Towards Safer Pedestrians: A Framework for Analyzing and Mitigating Pedestrian Violations and related Safety Issues
Bibliographic record
Abstract
Active models of travel, particularly walking, are an integral part of the multi-modal transportation system in urban areas. Walking provides numerous benefits at the individual and community levels (e.g., health benefits, reducing traffic congestion, emissions, and energy consumption). Nevertheless, safety concerns represent a major roadblock to the optimal utilization of walking as a key mode of travel. Pedestrians are among the most Vulnerable Road Users (VRUs) who are at a higher risk of being killed or severely injured as a result of road collisions. Previous research shows that many pedestrian behaviours could increase the risk of collisions significantly. Pedestrian violations, either temporal or spatial, stand as one of the riskiest behaviours that impact pedestrian safety. However, investigating such behaviour and quantifying its impact on safety are scarce in the literature. Accordingly, this research aims at developing a comprehensive framework to analyze pedestrian violations and understand when and where they can lead to collisions. To address these goals, the research utilized historical records of collisions that involve pedestrian violations. State-of-the-art statistical models (Copula models, Bayesian Structural Equation Modelling), Machine Learning techniques (Latent Class Analysis clustering), and Deep Learning methods (Self-Organizing Map) were applied to understand the factors contributing to such collisions on the micro-level (intersection and mid-blocks) and macro-levels (traffic analysis zones) and understand the characteristics of locations that experience a high frequency of those collisions. Additionally, a novel approach (dynamic R-vine copula-based time series model) was proposed to analyze the efficiency of pedestrian safety treatments that are implemented as part of vision zero programs. This approach enables the accurate assessment of the treatments, identifying the most effective combination of treatments, and investigating the association between area characteristics and treatment combination performance. Overall, this dissertation provides a solid understanding of pedestrian violations and safety for decision-makers, safety practitioners, and academia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".