Macroscopic Traffic Flow Analysis and Optimization with V2I Connectivity and Collision Avoidance Constraints
Bibliographic record
Abstract
In fully automated traffic streams, speed optimization of connected and autonomous vehicles (CAVs) is a fundamental challenge. However, while increasing the CAVs' speed improves traffic flow, it increases communication handoffs as the CAVs switch from one base station (BS) to another, thus reducing communication data rates. Therefore, a trade-off exists between the communication data rates and CAV traffic flow. In this paper, we develop a novel framework to analyze and maximize the macroscopic traffic flow by optimizing the speed of CAVs and network deployment such that the CAVs' data rate requirements can be satisfied. We first characterize a closed form expression of the macroscopic traffic flow by considering exponential distribution of the spacing between CAVs. The derived expression is used to jointly optimize the deployment of BSs and speed of CAVs while maximizing the CAVs' traffic flow with collision avoidance and handoff-aware data rate constraints. Closed-form optimal solutions are then presented for the CAV's speed and the number of BSs deployed along the corridor considering a high signal-to-noise ratio (SNR) regime. Numerical results validate the accuracy of the derived expressions. Our results show that increasing the BS density or lowering the data rate requirements of CAVs enhances the data rates which increases CAV speeds and in turn the traffic flow.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".