Methodology

OUR

Methodology

Microscopic traffic simulations of wildfire evacuation scenarios

An open-source microscopic traffic simulator, Simulation of Urban Mobility (SUMO), is used with scalability in mind for wildfire evacuation simulations. It incrementally combines road network data and road closures caused by wildfires to update the routes that vehicles will take, based on information about their origin and destination locations. This approach also accounts for stochastically changing roadway capacities and specific roadway sections. For each wildfire scenario, this simulation step will be repeated multiple times to obtain a probabilistic distribution of egress times and the number of isolated vehicles.

Seamless up-to-date precise road network data

We employ detailed road network datasets in our microscopic traffic simulator. To construct seamless, up-to-date, and precise road network data for accurate evacuation simulations, we utilize computer vision and machine learning methods with street-level images and high-resolution remotely sensed data.

People

Spatially explicit and detailed population information is critical for accurate wildfire evacuation simulations. We use census data from the U.S. Census Bureau to estimate refined population density and evacuation demands during wildfire events. Our approach employs not only current population data but also projections of future populations.

Wildfire environments

Spatially explicit and detailed population information is critical for accurate wildfire evacuation simulations. We use census data from the U.S. Census Bureau to estimate refined population density and evacuation demands during wildfire events. Our approach employs not only current population data but also projections of future populations.