A novel approach to investigate pedestrian safety in non-signalized crosswalk environments and related treatments using trajectory data
Bibliographic record
Abstract
The high frequency of pedestrian crash injuries and the great possibility of fatal consequences have made pedestrian safety a great focus in road safety research. Pedestrian safety becomes even more of a problem at non-signalized locations when compared to signalized crosswalk locations, due to the absence of traffic lights controlling the traffic. This dissertation aims to improve data collection methods for pedestrian safety analysis and to develop a methodological framework to investigate pedestrian safety at non-signalized crosswalk locations.The work started with reviewing methodologies and data collection methods in previous studies. Methods used in past studies were classified into five different approaches: the crash data approach and four surrogate safety approaches (the traffic data approach, the conflict event approach, the behavioral analysis approach, and the perception analysis approach). Issues in the methodologies applied, and data used were summarized. To overcome the limitations that regular visible spectrum cameras have encountered during the data collection process, the thermal camera was introduced and its performance in road user detection, classification, and speed measurement was validated through its comparison to the use of the regular camera. Results showed an evidently better performance from thermal camera for low visibility and shadow conditions. For speed measurements, the thermal camera was consistently more accurate than the regular camera at daytime and nighttime. A study was conducted to investigate pedestrian safety at crosswalks at nighttime using existing measures. Although, the methodology performed well, further limitations of using safety measure methods were discovered upon the completion of the study. A novel framework which looks at the interaction between the pedestrian and the vehicle, and their behavior during the interactions, was proposed and illustrated through a case study. The framework was further tested through a study to compare the performance of three main non-signalized crosswalk types (uncontrolled, marked, and stop sign controlled crosswalks) on pedestrian safety in Montreal. Stop sign controlled crosswalks had the best performance in protecting pedestrians while uncontrolled crosswalks performed the worst. To explore the extensive applications of the framework, the investigation of cyclist-pedestrian interactions was introduced as it has been a major but underestimated safety problem. Marked crosswalks alone fail to protect pedestrians from passing cyclists. Besides, pedestrian safety at crossings on cycling facilities with downhill grades was found to be a great issue. The dissertation will: provide a comprehensive literature review that acts as a practical reference to investigating pedestrian safety at non-signalized crosswalk locations; introduce a promising alternative, the thermal camera, to overcome the limitations of using the visible camera for automated traffic data collection; propose a new framework that describes pedestrian-vehicle interactions more precisely, compared to previous studies. This framework is promising for different purposes in road safety on various topics, such as the analysis of interactions between different types of road users, road user interaction simulation, safety treatments validations, and the performance evaluations of autonomous vehicles.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".