Combined Dynamic Route Guidance and Signal Timing Optimization for Urban Traffic Congestion Caused by Accidents
Bibliographic record
Abstract
When traffic congestion caused by accidents occurs on urban roads, the current signal control scheme may face failure. It is necessary to establish an effective dynamic route guidance scheme and formulate a reasonable signal timing scheme to alleviate the impact of the accident on urban traffic. In this paper, traffic congestion is divided into three states and the traffic evacuation strategies are formulated, respectively. Then, on the basis of analyzing the internal mechanism and spatiotemporal variation of queuing at intersections, a two-stage model is proposed to optimize the signal timing parameters. In the first stage, the basic timing parameters of each intersection, such as signal cycle and green ratio, are obtained. In the second stage, the offset optimization model is constructed to minimize the total delay time of the coordinated phase of all detour routes in the control scope, considering the early dissipation of queuing vehicles at the entrance of the intersection. This model is solved with a particle swarm optimization algorithm and simulated to the actual road network with VISSIM. As the experimental results show, this control method can greatly reduce the average vehicle delay, stop times, queue length, and the number of vehicles passing.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".