Scenario Planning for Automated Vehicles

In region after region, a growing chorus of leaders are calling for their city and metropolitan area to plan for a future of automated vehicles (AVs). Often these calls fall along a binary argument between what analysts call the “heaven’ and ‘hell’ scenarios (or ‘utopia’ or ‘dystopia’ visions) for AVs.

In the Heaven scenario, AVs a are parts of shared fleets of vehicles that travelers don’t own but use like we see today in some car and bike programs. There would be fewer parking lots and roadways since AVs use the infrastructure more efficiently and that land could be repurposed for other uses like recreation. Traffic congestion would be reduced and regional economies would prosper. In the Hell scenario, AVs would be individually owned, with private vehicles driving up vehicle miles travelled and emitting greater carbon emissions. Roadways would become parking lots as these private vehicles clog up streets and infrastructure would decline. Millions of jobs would be lost to automation.

While advocates on both sides like to set it all up as an either/or situation, the reality is that no one know for sure what the impact on AVs the built environment will be. Yet all over the country – and world – governments are under pressure to craft regulations to plan for a future that is difficult to forecast. Their predicament: how to develop ways to define how they grow, instead of being defined by it.

Fortunately, regions still have the ability to act proactively to craft the kind of future they want.

The approach comes from work pioneered in places like Minneapolis and San Diego a few years ago. The idea is to specifically connect metropolitan growth with transportation investments by examining land use patterns, density and urban form to find innovative solutions to challenges like housing, like carbon emissions, agricultural preservation, and economic development. These scenario planning tools enable practitioners to effectively plan for a range of changes and uncertainties through decision making processes that not only consider what is likely to happen but also, what communities want to happen in the future.

In Sacramento, for example, the region devised alternatives to current transportation investment priorities and land use patterns in order to get what it wanted: to increase travel options, shorten commutes, improve air quality, and provide for housing choices that more closely align with the needs of an aging population. This broad vision—the result of scenario planning with local officials and the general public—is intended to guide land-use and transportation choices over the next 50 years.

In Salt Lake City, Envision Utah is a public-private partnership that promotes sustainable growth in Utah’s Greater Wasatch Area through their Quality Growth Strategy. The strategy is a collection of six interconnected and intentional goals, including the promotion of transportation choices alongside the provision of housing choices at all income levels, and 32 strategies to meet these goals. One of the primary tools to inform those goals and strategies was the application of a scenario plan, which extrapolated past development trends to uncover what the region would look like if growth went unchecked.

New attention to these sophisticated tools is due to new provisions that are contained in 2012’s federal transportation law, the Moving Ahead for Progress in the 21st Century (MAP-21). That law gave a big push for these tools in the move towards a more performance-based planning and the federal highway administration has just released a helpful guidebook to help regions understand how scenario planning can be a useful and effective tool in their transportation planning processes.

Regions should consider this guidebook and the new scenario planning practices as an indispensable tool as they wrestle with the uncertain future ushered in by AVs. Because while there is a lot we do not know, we do know regions cannot address tomorrow’s challenges with yesterday’s solutions. Now they have new tools.

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