activity
20202022
most citedQuantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas

8 citations · 14 across the 5 of their papers we have counts for

collaborators

6 papers

physics.soc-ph20223 cited

Anatomy of Perturbed Traffic Networks during Urban Flooding

Akhil Anil Rajput, Sanjay Nayak, Shangjia Dong +1

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 give…

physics.soc-ph20228 cited

Quantitative Measures for Integrating Resilience into Transportation Planning Practice: Study in Texas

Cheng-Chun Lee, Akhil Rajput, Chia-Wei Hsu +10

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 he…

cs.AI2021

Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning

Zhenning Li, Hao Yu, Guohui Zhang +2

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…

cs.NI2021

Empirical Optimization on Post-Disaster Communication Restoration for Social Equality

Jianqing Liu, Shangjia Dong, Thomas Morris

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 th…

physics.soc-ph2020

Macroscopic and Microscopic Characteristics of Networks with Time-variant Functionality for Evaluating Resilience to External Perturbations

Xinyu Gao, Shangjia Dong, Ali Mostafavi +1

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 pertu…

eess.SP20203 cited

A Hybrid Deep Learning Model for Predictive Flood Warning and Situation Awareness using Channel Network Sensors Data

Shangjia Dong, Tianbo Yu, Hamed Farahmand +1

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 Net…