Development of Inherent and Dynamic Resilience in Traffic Networks: Microscopic Simulation, Dynamic Stochastic Assignment and Bayesian Decision Models
Notice bibliographique
Résumé
This research is aimed at the identification of measures and the development of methods for the investigation of how to enhance inherent (static) and dynamic resilience. The need for inherent and dynamic resilience arises in order to cope with highly disruptive events with potential for major impacts on key arterials and corridors. Specifically, the objectives are (1) to define and test link/corridor-level means to enhance the inherent resilience in terms of sustained ability to serve traffic while resisting deterioration of quality of flow, (2) to develop and assess dynamic resilience measures that address dynamic and stochastic characteristics of traffic affected by a major disruptive event, and (3) to define decision-making guides for managing traffic under disruptive conditions. This original research covers the traffic service resilience, but it does not include the physical resilience of transportation infrastructure. As for the spatial scope, the studies of inherent resilience are meaningful at the link and corridor level. On the other hand, to define and assess dynamic resilience measures, corridor and network level studies are essential. Following the steps of problem definition, setting objectives, and recognizing scope of research, resilience measures are defined and methodologies are developed for assessing these measures. Based on microscopic level simulations, predictive models of link performance are developed and applied for the quantification of resilience improvement. Dynamic traffic assignment methods are developed at the macroscopic level due to the necessity to study dynamic resilience measures at the network level. Macroscopic models based on dynamic stochastic assignment are investigated and compared with user equilibrium assignment methods. Due to the uncertainties in traffic flow during highly disruptive events, Bayesian decision analysis method for assessing dynamic resilience actions is researched. The dynamic resilience actions encompass combinations of user-equilibrium and stochastic assignment methods. These methodological developments lead to defining linkages of dynamic resilience measures with traffic control. Finally, based on advances in methods and their applications, conclusions are presented and contributions to knowledge are noted. Products of new and original research reported can potentially serve as means to reduce impacts of major stochastic events.
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) | 0,001 | 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 ».