Before and after traffic safety evaluations using computer vision techniques
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Notice bibliographique
Résumé
Traditionally, road safety analysis has been undertaken using historical collision records. This approach to road safety analysis is reactive in that the analyst has to wait for collisions to take place before an action can be taken. An alternative approach is to study traffic conflicts or near misses which occur more frequently, can be clearly observed and are related to collisions. However, there are issues of subjectivity, reliability, and cost associated with the use of human observers. The use of computer vision techniques to automate the process of collecting traffic conflicts data can help mitigate these problems. This thesis presents the results of a before-after safety evaluation of a proposed design for channelized right-turn lanes. The evaluation uses an automated safety analysis approach to identify and measure the severity of traffic conflicts. The new design, termed “Smart Channels”, decreases the angle of the channelized right turn to approximately 70 degrees, and is considered to have safety benefits for both vehicle-pedestrian and vehicle-vehicle interaction. Data for three treatment sites and one control site, located in British Columbia, Canada, are evaluated using automated traffic conflict analysis that relies on computer vision for conflict detection. The results of the evaluation show that the implementation of the right-turn treatment has resulted in a considerable reduction in the severity and frequency of merging, rear-end, and total conflicts. The total average hourly conflict was reduced by a statistically significant 51 percent, while the average conflict severity was reduced by a statistically significant 41 percent. Many different traffic conflict indicators have been proposed and studied, but the methods of combining the results has not been well examined. This thesis considers four conflict indicators and examines methods of combining or aggregating the information provided by each indicator in order to better account for all components of risk in traffic conflicts. The four indicators are time-to-collision, gap-time, deceleration-to-safety time, and post-encroachment time. Two primary aggregation methods are studied: time aggregation and road-user aggregation. Time aggregation is appropriate for determining aggregate severity over periods of time, and road-user aggregation is used for normalizing risk to the volume of users.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle