Tag Archives: quantitative

Driverless cars: implications for city planning and urban transportation

Nevada autonomous vehicle license plate. CC image from National Museum of American History.

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.

Different types of urban science

CC image from futureatlas.com

CC image from futureatlas.com

Jeff Wood’s handy mailing list on behalf of Re-connecting America pointed me towards this article from Urban Omnibus, disputing the broad conclusions from Geoffrey West’s work towards discovering a universal theory of cities.  Eric Peterson, the author, does not like the implications of West’s quantitative work and the implications of physical laws that might apply to cities:

Despite proposing to have radically reinvented the field in which architects and urbanists work, the article appears to have garnered little attention among commentators and blogs from within architecture and urbanism. Perhaps the article’s lack of substance explains professionals’ reluctance to engage with the implications of West’s work. Nonetheless, it is crucial for those of us interested in the serious study of urbanism to look closely at the article, if only because many of the assumptions it advances strike me as undermining an understanding of cities as complex and important things.

The charge that West’s work is somehow lacking in substance struck me as harsh and misguided.  The notion that there can be only one true understanding of how cities work misses the obvious difference between  West’s work and the more conventional urban studies that Peterson seems to prefer.  The difference appears to be a simple one, based on a misunderstanding of the kinds of universal rules West seeks to understand, as well as the fundamental difference between qualitative and quantitative observation.

Remembering that West is a physicist, Peterson’s charge that a universal theory of urbanism misses out on all of the complexity of a city represents a fundamental misunderstanding of what such a universal theory really means.  Just look at West’s field – physics – and you can easily see exceedingly complex movements that can all be understood by the basic laws of Newtonian mechanics.  A full understanding of motion, as we know it, is an exceedingly complex undertaking, yet Newton essentially boiled that complexity down to three basic laws of motion, which can easily be translated into simple maxims.  Bodies at rest tend to stay at rest; bodies in motion tend to stay in motion; for each and every action there is an equal and opposite reaction; etc.

These laws have limits to their validity, of course, but that does not discount the fact that complex systems can be understood via the basis of simple laws. This reduction isn’t something to be feared.

Peterson also seems to gloss over the mutually beneficial relationship between both qualitative and quantitative analysis.  He frames urbanism in a qualitative way and then implies that the quantification of urbanism not only has little to offer, but is indeed dangerous to our understanding of urban places:

Further, such an approach should be read as dangerous to all of us who see cities as phenomena formed at the collision of dynamic economic, historical, social, political and ecological forces.

This fear seems so misguided that I don’t even know where to begin.

Instead of recognizing cities as the products of these complex forces, the object of West’s study is purposefully contextless and unspecified. Describing how he applies his scientific principles to a specific city he’s studying, he says, “I don’t know anything about this city or even where it is or its history, but I can tell you all about it. And the reason I can do that is because every city is really the same.” West goes on to qualify this assertion by saying that, essentially, the differences between cities that we so often discuss are merely superficial, material ones, related to how a city functions rather than to each city’s unique history.

Even in areas of knowledge where we have a strong quantitative understanding of how things work, this knowledge has never derailed our searches for qualitative understanding as well – for context, for history, for social interactions.

Some of this confusion between the respective role for quantification and qualification stems from language.  Peterson notes early in his piece his disdain for West’s characterization of cities as “problems” to be solved.  Here, the word problem would have completely a different meaning to a mathematician and a physicist as compared to a ethnographer or an architect.  To the mathematician, a problem is not necessarily a social ill but a riddle to be solved, a question to be answered.

In the end, both approaches are crucial to our understanding of the places we live in.

A universal theory of cities

CC Image from lopolis

CC Image from lopolis

Last week, the New York Times Magazine featured a lengthy piece from Jonah Lehrer about two physicists who have formulated a sort of universal law for urban living.  The single biggest determinant of urban performance is size – increasingly large agglomerations offer economies of scale – people who live and work there are more productive, more creative, etc.  The physicists (Geoffrey West and Luis Bettencourt) summarize their main conclusions:

Three main characteristics vary systematically with population. One, the space required per capita shrinks, thanks to denser settlement and a more intense use of infrastructure. Two, the pace of all socioeconomic activity accelerates, leading to higher productivity. And three, economic and social activities diversify and become more interdependent, resulting in new forms of economic specialization and cultural expression.

We have recently shown that these general trends can be expressed as simple mathematical ‘laws’. For example, doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations. This systematic 15% savings happens because, in general, creating and operating the same infrastructure at higher densities is more efficient, more economically viable, and often leads to higher-quality services and solutions that are impossible in smaller places.

These core economies of scale, positive feedback loops, and benefits of agglomeration are what lets cities be cities.  Now, we have some math behind it.

Some more quotes from the NYT Mag piece.

On urban systems:

There is something deeply strange about thinking of the metropolis in such abstract terms. We usually describe cities, after all, as local entities defined by geography and history. New Orleans isn’t a generic place of 336,644 people. It’s the bayou and Katrina and Cajun cuisine. New York isn’t just another city. It’s a former Dutch fur-trading settlement, the center of the finance industry and home to the Yankees. And yet, West insists, those facts are mere details, interesting anecdotes that don’t explain very much. The only way to really understand the city, West says, is to understand its deep structure, its defining patterns, which will show us whether a metropolis will flourish or fall apart. We can’t make our cities work better until we know how they work. And, West says, he knows how they work.

On similarities and dissimilarities to natural systems:

[T]he real purpose of cities, and the reason cities keep on growing, is their ability to create massive economies of scale, just as big animals do. After analyzing the first sets of city data — the physicists began with infrastructure and consumption statistics — they concluded that cities looked a lot like elephants. In city after city, the indicators of urban “metabolism,” like the number of gas stations or the total surface area of roads, showed that when a city doubles in size, it requires an increase in resources of only 85 percent.

…

What Bettencourt and West failed to appreciate, at least at first, was that the value of modern cities has little to do with energy efficiency. […] In essence, they arrive at the sensible conclusion that cities are valuable because they facilitate human interactions, as people crammed into a few square miles exchange ideas and start collaborations. “If you ask people why they move to the city, they always give the same reasons,” West says. “They’ve come to get a job or follow their friends or to be at the center of a scene. That’s why we pay the high rent. Cities are all about the people, not the infrastructure.”

On positive feedback loops:

West and Bettencourt refer to this phenomenon as “superlinear scaling,” which is a fancy way of describing the increased output of people living in big cities. When a superlinear equation is graphed, it looks like the start of a roller coaster, climbing into the sky. The steep slope emerges from the positive feedback loop of urban life — a growing city makes everyone in that city more productive, which encourages more people to move to the city, and so on. According to West, these superlinear patterns demonstrate why cities are one of the single most important inventions in human history. They are the idea, he says, that enabled our economic potential and unleashed our ingenuity. “When we started living in cities, we did something that had never happened before in the history of life,” West says. “We broke away from the equations of biology, all of which are sublinear. Every other creature gets slower as it gets bigger. That’s why the elephant plods along. But in cities, the opposite happens. As cities get bigger, everything starts accelerating. There is no equivalent for this in nature. It would be like finding an elephant that’s proportionally faster than a mouse.”

Scarcity is the check on this superlinear growth, and innovation is what breaks that check.

On counterpoints to these universal laws: Lehrer quotes suburbanist Joel Kotkin in his piece, with Kotkin arguing against this logic of density and economies of scale, citing Silicon Valley and the Research Triangle.  Kotkin is too focused on the traditional narrative of cities and suburbs, however.  Both of those examples are still agglomeration economies, just comprised in a different physical form. A ‘city’ here is also the total urban area, not the arbitrary political boundaries that Kotkin often hangs his hat on.

It’s also important to note that this kind of universal law sets the baseline for what’s to be expected of a city – certain places will under or over-perform.  That’s where the quality of a place comes in, in my estimation.

On qualitative measures: West and Bettencourt specifically avoid the qualitative, since they can’t measure it well with data.  It’s important to not set qualitative and quantitative measurements in opposition, however.  WNYC’s RadioLab delved into the qualitative aspects of what makes cities into cities back in October.  These different explanations of cities are not mutually exclusive.  Indeed, they are complimentary.

This discussion, both the qualitative and quantitative aspects of it, seem to further embrace the Three D’s of density, diversity, and design.  The question is then about how to assess each of those factors.  Given that each one of those factors can be defined expansively (diversity of people, of skills, of land use, of incomes, of languages, of cultures, etc) and not all of those varied elements can be effectively quantified, this only reinforces the co-dependence of both analytical methods.

On planning: Lehrer closes his piece with a note about the inherent messiness of cities – the “energized crowding”, to steal a phrase from Spiro Kostof.

Unlike companies, which are managed in a top-down fashion by a team of highly paid executives, cities are unruly places, largely immune to the desires of politicians and planners. “Think about how powerless a mayor is,” West says. “They can’t tell people where to live or what to do or who to talk to. Cities can’t be managed, and that’s what keeps them so vibrant. They’re just these insane masses of people, bumping into each other and maybe sharing an idea or two. It’s the freedom of the city that keeps it alive.”

One common misconception about planners and planning is that we seek to control everything.  Instead, I am more interested in this kind of messy interaction.  Planning is about facilitating those interactions, not about controlling them.  For that reason, I find this kind of research fascinating.

The entire piece is fantastic.  Read the whole thing.