An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management
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...
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| Formaat: | Artikel |
| Taal: | English |
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MDPI AG
2023-06-01
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| Reeks: | Applied Sciences |
| Onderwerpen: | |
| Online toegang: | https://www.mdpi.com/2076-3417/13/11/6850 |
| _version_ | 1827739695865397248 |
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| author | Tanzina Afrin Lucy G. Aragon Zhibin Lin Nita Yodo |
| author_facet | Tanzina Afrin Lucy G. Aragon Zhibin Lin Nita Yodo |
| author_sort | Tanzina Afrin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T03:10:50Z |
| format | Article |
| id | doaj.art-b50cbe9d66e644f199d6f7e9b51bd9ea |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:10:50Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-b50cbe9d66e644f199d6f7e9b51bd9ea2023-11-18T07:37:37ZengMDPI AGApplied Sciences2076-34172023-06-011311685010.3390/app13116850An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic ManagementTanzina Afrin0Lucy G. Aragon1Zhibin Lin2Nita Yodo3Department of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58102, USADepartment of Engineering, Pontifical Catholic University of Peru, Lima 15088, PeruDepartment of Civil, Construction, and Environmental Engineering, North Dakota State University, Fargo, ND 58102, USADepartment of Industrial and Manufacturing Engineering, North Dakota State University, Fargo, ND 58102, USAMaintaining 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.https://www.mdpi.com/2076-3417/13/11/6850resiliencedata-drivenLSTMtraffictransportationdisaster |
| spellingShingle | Tanzina Afrin Lucy G. Aragon Zhibin Lin Nita Yodo An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management Applied Sciences resilience data-driven LSTM traffic transportation disaster |
| title | An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management |
| title_full | An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management |
| title_fullStr | An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management |
| title_full_unstemmed | An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management |
| title_short | An Integrated Data-Driven Predictive Resilience Framework for Disaster Evacuation Traffic Management |
| title_sort | integrated data driven predictive resilience framework for disaster evacuation traffic management |
| topic | resilience data-driven LSTM traffic transportation disaster |
| url | https://www.mdpi.com/2076-3417/13/11/6850 |
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