A Performance Evaluation of Time-Series Mobility Prediction for Connected Vehicular Networks
Bibliographic record
Abstract
The role of connected vehicular networks has become vital for the future of smart and modern cities, as it can be envisioned as a stand alone connected network or a bridge between various networks, and end-users. Vehicular networks design provides safety, traffic control management, and cruise control services, among others. Many applications and protocols such as mobility management, service discovery, and routing were proposed over the past years to elevate the performance of vehicular networks communication and connectivity. In that context, understanding the vehicles' mobility characteristic, behavior, and pattern would assist those applications to become user-centric, adaptive, and proactive based on the dynamics of vehicles' projections. In this paper, we evaluate the performance of time-series forecasting techniques for vehicular mobility prediction. We first describe and extract vehicles mobility features into time-series sequences and discuss several machine learning techniques. Then, we compare the performance of recent time-series ML-based techniques, LSTM, and GRNN in terms of complexity and accuracy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".