Developing Transportation Response Strategies for Wildfire Evacuations via an Empirically Supported Traffic Simulation of Berkeley, California

Abstract

Government agencies must make rapid and informed decisions in wildfires to evacuate people safely. However, current evacuation simulation tools for resource-strapped agencies largely fail to compare possible transportation responses or incorporate empirical evidence from past wildfires. Consequently, this study employs online survey data from evacuees of the 2017 Northern California Wildfires ( n = 37), the 2017 Southern California Wildfires ( n = 175), and the 2018 Carr Wildfire ( n = 254) to inform a policy-oriented traffic evacuation simulation model. The simulation is tested for a hypothetical wildfire evacuation in the wildland-urban interface of Berkeley, California. The study focuses on variables including fire speed, departure time distribution, towing of items, transportation mode, GPS-enabled rerouting, phased evacuations (i.e., allowing higher-risk residents to leave earlier), and contraflow (i.e., switching all lanes away from danger). It was found that reducing evacuating household vehicles (i.e., to one vehicle per household) and increasing GPS-enabled rerouting (e.g., 50% participation) lowered exposed vehicles (i.e., total vehicles in the fire frontier) by over 50% and evacuation time estimates (ETEs) by about 30% from baseline. Phased evacuations with a suitable time interval reduced exposed vehicles most significantly (over 90%) but produced slightly longer ETEs. Both contraflow (on limited links because of resource constraints) and slowing fire speed were effective in lowering number of exposed vehicles (around 50%), but not ETEs. Extended contraflow can reduce both exposed vehicles and ETEs. It is recommended that agencies develop a communication and parking plan to reduce the number of evacuating vehicles, create and communicate a phased evacuation plan, and build partnerships with GPS-routing services.

How to Measure the Network Vulnerability of Cities to Wildfires: Cases in California, U.S.A.

“…To make the simulation more realistic, vehicles are made to periodically and dynamically reroute depending on updated traffic situations, as in various related studies (4,5,(7)(8)(9)(10). Vehicles can get traffic information and notifications from the government through radio, variable message signs, and smartphones.…”

“…For departure time, numerous studies have considered various scenarios depending on the distribution of departure time—A departure time in one study follows the Poisson distribution—and agencies have plans such as communications of evacuation orders and phased evacuation ( 4 , 5 , 7 , 11 ). Even if most of the agencies have evacuation plans to respond to wildfires, not all plans can operate appropriately because of faster than expected propagation of wildfires, as in the example of the Camp Fire in Paradise in 2018 ( 4 , 6 , 10 ).…”

“…Moreover, even if such infrastructure works, it is difficult to update people in real-time on randomly closed links and where traffic is highly concentrated. As a result, vehicles may not get correct information every minute about their fastest routes to exit nodes ( 4 , 5 ). To investigate the value of information, the following two cases with different levels of information are considered.…”

“…Most studies selected study sites where wildfire cases occurred or that were located in wildfire-prone regions. Various scenarios were distinguished according to which roads are closed as a result of debris and fire-induced hazard, whether residents follow phased evacuation or not (no-notice evacuation), and where the fire initially ignited ( 4–6 , 10–12 ). Some studies have applied different scenarios to individual cities ( 4 , 10 ).…”

“…Simulation results were calculated using various metrics, including the number of isolated vehicles (IV), evacuation time estimates (ETE), delay time, edge speed, queue length, and total and average distance. Especially, ETE has been widely used for scenario evaluations ( 5 , 13 ).…”