Optimization of Multimodal Evacuation of Large-scale Transportation Networks
Notice bibliographique
Résumé
The numerous man-made disasters and natural catastrophes that menace major communities accentuate the need for proper planning for emergency evacuation. Transportation networks in cities evolve over long time spans in tandem with population growth and evolution of travel patterns. In emergencies, travel demand and travel patterns drastically change from the usual everyday volumes and patterns. Given that most US and Canadian cities are already congested and operating near capacity during peak periods, network performance can severely deteriorate if drastic changes in Origin-Destination (O-D) demand patterns occur during or after a disaster. Also, loss of capacity due to the disaster and associated incidents can further complicate the matter. Therefore, the primary goal when a disaster or hazardous event occurs is to coordinate, control, and possibly optimize the utilization of the existing transportation network capacity. Emergency operation management centres face multi-faceted challenges in anticipating evacuation flows and providing proactive actions to guide and coordinate the public towards safe shelters. \nNumerous studies have contributed to developing and testing strategies that have the potential to mitigate the consequences of emergency situations. They primarily investigate the effect of some proposed strategies that have the potential of improving the performance of the evacuation process with modelling and optimization techniques. However, most of these studies are inherently restricted to evacuating automobile traffic using a certain strategy without considering other modes of transportation. Moreover, little emphasis is given to studying the interaction between the various strategies that could be potentially synergized to expedite the evacuation process. Also, the absence of an accurate representation of the spatial and temporal distribution of the population and the failure to identify the available modes and populations that are captive to certain modes contribute to the absence of multimodal evacuation procedures. Incorporating multiple modes into emergency evacuation has the potential to expedite the evacuation process and is essential to assuring the effective evacuation of transit-captive and special-needs populations .\nThis dissertation presents a novel multimodal optimization framework that combines vehicular traffic and mass transit for emergency evacuation. A multi-objective approach is used to optimize the multimodal evacuation problem. For automobile evacuees, an Optimal Spatio-Temporal Evacuation (OSTE) framework is presented for generating optimal demand scheduling, destination choices and route choices, simultaneously. OSTE implements Dynamic Traffic Assignment (DTA) techniques coupled with parallel distributed genetic optimization to guarantee a near global optimal solution. For transit evacuees, a Multi-Depots, Time Constrained, Pick-up and Delivery Vehicle Routing Problem (MDTCPD-VRP) framework is presented to model the use of public transit vehicles in evacuation situations. The MDTCPD-VRP implements constraint programming and local search techniques to optimize certain objective functions and satisfy a set of constraints. The OSTE and MDTCPD-VRP platforms are integrated into one framework to replicate the impact of congestion caused by traffic on transit vehicle travel times. \nA proof-of-concept prototype has been tested; it investigates the optimization of a multimodal evacuation of a portion of the Toronto Waterfront area. It also assesses the impact of multiple objective functions on emergency evacuation while attempting to achieve an equilibrium state between transit modes and vehicular traffic. Then, a large-scale application, including a demand estimation model from a regional travel survey, is conducted for the evacuation of the entire City of Toronto.\nThis framework addresses many limitations of existing evacuation planning models by: 1) synergizing multiple evacuation strategies; 2) utilizing robust optimization and solution algorithms that can tackle such multi-dimensional non deterministic problem; 3) estimating the spatial and temporal distribution of evacuation demand; 4) identifying the transit-dependent population; 5) integrating multiple modes in emergency evacuation. The framework presents a significant step forward in emergency evacuation optimization.
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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 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 ».