MANETS/VANETS in AutomobileSystems: A Simulative Study into how Mobile Ad-hoc Networks can be used in Traffic Control Systems
Bibliographic record
Abstract
This thesis describes the modeling and implementation of an advanced traffic signal control system within a simulation environment, thus creating a laboratory for the evaluation of advanced traffic control strategies at road intersections, including transit signal priority. The simulation is done as a C++ implementation code and NS2 simulation is proposed for further evaluation and testing for a microscopic traffic simulation for Intelligent Transportation System(ITS) design. The control system is designed with a generic and flexible logic that allows it to simulate a wide range of traffic signal control types and strategies. The strategies include means of deadlock avoidance and means to avoid starvation to all the vehicles approaching an intersection requesting for a right of way. The control system is also designed as a distributed control system in which vehicles leading in a road segment i.e. for vehicles approaching an intersection, the leader refers to the car leading the pack at or in the junction. This vehicle is made to participate in a leader election process. This is a bid or a contest to gain or win the right of way for the vehicles for which the leader is in the same road segment with. Specialized features of advanced control strategies are implemented within the Control system framework which allows the implementation of transit signal priority and other specialized vehicles that might require prioritization within the simulation environment, allowing the simulation of both passive and active signal priority strategies. The capabilities of the control system are illustrated through a case study in which a simulation is done for a three and four way intersection and the results of the simulation studied against the objectives of the prioritization strategies. An evaluation of the currently implemented system is performed, and recommendations for improvement and further study are offered.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".