Nevada autonomous vehicle license plate. CC image from National Museum of American History.
Building on the implications of driverless cars on car ownership, as well as the notion that planners aren’t preparing for the rise of autonomous vehicles, I wanted to dive further into potential implications of widespread adoption of the technology. Nat Bottigheimer in Greater Greater Washington argues that city planning as a profession is unprepared for autonomous vehicles:
Self-driving cars address many of the safety and travel efficiency objections that Smart Growth advocates often make about road expansion, or the use of limited street space.
Part of Bottingheimer’s concern is a lack of quantitative analysis, particularly as it relates to the impacts of self-driving cars. However, the real debate is about qualitative values that feed into our analysis.
The officials responsible for parking lot and garage building, transit system growth, bike lane construction, intersection expansions, sidewalk improvements, and road widenings need to analyze quantitatively how self-driving cars could affect their plans, and to prepare alternatives in case things change.
There is one over-arching problem with this approach: our current quantitative analysis all too often is nothing but bad pseudo-science. Donald Shoup has extensively documented the problems with minimum parking requirements in zoning codes, for example. Here, poor policy with vast unintended consequences is based on some level of flawed quantitative analysis, the kind that does not acknowledge the inherent uncertainty in our understanding or ability to project the future. Instead, the analysis is based on assumptions, yet the assumptions are really value-laden statements that carry a great deal of weight.
Even the very structure of the planning and regulation for the future carries a bias: a requirement to provide parking spaces in anticipation of future demand will, by nature, ignore the complexity of the marketplace for off-street parking and the natural range of parking demand.
Bottigheimer is also concerned about the impacts of self-driving cars on future land use forecasts:
Planners need to examine how travel forecasting tools that are based on current patterns of car ownership and use will need to change to adapt to new statistical relationships between population, car ownership, trip-making, car-sharing, and travel patterns.
By all means, we need to adjust our forecasting tools. However, we shouldn’t be doing so simply based on the arrival of a new technology. We should adjust them because they’re not particularly accurate and their erroneous projections have large impacts on how we plan. Driverless cars aren’t the problem here. The problem is in our assumptions, our inaccurate analysis, and our decision-making processes that rely on such erroneous projections.
Leaving the limitations of quantitative analysis aside for the moment, we can still hypothesize (qualitatively, perhaps) about the future world of driverless cars. Assuming that autonomous vehicles do indeed reduce car ownership and begin to serve as robo-taxis, we can sketch out plausible scenarios for the future. We assume car ownership will decrease, but vehicle-miles traveled may increase.
City Planning and Street Design:
One of Bottigheimer’s chief concerns is that “planners and placemaking advocates will need to step up their game” given the potential benefits for safety, increased car capacity,
As mentioned above, much of the ‘safety’ benefits are about cars operating in car-only environments (e.g. highways), when the real safety challenges are in streets with mixed traffic: pedestrians, bikes, cars, and buses all sharing the same space. In this case, the values planners and placemaking advocates are pushing for remain the same, regardless of who – or what – is driving the cars. The laws of physics won’t change; providing a safe environment for pedestrians will still be based on the lowest common denominator for safe speeds, etc.
The biggest concern should be in the environments that aren’t highways, yet aren’t city streets, either. Will driverless cars forever push stroads into highway territory? Borrowing Jarrett Walker’s phrasing, technology can’t change geometry, except in some cases at the margins.
Instead of a technical pursuit of maximum vehicle throughput (informed by quantitative analysis), the real question is one of values. The values that inform planning for a place or a street will set the tone for the quantitative analysis that follows. Maximizing vehicle throughput is not a neutral, analytical goal.
Congestion:
Congestion is a more interesting case, as it will still be an economic problem – centralized control might help mitigate some traffic issues, but it doesn’t solve the fundamental economic conundrum of congestion. Here, too, the economic solutions in a world of human-driven cars will have the same framework as one with computers behind the wheel.
Driverless cars might change the exact price points, but they don’t alter the basic logic behind congestion-mitigation measures like a cordon charge in London or Stockholm, or like Uber’s surge pricing (efficient and rational as it might be, but perhaps too honest). Again, technology can’t fundamentally change geometry. Cars will still be cars, and even if driverless cars improve on the current capacity limitations of highways, they do not eliminate such constraints.
Qualitative Concerns:
Instead of twisting ourselves in knots over projections about the future that are sure to be wrong, planning for autonomous cars should instead focus on the values and the kind of places we want to plan for. We should adjust our policies to embrace the values of the communities (which alone is a challenging process). We should be aware about the poor accuracy of forecasts and work to build policies with the flexibility to adapt.