Impacts of Connected, Automated Vehicle Behaviour on Transportation Greenhouse Gas Emissions
Bibliographic record
Abstract
This study investigates the emissions impact of connected automated vehicles (CAVs) compared to driver-operated vehicles (DOVs) on four key roads in Ottawa: Highway 417, Baseline Road, Hunt Club Road, and Airport Parkway/Bronson Avenue.The primary objective of this research is to identify statistical models that can signify changes in greenhouse gas (GHG) emissions.Additionally, the study aims to illustrate the trends in traffic parameters and GHG emissions across different driving behaviours and varying traffic demands.Employing simulations encompassing three CAV driving behaviours (cautious, normal, and aggressive) and varying CAV penetrations from 0% to 100%, the research replicates projected morning peak hour traffic demands in 2031 while introducing fluctuations of ±20% from the projected traffic volume.The analysis of emissions data is performed by employing outputs generated from PTV VISSIM, a microsimulation traffic software, in conjunction with a motor vehicle emission simulator software.Key findings suggest a correlation between CAV penetration and expected GHG emissions.Notably, aggressive driving behaviour demonstrated superior performance in both minimizing delay and emissions across the four road networks, followed closely by the normal driving behaviour.These results emphasize the potential of both normal and aggressive driving behaviours in reducing GHG emissions and traffic delays compared to DOVs when higher CAV penetrations are incorporated.The developed statistical models for emissions as function of main traffic flow parameters can be invaluable for policymakers and transportation planners seeking to mitigate road network emissions and traffic congestion through the integration of CAVs, ultimately contributing to sustainable and efficient urban mobility.Seg ment DOV's CAV's Cautious Normal Aggressive 0% 25% 50% 75% 100% 25% 50% 75% 100% 25% 50% 75% 100% 6 640.4 849.2 764.0 764.9 717.1 664.0 647.0 609.5 629.3 625.8 602.6 580.6 543.7 7 333.1 385.6 373.1 388.8 378.6 329.3 332.1 321.5 339.3 312.5 297.7 291.4 267.0 8 635.9 952.7 1058.4 1098.0 1067.0 628.8 611.7 594.6 907.2 618.1 591.5 577.2 558.8 9 682.1 802.9 537.5 401.9 351.9 676.9 702.6 719.0 748.8 653.1 661.2 667.6 702.6 10 628.5 560.3 356.9 262.7 226.6 611.0 601.4 577.8 463.3 611.1 590.3 573.7 544.7 11 418.0 460.8 291.2 205.7 178.4 407.6 399.9 385.6 319.5 404.5 397.2 388.4 377.4 12 322.4277.8 177.8 131.8 113.9 319.6 322.2 304.9 246.5 308.4 292.5 287.4 274.6 13 107.5 113.2 90.8 79.4 80.3 110.0 111.6 108.2 97.0 109.0 107.7 108.1 106.0 14 407.6 464.6 339.4 285.2 268.2 392.6 395.8 374.6 325.5 389.3 384.4 378.6 372.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".