Wildfire Evacuation Scenario in Colorado

Abstract

In late 2010, the City of Colorado Springs and the Pikes Peak Area Council of Governments in Colorado completed a cooperative wildfire evacuation planning process. The process was supported by the simulation of wildfire evacuation scenarios with an adaptation of the Council of Governments’ four-step travel model. The adapted model was used to assess times to evacuate, identify choke points, and develop traffic control plans for identified at-risk neighborhoods. Evacuation simulations used a worst-case scenario in which the wildfire event took place during the p.m. peak traffic hour, and affected households were included in both background commuter traffic and neighborhood evacuation traffic. Link-based hourly volume-to-capacity ratios were used as a metric to estimate times to evacuate and to identify egress bottlenecks. Route closures, route restrictions, no-entry restrictions, and contraflow operations were evaluated as measures for inclusion in neighborhood-level traffic control plans. On Tuesday, June 26, 2012, the simulated worst case became reality. More than 34,000 persons were evacuated during the event, including 26,000 during the 6-h period between 15:30 and 21:30. Before the wildfire was fully contained, 18,247 acres had burned, 347 homes and other structures had been destroyed, and two people had died. That the no-notice, mandatory evacuation was as successful as it was can be attributed to extensive advance planning, the accuracy of modeled evacuation simulations, and the effectiveness of the final traffic control plans. This study examined how the model results were borne out by actual experience. Recommendations to improve no-notice evacuation planning from the perspective of lessons learned are presented.

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

“…However, much less work has focused on decisions during the evacuation process (e.g., route, destination) (11,12), as noted in (13). Other work, such as (14), notes that behavior such as extra-trip making, mobilization time, and background traffic can also impact evacuations. Moreover, alternative transportation strategies such as the sharing economy (e.g.…”

“…The initial research can be done as a desktop study, as the vegetation coverage map, the road network map, and other geospatial information can be readily obtained from sources such as the LANDFIRE program and OpenStreetMap. During the development stage, refinement can be made through meetings with the emergency responders and the wider community (14). Prior to wildfires, residents need to be informed of their zones, potentially through letters sent to homeowners and renters or announcements via online neighborhood hubs such as Nextdoor.…”

“…However, much less work has focused on decisions during the evacuation process (e.g., route, destination) ( 11 , 12 ), as noted in Kuligowski ( 13 ). Other work, such as de Araujo et al ( 14 ), notes that behavior such as extra-trip making, mobilization time, and background traffic can also affect evacuations. Moreover, alternative transportation strategies such as the sharing economy (e.g., Wong and Shaheen [ 15 ] and Wong et al [ 16 , 17 ]) may be feasible under certain wildfire evacuation circumstances, changing the potential modal split and sometimes increasing social equity.…”

“…Some examples in the literature offer more details of how the strategies can be implemented in a wildfire evacuation context. Contraflow was studied in de Araujo et al ( 14 ), where locations were determined by iteratively turning the excess road capacity in the opposite direction of road links. So et al ( 26 ) proposed a phased evacuation strategy where those closer to danger should leave first (Innermost First Out, InFO), while Chen and Zhan ( 27 ) tested all phase sequences in a road network with four evacuation zones.…”

The Mechanism of Crowd Stampede Based on Case Statistics through SNA Method

“…Previous research on influencing factors of stampede principally concentrates on statistics of accident types, emergencies, crowd panic, pedestrian falling, and other behaviour errors of accident subjects, and less attention has been paid to organizational factors that lead to these behavioural errors, namely, on-site transactions, crowd monitoring, and emergency plans [5][6][7][8]. Research on the triggering mechanisms of stampede accidents mostly uses computer modelling, simulation, and subjective analysis to introduce the time logic and evolution process of stampede into incident analysis and discusses the effect of humanmade errors on the triggering of trampling incidents from the micro level [9,10]. Existing research on crowd stampede generally prioritizes analysis of crowd-level behaviour errors in risk events and is insufficient in discussion on organizational-level management factors.…”