Molecular Dynamics Characteristics and Model of Vehicle-Following Behavior
Bibliographic record
Abstract
The vehicle-following behavior is a self-organizing behavior that restores dynamic balance under the stimulation of external environmental factors. In fact, there are asymmetric problems in the process of acceleration and deceleration of drivers. The existing traditional models ignored the differences between acceleration and deceleration of vehicles. In order to solve this problem, the vehicles driving on the road are compared to interacting molecules. Vehicle-following characteristics are studied, and the molecular following model is established based on molecular dynamics. The model parameters under different conditions are calibrated considering the required safety distance by the vehicle and the reaction time of the driver. With the help of the vehicle running track graphs, speed, and acceleration graphs, the numerical simulations of the molecular following model and the classical optimal speed vehicle-following model are carried out. The results of the comparative analysis show that the acceleration in the process of acceleration and deceleration is not constant but more sensitive to the deceleration of the preceding vehicle than to the acceleration and more sensitive to the acceleration/deceleration of the short-distance vehicle than to the acceleration/deceleration of the long-distance vehicle. Therefore, the molecular following model can better describe the vehicle-following behavior, and the research results can provide a theoretical basis and a technical reference for the analysis of traffic flow dynamic characteristics and adaptive cruise control technology.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".