In an effort to develop a more robust analysis of potential growth the Albuquerque Metropolitan Area, an integrated scenario planning process has been undertaken which evaluates the tradeoffs between different development patterns by exploring scenarios through performance measures. During the first phase, three possible scenarios were devised and the costs and benefits of possible future development patterns were evaluated based on land consumption, transportation conditions, environment and economic measures. These scenarios were called out as i) allowable uses/intensity, ii) emerging lifestyles, and iii) balancing housings/jobs. UrbanSim, a microsimulation model for forecasting land use was used to capture socioeconomic variations along the horizon years. In addition, a bi-directional connection was modeled to iterate socioeconomic parameters with Cube Voyager travel demand model in order to capture the influence of travel times on growth patterns and vice versa.

In the second phase, the three scenarios were refined to a Trend and a Preferred Scenario through interactive workshops and focus groups with regional stakeholders. The introduction of travel skims as an iterative input to the land use model changed the land use forecast to reflect transportation conditions. In particular, it was found that household growth was constrained on the west side of the Rio Grande most likely reflecting congestion on the river crossings.

Multiple simulations of the scenarios were performed to test various policy levers. One policy lever tested was zoning, and allowable densities and uses were adjusted in the Preferred Scenario. In addition, zone-based and point-based levers were implemented to influence the attractiveness of the activity centers, transit nodes, and key corridors. These levers are intended to simulate any type of municipal-based incentives such as reduced impact fees or density bonuses. A range of lever values were tested for sensitivity and reasonability. The final set of values greatly assisted in fine tuning the model behavior to create a publically acceptable preferred scenario. The desire of the stakeholders to see a Preferred Scenario that was adequately different from the Trend brings up interesting questions related to the role of predictive models in scenario analysis and how much is enough when evaluating differences between scenarios.