Wildfires have become a perpetual crisis for communities across California. For life-threatening wildfires, mass evacuation often becomes the only viable option to protect lives. Yet, looking back at recent events, including the devastating 2018 Camp Fire in Northern California, there are significant challenges associated with the evacuation process, such as multi-agency coordination, agency-resident communication, and management of extraordinarily high amounts of traffic within a short period of time. Currently, emergency planners use evacuation models that are typically based on existing traffic simulation models; however, it is increasingly clear that other factors need to be considered, such as fire progression and communication systems. To address this gap, UC Berkeley researchers constructed a framework and set of models that include the combined impacts of three dynamic processes on evacuations – fire progression, communication systems, and traffic flow. The framework and models were applied to two case studies in California: the town of Paradise and the unincorporated community of Bolinas. In the Paradise case, the scenarios were based on the 2018 Camp Fire event. For the Bolinas case, the scenarios were based on hypothetical wildfire events.

In the event of a wildfire, government agencies need to make quick, well-informed decisions to safely evacuate people. Small communities, such as in Marin County, with a mix of residences and flammable vegetation in Wildland-Urban Interface zones tend to lack resources to conduct evacuation studies. Consequently, this study uses a framework of wildfire and traffic simulations to test the performance of potential evacuation strategies, including reducing the volume of evacuating vehicles through car-pooling, phasing evacuations by staggering evacuation times by zone, and prohibiting street parking in four representative areas of Marin County. Results show that reducing vehicle numbers lowers the average travel time by 20%-70% and average exposure time to wildfire by 27%-60% from the baseline. Phased evacuations with suitable time intervals lower the average travel time by 13.5%-70%, but may expose more vehicles to fire in some situations. Prohibiting street parking yields varying results due to different numbers of exits and evacuees. In some cases, prohibiting street parking reduces the average travel time by over 50%, while in other cases it only reduces the average travel time by 9%, contributing little to evacuation efficiency. Altogether, Marin County may want to consider developing a communication and parking plan to reduce the number of evacuating vehicles in wildfire situations. Phased evacuation is also highly recommended, but the suitable phasing interval depends on the speed of fire spread and number of evacuees. Further, whether to establish street parking prohibition policies for a certain area depends on the number of exits and the number of vehicles on the streets.

Located within the seismically active Pacific Ring of Fire, California's transportation infrastructure, especially in the Bay Area, is susceptible to earthquakes. A review of current research and stakeholder interviews revealed a growing awareness of emergency preparedness among local jurisdictions and transit agencies in recent years. However, many have yet to formalize and publish their recovery plans. This study introduces an agent-based multimodal transportation simulation tool to enhance post-earthquake transportation resilience. Integrating a road network simulator with a metro system simulator, the tool employs an optimized Dijkstra-based algorithm to calculate optimal routes, travel times, and fares. A case study is conducted for the East Bay, using the simulator to gauge the impact of a compromised Bay Area Rapid Transit (BART) system. The results suggested that original BART passengers could face either longer commute times or higher costs during the recovery phase of a major earthquake without appropriate policies. Such outcomes could disproportionately burden low-income riders, affecting their mobility and overall travel time.

In recent years, “smart city” technologies have emerged that allow cities, counties, and other agencies to manage their infrastructure assets more effectively, make their services more accessible to the public, and allow citizens to interface with new web-and mobile-based alternative service providers. This project developed an innovative user-friendly web interface for local and state policymakers that tracks and displays information on the adoption of such technologies in California across the policing, transportation, and water and wastewater sectors for a comprehensive set of local service providers: connectedgov.berkeley.edu. Contrary to conventional smart city indices, our platform allows users to view rates of adoption in maps that attribute adoption to the local public agencies or service providers actually procuring or regulating the technologies in question. Users can construct indices or view technologies one by one. Users can also explore the relationship between technology adoption and local service area conditions and demographics, or download the raw data and scripts used to collect it. This report illustrates the utility of the data we have collected, and the analytics one can perform using our web interface through an analysis of the rollout of three technologies in the transportation sector: electric vehicle (EV) chargers, transportation network company (TNC) service areas, and micromobility services across California.

Many small, resource-strapped communities located in areas vulnerable to wildfire don’t have resources to conduct dedicated evacuation studies and many do not consider the impact of background traffic (i.e., normal traffic rather than evacuating traffic) on evacuation. In response, we explored the performance of several generalizable evacuation strategies with background traffic for representative communities in Marin County, including the Ross Valley, Woodacre Bowl, Tamalpais Valley, and an area near Highway 101 and Ignacio Boulevard in Novato (hereafter referred to as ‘Novato Neighborhood’). The strategies we explored include vehicle reduction (i.e., evacuees share a vehicle), phased evacuation (i.e., evacuees in different zones have different departure times), and off-street parking (i.e., street parking is prohibited on a high-fire Red Flag Day to increase overall road capacity in the event of an evacuation). We then tested each strategy using a wildfire-traffic simulation framework.