Zobrazeno 1 - 10
of 129
pro vyhledávání: '"Dong, Shangjia"'
Household and individual-level sociodemographic data are essential for understanding human-infrastructure interaction and policymaking. However, the Public Use Microdata Sample (PUMS) offers only a sample at the state level, while census tract data o
Externí odkaz:
http://arxiv.org/abs/2407.01643
Urban flooding disrupts traffic networks, affecting the mobility and disrupting access of residents. Since flooding events are predicted to increase due to climate change, and given the criticality of traffic networks, understanding the flood-caused
Externí odkaz:
http://arxiv.org/abs/2210.00403
Autor:
Lee, Cheng-Chun, Rajput, Akhil, Hsu, Chia-Wei, Fan, Chao, Yuan, Faxi, Dong, Shangjia, Esmalian, Amir, Farahmand, Hamed, Patrascu, Flavia Ioana, Liu, Chia-Fu, Li, Bo, Ma, Junwei, Mostafavi, Ali
The objective of this study is to propose a system-level framework with quantitative measures to assess the resilience of road networks. The framework proposed in this paper can help transportation agencies incorporate resilience considerations into
Externí odkaz:
http://arxiv.org/abs/2205.02758
Autor:
Yuan, Faxi, Fan, Chao, Farahmand, Hamed, Coleman, Natalie, Esmalian, Amir, Lee, Cheng-Chun, Patrascu, Flavia I., Zhang, Cheng, Dong, Shangjia, Mostafavi, Ali
Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneou
Externí odkaz:
http://arxiv.org/abs/2111.06461
Autor:
Yuan, Faxi, Mobley, William, Farahmand, Hamed, Xu, Yuanchang, Blessing, Russell, Dong, Shangjia, Mostafavi, Ali, Brody, Samuel D.
The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. Predictive flood monitoring of road network flooding status plays an essential role in
Externí odkaz:
http://arxiv.org/abs/2108.13265
Publikováno v:
Transportation Research Part C: Emerging Technologies Volume 125, April 2021, 103059
Inefficient traffic control may cause numerous problems such as traffic congestion and energy waste. This paper proposes a novel multi-agent reinforcement learning method, named KS-DDPG (Knowledge Sharing Deep Deterministic Policy Gradient) to achiev
Externí odkaz:
http://arxiv.org/abs/2104.09936
Disasters are constant threats to humankind, and beyond losses in lives, they cause many implicit yet profound societal issues such as wealth disparity and digital divide. Among those recovery measures in the aftermath of disasters, restoring and imp
Externí odkaz:
http://arxiv.org/abs/2103.10582
Knowledge of time-variant functionality of real-world physical, social, and engineered networks is critical to the understanding of the resilience of networks facing external perturbations. The majority of existing studies, however, focus only on the
Externí odkaz:
http://arxiv.org/abs/2006.09574
The objective of this study is to create and test a hybrid deep learning model, FastGRNN-FCN (Fast, Accurate, Stable and Tiny Gated Recurrent Neural Network-Fully Convolutional Network), for urban flood prediction and situation awareness using channe
Externí odkaz:
http://arxiv.org/abs/2006.09201
Publikováno v:
In Sustainable Cities and Society October 2023 97
