from probability descriptions of uncertain input variables to generate a probabilistic description of results. The findings showed that, first, the benefits with the use of RTUT treatment can be countervailing even for a single objective. Second, the net present value associated with the RTUT treatment increased with an increase in the proportion of left‐turn traffic from the major street when the percentage of left‐turn traffic from the major street was from 0% to 22% and became stable after that. The study illustrates the detailed process of evaluating projects considering multiple objectives. This process offers policy and decision makers a solid and practical reference using existing guidebooks and software. The findings also provide suggestions about the suitable condition to install RTUT treatments at TWSC intersections. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".