CAR FOLLOWING TECHNIQUES: THE ROLE OF THE HUMAN FACTOR RECONSIDERED
Bibliographic record
Abstract
[EN] Engineering and psychophysiological car following models emerge in the late 1950s \n(Saifuzzaman & Zheng, 2014). Such models differ in their ground concepts and \nexplanatory mechanisms, but both assume a fundamental tenet: following each other, \ndrivers invariably attempt to couple, keeping safety distance. More recent models focus on \nthe spontaneous emergence of traffic jams that results from the properties of a system of \ninteracting vehicles (i.e., without bottlenecks). In an experimental setting Sugiyama et al., \n(2008) have successfully recreated the conditions that allow the observation of the typical \nsoliton wave going backwards through several car clusters. When certain speed, density \nand inter-vehicular distance join, so do traffic jams. Some of us have built upon these and \nother factors (e.g., wave movement in nature) exploring the mathematical properties of a \nsystem with three incognita that also needs three variables to be solved (Melchor & \nSánchez, 2014). Two canonical car-following techniques emerge as a consequence: \nDriving to keep safety Distance (DD) vs Inertia (DI). Also a basic question: can drivers \nactually understand and follow either way, or do they stick to a basic normative driving \nbehavior? This paper summarizes the results after three experimental studies done with a \ndriving simulator. Several performance measures from individual drivers (accelerations, \ndecelerations, average speed, distance to leader, and so on) were taken. As an overall \nindicator, results consistently announce in the three studies that DI trips consume less fuel \n(about 20%) than DD ones.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".