Modeling Lane Changes at Freeway On‐Ramps With a Novel Car‐Following Model Based on Desired Time Headways
Bibliographic record
Abstract
The traffic flow at freeway on‐ramps is influenced not only by the lane changes made by merging vehicles but also by the longitudinal behavior of the merging vehicles and vehicles in the main lane. Existing car‐following models are not suitable to represent the longitudinal behavior during merging because they are based on the idea that vehicles intend to reach a steady state, that is, constant time headway and zero speed difference, as soon as possible. At on‐ramps, however, merging vehicles have time to reach this steady state until they reach the end of the on‐ramp. We therefore derive a novel car‐following model based on desired time headways that is able to represent this continuous adaptation toward a steady state. From this car‐following model, we derive a lane change model for freeway on‐ramps with seven parameters. The lane change model includes a leader selection algorithm, which enables merging vehicles to pass or be passed by vehicles in the main lane. The model also includes components to predict the lane change start time based on surrogate safety measures and to describe the lateral behavior during the lane change. Due to the resemblance to car‐following models, the methodology to calibrate the lane change model at the microscopic scale can be adopted from car‐following models. To validate the model, we conduct traffic simulations and compare the simulated traffic flow with trajectory data from two German freeway on‐ramps. The results show that the model accurately represents the longitudinal driving behavior of merging vehicles and their followers, although it slightly overestimates the number of merging vehicles passing a vehicle in the main lane under congested traffic conditions. The simulations yield accurate headway distributions, except in cases of very risky driver behavior, and realistically capture the macroscopic speed‐density relationship at the on‐ramp.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".