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.

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For U.S. metropolitan planning organizations (MPOs), obtaining, preparing, and validating socioeconomic forecasts are fundamental tasks to ensure that logical, consistent, and approved population and employment data are provided to the travel demand models. This process is enhanced if the MPO includes both transportation and land use inputs into the forecast process. To fulfill these desires, the Pikes Peak Area Council of Governments (the MPO in Colorado Springs, Colorado) decided to use TELUM–-transportation, economic, and land use model software–-to conduct the land use forecasting task. TELUM was developed by Stephen Putman and the New Jersey Institute of Technology (NJIT) as part of the transportation, economic, and land use system sponsored by FHWA. With assistance from NJIT, MPO staff serving this 500,000-plus person region calibrated the land use model for the 2000 to 2005 period and then used TELUM to produce land use forecasts for six 5-year increments between 2005 and 2035. The Colorado Springs region has a unique character, including a workforce that is 11% active military personnel, a diverse employment profile including high-tech manufacturing and a large recreation-based workforce, and a growing retiree population. Additionally, the MPO area includes a dense urban area, large exurban developments, rugged mountain areas, and frontier grasslands, all of which challenged the technical staff that worked on model development. The technical and policy insights and lessons learned from the TELUM application in Colorado Springs have transferable value for all sizes of MPOs faced with forecasting development and transportation.

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The evaluation and prioritization of highway segments is a key task in the allocation of transportation planning resources at metropolitan planning organizations (MPOs). The network robustness index (NRI) is a new and evolving transportation planning measure that is based on changes in travel time that result from the removal of one highway link segment. In Colorado Springs, Colorado, the MPO applied the NRI measure, first on the National Highway System (NHS) and then on a small set of highway project links. The use of the NRI on the NHS made clear a ranking system for high-level criticality in the region, with four categories emerging: low, medium, high, and critical. This criticality assessment function of the NRI is effective in road segment evaluation for evacuation, resilience, and emergency preparedness. The NRI was also tested for planning. A small set of regional highway projects was tested with increases in capacity or speed to rank the projects numerically by vehicle hours saved. This use of the NRI may be of value in evaluating regional transportation plan projects. In all, the NRI was found to be a simple, flexible, and practical tool for MPO transportation planning.