Measuring Transportation Network Performance During Emergency Evacuations: A Case Study of Hurricane Irma and the Camp Fire

The United States has witnessed several natural disasters in recent memory. Natural disasters such as hurricanes, wildfires, and floods not only cause extensive monetary damages but also lead to spatiotemporal displacement of affected residents. Often, in these scenarios, governments at various levels – state/regional/local – grapple with how to effectively evacuate those affected while ensuring their safe relocation, and minimal risk. Major roads such as the interstates often suffer from heavy gridlock during such evacuations leading to bottleneck formulation and slow traffic speeds due to the high volume, and demand of vehicles. Resource shortages such as fuel and water further intensify the risk of evacuations. During an impending hurricane or wildfire, it is critical that public authorities have a complete understanding of the traffic characteristics before deciding to execute emergency evacuations. This project will utilize Big Data to investigate in detail evacuation operations undertaken during Hurricane Irma in Florida (2017) and the Camp Fire in California(2018) to analyze temporal and spatial traffic patterns and assess the performance of the transportation network. An examination of the evacuation traffic patterns, and travel time during said events will serve as an important baseline to benefit emergency planning and management in areas with similar circumstances. This study is timely due to the nature of these natural disasters and their widespread impacts in the states of Florida, and California.