Cost and Benefit Analysis of An Integrated Traffic Management System Utilizing Connected Vehicles
Bibliographic record
Abstract
The development of connected and autonomous vehicles (CAVs) enables advanced traffic management to improve urban mobility, driving safety, and energy efficiency. An integrated traffic management system was developed to manage a very large number of CAVs in a large network. However, the operational cost of the integrated system is not well evaluated. In this paper, a comprehensive cost and benefit analysis of an integrated traffic management system is conducted to demonstrate the potential implementations of the system in large-scale cities with CAVs in the future. The analysis is conducted in Los Angeles, USA with the implementation of the integrated system for different layers of the network. The savings of the system on mobility and energy are evaluated under different market penetration rates of CAVs. And, the cost of the system on transmitting messages and operating the integrated system at different layers will also be measured. The combined savings and cost of the system is summarized to illustrate its overall benefits for the entire transportation services in LA downtown. The results indicates that the computational cost increases linearly with respect to the market penetration rates (MPRs) of CAVs. And, at higher MPRs, the monetary savings for the entire network are larger due to the significant improvement of mobility.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".