Redirigiendo al acceso original de articulo en 18 segundos...
Inicio  /  Applied Sciences  /  Vol: 13 Par: 11 (2023)  /  Artículo
ARTÍCULO
TITULO

An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management

Tanzina Afrin    
Lucy G. Aragon    
Zhibin Lin and Nita Yodo    

Resumen

Maintaining smooth traffic during disaster evacuation is a lifesaving step. Traffic resilience is often used to define the ability of a roadway during disaster evacuation to withstand and recover its functionality from disturbances in terms of traffic flow caused by a disaster. However, a high level of variances due to system complexity and inherent uncertainty associated with disaster and evacuation risks poses great challenges in predicting traffic resilience during evacuation. To fill this gap, this study aimed to propose a new integrated data-driven predictive resilience framework that enables incorporating traffic uncertainty factors in determining road traffic conditions and predicting traffic performance using machine learning approaches and various space and time (spatiotemporal) data sources. This study employed an augmented Long Short-Term Memory (LSTM)-based approach with correlated spatiotemporal traffic data to predict traffic conditions, then to map those conditions to traffic resilience levels: daily traffic, segment traffic, and overall route traffic. A case study of Hurricane Irma?s evacuation traffic was used to demonstrate the effectiveness of the proposed framework. The results indicated that the proposed method could effectively predict traffic conditions and thus help to determine traffic resilience. The data also confirmed that the traffic infrastructures along the US I-75 route remained resilient despite the disturbances during the disaster evacuation activities. The findings of this study suggest that the proposed framework is applicable to other disaster management scenarios to obtain more robust decisions for the emergency response during disaster evacuation.

 Artículos similares

       
 
Michaela Cellina, Maurizio Cè, Marco Alì, Giovanni Irmici, Simona Ibba, Elena Caloro, Deborah Fazzini, Giancarlo Oliva and Sergio Papa    
Digital twins are virtual replicas of physical objects or systems. This new technology is increasingly being adopted in industry to improve the monitoring and efficiency of products and organizations. In healthcare, digital human twins (DHTs) represent v... ver más
Revista: Applied Sciences

 
Yuqian Wu, Miao Wang, Wenkui Chu and Guoqing Wang    
Organization preference knowledge is critical to enhancing the intelligence and efficiency of the multi-platform aircraft mission system (MPAMS), particularly the collaboration tactics of task behaviors, platform types, and mount resources. However, it i... ver más
Revista: Aerospace

 
Matia Menichini, Linda Franceschi, Brunella Raco, Giulio Masetti, Andrea Scozzari and Marco Doveri    
In the context of climate change, the correct management of groundwater, which is strategic for meeting water needs, becomes essential. Groundwater modeling is particularly crucial for the sustainable and efficient management of groundwater. This manuscr... ver más
Revista: Water

 
Alireza Namdari, Maryam Asad Samani and Tariq S. Durrani    
Lithium-ion is a progressive battery technology that has been used in vastly different electrical systems. Failure of the battery can lead to failure in the entire system where the battery is embedded and cause irreversible damage. To avoid probable dama... ver más
Revista: Algorithms

 
Jeonghyeon Choi, Jeongeun Won, Suhyung Jang and Sangdan Kim    
Many studies have applied the Long Short-Term Memory (LSTM), one of the Recurrent Neural Networks (RNNs), to rainfall-runoff modeling. These data-driven modeling approaches learn the patterns observed from input and output data. It is widely known that t... ver más
Revista: Water