A Microscopic Traffic Model to Investigate the Effect of Connected Autonomous Vehicles at Bottlenecks and the Impact of Cyberattacks
Bibliographic record
Abstract
Bottlenecks reduce both traffic safety and efficiency, resulting in congestion and collisions. The introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks and can improve traffic efficiency at bottlenecks. This paper proposes a microscopic traffic model to investigate CAV behavior at bottlenecks and examine the effect of cyberattacks. The model is developed using data collected from a roadside sensor node. It is implemented in MATLAB using the Euler scheme to simulate a platoon of vehicles on a circular road of length 1 km. The performance is compared with the intelligent driver (ID) model. The results obtained indicate that the road capacity with the proposed model is 1.4 times higher than with the ID model. Further, the proposed model results in nearly constant speeds with small variations, which is realistic. Conversely, the ID model produces large speed variations that are unrealistic. In addition, the proposed model results in less acceleration and deceleration, which leads to lower vehicle emissions and pollution. The efficiency is better than with the ID model due to CAV communication and coordination, so queues dissipate faster. The traffic flow with the proposed model increases as the density decreases, which is consistent with traffic dynamics. It is also shown that the proposed model can characterize CAV behavior under cyberattacks that cause disruptions in the data. Thus, it can be employed for traffic control and forecasting when bottleneck conditions exist and there is malicious behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 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".