Modeling driver distance recognition and speed perception at night for freeway speed limit selection in china
Bibliographic record
Abstract
Developing a proper speed limit for freeway is critical for roadway safety. Due to the difference in vis-ibility between day and night, it is necessary to have different speed limits for the two time periods on freeways with changing geometric features. Aiming to reduce the number of crashes caused by speed-ing at night on freeways, an exploratory study was conducted on the maximum speed limit at night. In order to investigate the potential relationship between drivers ’ distance recognition and driving speed and between speed perception and driving speed under different geometric design features, an experi-ment was carried out on a 22-km-long freeway segment on Chang-song freeway in China. Based on round-trips made by 10 drivers during day and night on this segment, drivers ’ recognition distance (distance between a sign and the location where the sign was clearly recognized the fi rst time) and estimated speed were recorded. The data analysis results show that driver recognition distance at night decreases by about 7 % compared with recognition distance at daytime. The accuracy of driver speed perception at nighttime is only 29%, whereas it is 67 % at daytime. With the collected data, several mul-tivariate non-linear regression models were established to capture the relationship among the variables of recognition distance, estimated speed at night, driving speed, and highway alignment indexes. Then the modeling results were used to develop the speed limit model by physical equations. A case study is introduced at the end of the paper.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".