Quantitative Evaluation of the Impacts of the Time Headway of Adaptive Cruise Control Systems on Congested Urban Freeways Using Different Car Following Models and Early Control Results
Bibliographic record
Abstract
The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".