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• W3199147943 abstract "• An adaptive reinforcement learning model is developed to learn human travel behaviors in regular situations. • The model is capable of simulating the traffic conditions and evaluate the perturbations of disasters on urban mobility. • A case study of the 2017 Hurricane Harvey in Houston is conducted to examine the application of the model. • The model and outcomes of applications can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility. 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 evaluating 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. Employing millions of trajectory data from INRIX, the application of the proposed model is shown in the context of Houston and the flooding scenario caused by Hurricane Harvey in August 2017. The results show that the model can achieve more than 76% precision and recall at the model learning stage. The mean percentage error of the travel distance in predicted trajectories is 4.29%, compared to the travel distances in empirical data. In addition, predicted density of vehicles in grid cells are negatively associated with the traffic speed on road segments and inundation intensity in grid cells during the flooding. The results from the simulation further show that the model could predict traffic patterns and congestion resulting from urban flooding. The outcomes of the analysis demonstrate the capabilities of the model for analyzing urban mobility during crises, which can inform the public and decision-makers about the response strategies and resilience planning to reduce the impacts of crises on urban mobility." @default.
• W3199147943 created "2021-09-27" @default.
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• W3199147943 date "2021-12-01" @default.
• W3199147943 modified "2023-10-10" @default.
• W3199147943 title "Evaluating crisis perturbations on urban mobility using adaptive reinforcement learning" @default.
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• W3199147943 doi "https://doi.org/10.1016/j.scs.2021.103367" @default.
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