On the Impact of Road Traffic Control on Mobile Communications
Bibliographic record
Abstract
Road traffic control systems can change vehicles’ speeds, density, and distribution in spatial and temporal dimensions, which may have significant impacts on the performance of pre-established cellular networks and Vehicular Ad-hoc Networks (VANETs). Identifying these impacts is crucial for satisfying service requirements, especially for the future of connected autonomous vehicles. Despite the extensive research that studied the impact of mobility on communication, the impact of traffic control on communication has not been addressed. Therefore, in this paper, we attempt to understand how traffic control strategies can affect communication network performance. We focus on vehicle navigation techniques because of their global network impacts that can significantly affect the load and handover rate on base stations. In this paper, we compare the Dynamic Shortest Path Routing (DSPR) to the state-of-the-art vehicle routing techniques, namely, the K-Shortest Path Routing (K-SPR) and Travel Time System Optimum Navigation (TTSON). We build a real network with calibrated traffic and use a microscopic traffic simulator as a testbed to measure the load and handover rates on base stations. Moreover, we developed and validated an analytical model to compute the packet drop probability based on the base station normalized load in the Fifth Generation New Radio (5G-NR) cellular networks. The developed model is integrated into the testbed to evaluate the reliability of the three traffic control systems. The analysis shows that road traffic load-balancing achieved by both TTSON and K-SPR improves communication performance in the simulated network.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".