Yao, Kaisen, authorChen, Suren, advisorHeyliger, Paul, committee memberAtadero, Rebecca, committee memberBradley, Thomas, committee member2024-01-012024-01-012023https://hdl.handle.net/10217/237420Global urbanization has triggered increasing demands on modern transportation infrastructure systems by the growing density of population and intensity of human activities in the cities. A great challenge has emerged in recent decades in terms of making urban communities more resilient against physical, social, and economic disruptions. As the backbones of urban communities, road networks are expected to provide essential functionality under various disruptions caused by different extreme events and incidents, such as natural hazards, pandemics, and crashes. In response to the existing challenges, this dissertation research aims to develop multi-scale urban transportation resilience modeling techniques and adaptive intersection intervention strategy against disruptions under hazards. Specifically, this dissertation will (1) identify the appropriate simulation platform for microscopic traffic analysis and intervention under hazardous and disrupted scenarios; (2) develop microscopic flow-based and graph-based urban traffic network modeling techniques under typical disruptions; (3) propose resilience-based performance indexes in both global network and local scales of disrupted traffic systems; (4) develop new traffic speed forecasting techniques considering data disruptions using deep learning technology to offer robust traffic performance forecasting during hazards; and (5) finally establish time-progressive traffic resilience forecasting strategy during hazardous weather to support proactive intervention.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.Text