Analyzing Speed Disparity in Mixed Vehicle Technologies on Horizontal Curves
Bibliographic record
Abstract
Vehicle technologies are rapidly evolving due to their multidimensional advantages. Different vehicle technologies like connectivity and automation on the same roads with the driver-controlled vehicles in an environment of mixed vehicle technologies may increase speed disparity. Previous studies have indicated that speed disparity measures and traffic collisions are inextricably related. Therefore, identifying the relationships between speed distribution measures of different vehicles and road geometry can help identify instances of speed disparity resulting from mixed vehicle technologies and identify the effectiveness of potential countermeasures. This research used speed prediction models in three different regions to analyze the speed behaviour of connected vehicles (CVs), automated vehicles (AVs) and vehicles with no connectivity or automation (NCVs) on horizontal curves on freeways and major arterials. Safety Pilot Model Deployment (SPMD) dataset in Michigan, USA were used to model CV speed behaviour at various points on horizontal curves in major arterials and freeways. Because the data involved repeated trips by same drivers in their instrumented vehicles, LME modelling was utilized in model development. In addition, speed data collected on major arterials and freeways in Ontario, Canada, using instrumented NCVs were used to develop similar LME models for NCVs. This research also used AVs speed prediction model available in the literature to model the speed behaviour of AVs on horizontal curves in Spain. A methodology is then presented to estimate speed disparity measures on horizontal curves with no control and with different control strategies or countermeasures. The results indicated that the mixed vehicle environment can cause the combined standard deviation of all vehicles (σ_c) to considerably increase on arterials and freeways under no control, which would in turn increase the collision experience. Countermeasures were proposed, which showed the potential in reducing σ_c and reduce the negative safety impacts of the mixed vehicle environment. Sensitivity analysis confirmed that speed compliance rates of the driver operators of CVs and NCVs with the speed advisories in the proposed countermeasures have insignificant impact on the speed disparity measure σ_c.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".