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Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises by Fan, Chao; Jiang, Xiangqi; Mostafavi, Ali is a scholarly article available to read on EtoBox.

What is Adaptive Reinforcement Learning Model for Simulation of Urban Mobility during Crises about?

The objective of this study is to propose and test an adaptive reinforcement learning model that can learn the patterns of human mobility in a normal context and simulate the mobility during perturbations caused by crises, such as flooding, wildfire, and hurricanes. Understanding and predicting human mobility patterns, such as destination and trajectory selection, can inform emerging congestion and road closures raised by disruptions in emergencies. Data related to human movement trajectories are scarce, especially in the context of emergencies, which places a limitation on applications of existing urban mobility models learned from empirical data. Models with the capability of learning the mobility patterns from data generated in normal situations and which can adapt to emergency situations are needed to inform emergency response and urban resilience assessments. To address this gap, this study creates and tests an adaptive reinforcement learning model that can predict the destinations of movements, estimate the trajectory for each origin and destination pair, and examine the impact of perturbations on humans' decisions related to destinations and movement trajectories. The applic

Author

Fan, Chao; Jiang, Xiangqi; Mostafavi, Ali

Published

2020

Language

EN