Context-Aware Services Using MANETs for Long-Distance Vehicular Systems: A Cognitive Agent-Based Model
Bibliographic record
Abstract
Long-distance transportation systems play an important role in economic growth. Yet, these systems are incurred with multifaceted delays and cost problems. The major incites affecting transportation systems are congestion, breakdowns, emergencies, and inclement weather. Scarcity of information about the environment also exacerbates travel problems. It is essential to employ monitoring and guidance that aid in making timely decisions through premediated information. This work aims to provide a flexible model for the long-distance transport system. The model is based on problems faced in long-distance transportation. Moreover, we examine the possible use of emerging Information and Communication Technologies (ICTs) for better transportation. The system dynamics study the problem at hand through cognitive agent-based modelling (ABM) concepts. The integrated model lays the rules to abate traffic delays. In this model, the distance travelled by vehicles is divided into sections using checkpoints. Every section is composed of different agents such as medical units, police stations, workshops, and petrol pumps. The vehicle shifts connection over the mobile ad hoc network (MANET) when enters or leaves a section. We used NetLogo for simulation of the model. A monitoring and guidance system is tested, and obtained results are analyzed by addressing problems causing delays. The guidance system helps vehicles to take optimal decisions for the time, congestion, and rests. The model can be used to improve decision-making for vehicles through premediated decisions. The proposed model can help to improve the efficiency of the transportation systems by reducing travel time.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".