Trade-Offs Between Bus and Private Vehicle Delays at Signalized Intersections: Case Study of a Multiobjective Model
Bibliographic record
Abstract
Mixed road users on most urban arterials are controlled by the same set of signals and must compete for shared road space. Transit signal priority (TSP) systems have been established to improve transit service operations in mixed traffic. To balance the benefits of priority control with the negative effects, most existing adaptive TSP strategies normally use an integrated performance index, a weighted sum of all types of delays, for evaluation and optimization. In a previous study, the authors formulated the TSP optimization into a quadratic programming problem with an enhanced delay-based performance index to obtain global optimization. In this study, the problem was formulated into a multiobjective optimization model, which was solved with a nondominated sorting genetic algorithm. Pareto-optimal front results were presented to evaluate the trade-offs between two objectives: minimization of private vehicle delay and of bus delay. Then the most appropriate solution was chosen with high-level information. A simulation study was conducted along 7.4 km of a bus corridor, with an adaptive TSP simulation platform, by using a full-scale signal simulator, ASC/3, in Vissim. The results show that the Pareto-optimal solutions provided more interesting practical options for decision makers.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".