Influence of demand and capacity in transportation simulations of short-notice, distant-tsunami evacuations
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Abstract
Distant tsunamis require short-notice evacuations in coastal communities to minimize threats to life safety. Given the available time to evacuate and potential distances out of hazard zones, coastal transportation planners and emergency managers can expect large proportions of populations to evacuate using vehicles. A community-wide, short-notice, distant-tsunami evacuation is challenging because it creates a sudden, significant, and concentrated demand on road-network systems. Transportation planners and emergency managers need methods to help them determine if a road network can handle an evacuation surge and if not, where interventions can best reduce overall clearance times. We use the coastal community of Bay Farm Island (City of Alameda, California, USA) and the distant-tsunami threat posed by Aleutian-Alaskan earthquakes as a case study to explore the use of agent-based, transportation simulation to support short-notice, tsunami-evacuation planning. Results demonstrate how vehicle simulation can characterize network performance during a tsunami evacuation in the absence of real-world measurements of vehicle demand and flow. Changes in vehicle demand had the greatest influence on reductions in clearance times and recommended reductions varied based on time of day. Doubling the capacity of certain road segments based on traditional vehicle-capacity ratios and level-of-service thresholds reduced overall clearance time in some cases but increased it in other cases. The proposed simulation approach can serve as an analytical foundation for future efforts to characterize distant-tsunami evacuations in other coastal communities throughout the world.
Study Area
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Influence of demand and capacity in transportation simulations of short-notice, distant-tsunami evacuations |
| Series title | Transportation Research Interdisciplinary Perspectives |
| DOI | 10.1016/j.trip.2020.100211 |
| Volume | 7 |
| Year Published | 2020 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Western Geographic Science Center |
| Description | 100211, 14 p. |
| Country | United States |
| State | California |
| Other Geospatial | San Francisco Bay |
| Google Analytic Metrics | Metrics page |