Network-Level Safety Prediction Models for Long-range Transportation Planning
Notice bibliographique
Résumé
This research develops a holistic, proactive approach for integrating traffic safety into transportation planning to effectively evaluate the network-level safety impacts of transportation plans and policies. The research enhances the regional transportation model (RTM) framework by incorporating a network-based collision prediction model (NCPM) as a fifth step in the traditional four-step RTM modelling structure, allowing the model to predict the number of collisions on major and local roads at the planning stage. In addition to traditional estimates of traffic demand, the developed integrated RTM-NCPM framework also predicts the number of collisions for base and future planning horizons. The obtained results show promise in their ability to replicate observed collision frequencies and types in the base year, pointing to the robustness of the proposed framework. The approach developed in this thesis was rigorously evaluated for various future scenarios using the City of Calgary’s RTM as a case study. A sensitivity analysis was conducted to test model performance under several congestion pricing and transit fare policies. The RTM-NCPM framework showed a decrease in Property Damage only (PDO) and fatal and injury (FI) collisions of 13% and 6%, respectively, on local roads and of 8.5% and 8.6%, respectively, on major roads when fuel price was doubled. PDO and FI collisions decreased by 8% and 5%, respectively, on local roads and by 4.1% and 3.6%, respectively, on major roads when parking costs were doubled. When transit fares were reduced by half, PDO and FI collisions decreased by 5% and 2%, respectively, on local roads and by 3.8% and 3.4%, respectively, on major roads. The research develops a system dynamics (SD) modelling approach that models future impacts of autonomous vehicles (AVs) on the number of collisions. This approach captures the complex interactions resulting from the introduction of AVs while taking inputs from the RTM model. The developed model was used to examine the effectiveness of potential future AV-related policies and scenarios in reducing collisions. These scenarios and policy changes include: higher AV penetration rates, shared AVs with higher passenger occupancy, and improvements to sensing and communication technologies. The SD model for a scenario with shared autonomous vehicles (SAVs) with an average occupancy rate of 1.4 showed an increase in collisions through the year 2060, followed by a decrease in collisions. This scenario’s results suggested that the extreme assumption regarding the highest level of SAV mode share, with an average occupancy rate of 12 and a total shift of AVs to SAVs, would result in the lowest number of collisions compared to other scenarios.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».