Improved Driveway Design for Superblocks to Reduce the Crash Risk
Bibliographic record
Abstract
The superblock has become a typical land use in China and many growing Asian cities. Superblock access points generate traffic congestion and many conflicts among all road users. Driveway design is a critical process and has a major impact on traffic conditions around superblocks. There are various guidelines for the two key factors for driveway design, driveway width and curb radius, but they provide reference values corresponding to traffic volume and speed; these are not sufficient for managing the complex traffic environment around superblocks. To improve driveway design, we develop detailed access point design models that account for conflicts between turning and through motorized vehicles, conflicts between motorized and nonmotorized traffic, speed differential larger than 10 km/h, and lane encroachments of entering and exiting vehicles. The crash risk models evaluate and optimize the combination of driveway width and curb radius with respect to three traffic safety indexes: traffic conflict, lane encroachment, and speed differential. A case study evaluation shows that the updated driveway design models produce a lower crash risk; seven of the ten driveways improved by 16.14% or more. The updated driveway design for superblocks would be beneficial for analyzing, permitting, and managing traffic operations at superblocks and oversized development with many and complex driveways.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".