Variable Cell Transmission Model for Mixed Traffic Flow with Connected Automated Vehicles and Human-Driven Vehicles
Bibliographic record
Abstract
The current research on the mixed traffic flow characteristics of human-driven vehicles (HDVs) and connected automated vehicles (CAVs) mainly focuses on the micro-level. To study the characteristics of the mixed traffic flow from the medium and macro level, this paper proposes a variable cell transmission model (VCTM). First, the fundamental diagram is introduced based on the phenomena of hysteresis of traffic flow. Second, the VCTM with different market penetration rates (MPR) of CAVs is proposed based on the classical cell transmission model (CTM). Then, the effectiveness of VCTM is verified by micro-simulation based on the intelligent driver model (IDM). Finally, some congestion indexes are selected to discuss the characteristics of mixed traffic flow based on the VCTM with an expressway simulation. The results show that the traffic capacity and congestion dissipation capacity gradually are increased with the increase of MPR of CAVs. The homogeneous CAVs traffic flow capacity can reach 1.41 times that of the homogeneous HDVs traffic flow, and the congestion dissipation time can be reduced by 25%. The larger MPR is, the greater the improvement effect is. In addition, compared with CTM, VCTM can reflect the delay, queuing, and dissipation of mixed traffic flow more accurately, which is helpful to capture the evolution mechanism of mixed traffic flow in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".