Searching for authoritative numbers on how much of urban space is devoted to cars, I found this gem by Manville and Shoup, People, Parking, And Cities. The authors debunk the numbers bandied about by many — two thirds of LA is devoted to car use, etc. — as being undocumented if you follow the trail of citations. They found that Meyer and Gómez-Ibáñez (1983) had proposed an inverse relation between the share of land in streets and the share of land in streets per person, based on 1960 data:
Automobile use does not result in an exceptional percentage of land being given to transportation purposes. Rather, the automobile seems to create exceptional demands for transportation land relative to the number of people in an urban area. Specifically, cities more dependent on the automobile tend to have more street acreage per person but a smaller percentage of total land in streets.
Basically, larger lots leads to low population density, but more importantly, as the car has become dominant in transportation the cities are designed for cars and not for people:
People, Parking, And Cities - Michael Manville and Donald Shoup
Given these results, how can we account for the perception that low-density areas give more of their land to streets? Certainly people tend to associate lower density with increased automobile use, and automobile use with streets. The first of these associa- tions, as we have seen, is more complicated than a simple one- way relationship, but the second may increasingly be true. The association between low density and auto-oriented land use, in other words, may lie less in the share of land given over to streets, and more in the share of streets given over to cars.
The modernist street designs identified by Southworth and Owens (1993) consume less total land area than the dense grids that preceded them, but broad boulevards and cul-de-sacs are also streets whose primary purpose—and perhaps sole purpose—is the swift and safe movement of automobiles. The desire in newer areas to accommodate the car has often led to the removal of other uses from roads and streets. Cul-de-sacs, which force more circuitous routes and have a notoriously limited utility for pedestrians, have been promoted. Intersections, which slow traffic or cause it to stop—but which make streets more amenable to walking—have been minimized. Those intersections that get built are made wider, allowing cars to turn with less deceleration but forcing pedestrians to traverse more road space (Southworth and Ben-Joseph 1996).
Where older intersections often have a curb radius of 3–4 ft, newer intersections flare out: It is not uncommon for zoning laws to call for 15 or 20 ft curb radii. The 9 ft travel lanes of older neighborhoods were replaced in newer developments by 11 and 12 ft lanes, and parking lanes are recommended to be wider still, so through traffic will not be unduly slowed when drivers pulled into or out of spaces. In practice, parking lanes rarely reach their recommended widths, but the standards illustrate a new concern with the street as a territory of the car, rather than as an arena for multiple modes and activities. In some places parking lanes have not been widened but instead prohibited entirely; Century City has banished all its parked cars to off-street garages, and reserves its broad streets for moving automobiles. The end effect is the same. Because curb parking can help make a street feel more human scaled (by encouraging movement on the sidewalks, and by providing a barrier between pedestrians and fast-moving traffic) its removal can amplify the sense that the street is a facility for cars alone.
Manville and Shoup reevaluated the study data that Meyer and Gómez-Ibáñez used, and reaffirmed the basic insights.
Our results indicate that the relationship they identified between density, street space, and streets per capita is still valid. The coefficient of correlation between density and lane-miles per square mile was 0.87, while the coefficient of correlation between density and lane miles per 1,000 persons was −0.39. This latter coefficient is weaker than the relationship identified by Meyer and Gómez-Ibáñez, but still negative.
Columns 4 and 5 of Table 2 show each area’s daily vehicle miles traveled (VMT) per square mile, and VMT per capita. Like our figures for lane mileage, these numbers are derived from the TTI’s database. Given the relationship we have found between street space and density, it is reasonable to expect that VMT interacts with density in a similar manner. Previous research has shown that traffic volumes correlate highly with density: Ross and Dunning (1997), in a report to the Federal Highway Administration, found that traffic volumes rose at 80% of the rate of population change. It may be, however, that density and VMT share the same complicated relationship as density and street space.
Our calculations suggest this is so. For the 20 largest urbanized areas, the coefficient of correlation between population density and VMT per square mile is 0.90, while the coefficient between density and VMT per capita is −0.58. Los Angeles, the densest area, has the highest daily VMT per square mile (128,000), and by a significant margin. It sits in the middle of the pack in terms of VMT per capita. Using all 85 urban areas weakens the relationship only slightly: the coefficient of correlation between density and VMT per square mile falls to 0.86, and the relationship between density and VMT per capita becomes −0.47. Increases in population density reduce the VMT per person but increase the VMT per square mile. In low-density areas each person creates more VMT, but because there are fewer people per square mile the VMT per square mile falls. These findings accord well with the idea that sprawl can reduce congestion, but that it also makes for longer trips.
High levels of VMT per square mile suggest high levels of traffic congestion. For this reason it is not surprising that Los Angeles has such a large VMT per square mile, not only because it reinforces the popular perception that LA has the nation’s worst traffic, but because the region’s relative equality of density (which we discuss in the next section) deprives it of any truly low-density areas that would offer a respite from high congestion levels. We can follow this logic back further into our original seeming paradox: since congestion is properly thought of as competition for scarce road space, areas with high levels of congestion—which is to say dense areas—can be conceived of as lacking in road space, even though they have more of it than less dense areas.
Obviously the problem is not quite that simple. The optimal solution to competition for scarce road space is not more road space, but—as with competition for any scarce resource—prices. In the absence of road pricing, however, it is not uncommon for traffic engineers to state that a congested area has an undersupply of streets. Congestion worsens as population increases because the supply of streets is relatively static, and cannot keep pace with increases in density and VMT if everyone drives everywhere.
So, cities become designed around their streets, and the lower the population density (larger lots) the more time people spend driving in cars, which leads to greater congestion, like LA.
And the result is that cities like LA do in fact dedicate a higher proportion of space to cars.
This means that the rise of autonomous cars — even in places like LA, will lead to strong motivations to increase density, and to reuse space now dedicated to cars that are generally at rest: parking. LA has 24% of its central business district dedicated to parking, for example, leaving aside the underground and multilevel structures allocated to it.
The final table includes a wide variety of cities, including New York, and rationalize parking as a function of jobs in the city:
New York has the amazingly low figure of 0.06 parking spaces per job in the downtown area, contrasted with LA’s 0.52: ten times more parking per person in LA than NYC, and LA is — to the authors’ knowledge — the highest percentage on earth.
The authors quote Lewis Mumford, who said
The right to access every building in the city by private motorcar, in an age when everyone owns such a vehicle, is actually the right to destroy the city.
And they close with a recommendation:
Perhaps the simplest and most productive reform of American zoning would be to declare that all existing off-street parking requirements are maximums rather than minimums. The examples of New York and San Francisco suggest that limits on off-street parking can foster many of density’s benefits, and urbanists who admire these cities might urge other places to adopt their approaches to parking. From a different perspective, however, more regulation may not be the best first step. The market can mediate the supply of parking in most urban areas, and despite the planner’s frequent desire to replace a floor with a ceiling, it may be better to simply deregulate parking—to force it on no one and let those who want it pay for it. A market-oriented approach to parking would eliminate cumbersome regulations, remove incentives to drive, and let city planners concentrate on matters that seriously demand their attention.
Or let some innovation like autonomous cars come along, and watch what happens when 70% or more of the cars go away.
Briegmann wonders if the driverless (autonomous) car would lead to reduced congestion, but also greater sprawl?
Robert Bruegmann via Bloomberg
The driverless car might well substantially alter all the equations: the division between public and private, the collective and individual. Transportation policy has never been as clear as the polemics on the subject would suggest. The taxi, for example, has long shared characteristics of each. In recent years, the divide between public and private transport has been further eroded with the Zipcar (ZIP), Super Shuttle and other on- demand vehicles such as Personal Rapid Transit, a system of small automated vehicles running on guideways. A pioneering and successful example of PRT, constructed in the 1970s, can still be seen in operation in Morgantown, West Virginia.
What the driverless automobile might do is further break down the distinctions. Suppose an individual can summon a vehicle on demand — a small capsule like a golf cart for doing errands in the city, for example, or something more like a van to transport a track team to another city — and that vehicle can go directly from starting point to destination. The flexibility this system could provide might well reduce the incentive for owning an automobile, which has to serve all purposes, is expensive to buy and maintain, and in most cases spends most of its time taking up valuable space in a garage or parking lot.
If the driverless car reduces congestion by maximizing the use of existing highways and taking passengers farther and faster with greater comfort, it could lead to even more dispersed cities. But it could also have the opposite effect.
Given the large amount of space devoted to roads and parking in American cities, even minor increases in collective use of vehicles could lead to less need for new pavement and parking and to higher residential and commercial densities. This would reinforce a trend that is already visible, as new development at the far suburban edge of most urban regions is currently being created at higher densities than in the past and there is a great deal of infill in city centers and close-in suburbs.
Although the driverless automobile, like almost every technological advance, will undoubtedly bring on a great many new problems, it could also help ease several existing problems caused by the automobile, notably traffic fatalities and congestion.
My bet is that the transition will follow an S curve of adoption, with very different models at different stages. At first, when less than 15% of the population use auto-autos it will be like today’s electric cars: a personal choice, but basically leading to only small changes in the ecosystem: for example, very few chargers at strip malls and offices. It is only after the early majority start to adopt auto-autos that things will really change, and I bet it will unfold fastest in cities.
Bruegman mentions taxis as vehicles that have elements of both public and private transportation. What happens, though, when taxis are autonomous, and no longer require taxi drivers? First of all, they become much much cheaper. Let’s imagine that 50% of the expense of a taxi is the human driving it. So taxi fares could — would — drop by at least half, and probably more, including the tip!
In such a scenario, those living anywhere with a high enough population density to support taxis would have very strong motivations to not own a car, much more so that today, even given taxis, Zipcar and other public transport. In areas of lower density, even those where taxis are not really viable in large numbers, taxis would become much more prevalent.
My sense is that this would allow for a strong incentive for people to move from lower to higher density areas, along with the added benefit of not requiring parking for the no-longer necessary car.
Investigating the likelihood of lightning strikes causing damage to telecommunications systems, NTT researchers stumble upon the 3/4 exponent — the same exponent underneath the relationship of placenames and population density (see The curious relationship between place names and population density) and other density-related phenomena:
Tim De Chent via Per Square Mile
Using past data on lightning strikes, telecom equipment failures due to lightning strikes, and the 2005 Japanese census, they [NTT researchers] developed a model to describe how telecom equipment failures due to lightning correlate with population density. At first blush, I expected urban areas to receive the brunt of the impact—after all, they have loads more equipment than rural areas—but the results were just the opposite. Expensive circuitry and antennas were safer in urban Tokyo than they were in rural Gunma, even when the discrepancy in lightning strikes between the two regions was taken into account.
The authors offer two explanations for why telecom equipment is safer in urban areas. First, many of the copper lines that feed base stations and boxes run underground in cities, which lowers the induced voltage during a strike. Second, the equipment itself tends to be exposed to the elements in the country, either on the ground or perched atop telephone poles. In the city, most of it in encased in apartment buildings.
But there is another possible explanation they missed—the design of telecom networks and their relationship to population density. The evidence lies in their calculated coefficient that describes how population density can predict equipment failures due to lightning strikes. The coefficient is ¾, and if you’ve been reading this blog for a while, you’ll no doubt recognize that number. As an exponent, ¾ is powerful descriptor, explaining a variety of phenomenon ranging from how plant sizes influences population density to how human population density affects the density of place names.
In this case, ¾ seems to say less about the pattern of lightning strikes than it does about telecom network design and the differences between rural and urban infrastructure. Denser populations require more equipment, but not at a fixed rate. Cellular networks provide a good example. In rural areas, cell sizes are limited by area, not the number of users. It’s the opposite in the city—the more users, the smaller cells become. Therefore, phone companies can rely on fewer cells and less equipment per person in the city than in the country.
The relationship between infrastructure demands and population density could go a long way to explaining why there is a lower rate of equipment failure in denser areas—there’s simply less equipment per person in the city than in the country. But the fact that telecom infrastructure—and damage to it—appears to scale at the same power that describes an range of phenomena related to density and metabolism, well, that’s just too good to be a coincidence.
Back in 2007, for example, only 8 of the top 50 urban areas (by GDP) were located there. Half of global GDP came from the developed world’s top 380 cities, with 20-plus percent from just 190 North American ones. But over the next 15 years, the urban center of gravity will move south and—still more decisively—east. By 2025, Asia will boast upward of 20 of the top 50 cities, and Shanghai and Beijing will have GDPs higher than those of Los Angeles and London.
Luis Bettencourt and Geoffrey West, Bigger Cities Make Do With Less (Scientific American, September 2011)
This new, more quantitative science of cities is becoming possible because of the increasing availability of information—official statistics as well as novel measures of human and social activity—on cities and metropolitan areas worldwide.
By sifting through this flood of data, covering thousands of cities around the world, we have unveiled several mathematical “laws” that explain how concentrating people in one place affects economic activity, return on infrastructure investment and social vitality. Despite the rich diversity of metropolitan regions across the U.S., China, Brazil and other nations, we found a remarkable universality in the way that socioeconomic characteristics increase with a city’s population. For example, if the population of a city is doubled, whether from 40,000 to 80,000 or from four million to eight million, we systematically see an average increase of around 15 percent in measures such as wages and patents produced per capita. If eight million people all live in one city, their economic output will typically be about 15 percent greater than if the same eight million people lived in two cities of half the size. We call this effect “superlinear scaling”: the socioeconomic properties of cities increase faster than a direct (or linear) relation to their population would predict.
The data also reveal that cities’ use of resources follows a similar, though inverted, law. When the size of a city doubles, its material infrastructure—anything from the number of gas stations to the total length of its pipes, roads or electrical wires—does not. Instead these quantities rise more slowly than population size: a city of eight million typically needs 15 percent less of the same infrastructure than do two cities of four million each. This pattern is referred to as sublinear scaling. On average, the bigger the city, the more efficient its use of infrastructure, leading to important savings in materials, energy and emissions.
Our findings also show that these patterns of increased productivity and decreased costs hold true across nations with very different levels of development, technology and wealth. Although we have much more information for cities in richer parts of the world, we are beginning to obtain good data from rapidly developing countries as well, and they seem to fit the same mold. The gross domestic product for cities in Brazil and China, for instance, closely follows the same superlinear curve that western European and North American cities exhibit, though starting from a lower baseline. We believe that the pattern holds true because the same basic social and economic processes are at work, whether in São Paulo’s favelas, under Beijing’s smog-filled skies or along Copenhagen’s tidy streets.
Although urban superlinear scaling, which represents the average, idealized behavior of a city of a given size, prevails around the globe, actual cities deviate to varying degrees from the roughly 15 percent enhancements that come with size. Detailed data covering 40 years show, for example, that San Francisco and Boston are richer than their size would indicate, whereas Phoenix or Riverside, Calif., are somewhat poorer. Curiously, these deviations persist for decades: cities tend to stay remarkably close to their overperforming or underper- forming histories. For example, cities that have attempted to improve their lot by creating conditions for the “next Silicon Valley” have often had disappointing results. Our research suggests that certain intangi- ble qualities of social dynamics—more than the development of material infrastructure—hold the key to generating virtuous cycles of innovation and creation of wealth. These processes, such as the de- velopment of a spirit of local entrepreneurship, a reputation for cutting-edge novelty, and a culture of excellence and competitiveness, are difficult to design through policy because they rely on the dynamics of a city’s social fabric across many dimensions. We expect the results of this exciting area of research will lead to better “recipes” for sustainable socioeconomic development.
What we can say with certainty, however, is that increased population promotes more intense and frequent social interactions, occurrences that correlate with higher rates of productivity and innovation, as well as economic pressures that weed out inefficiencies. In a city with high rents, only activities that add substantial value can be profitable. These economic pressures push urbanites to come up with new forms of organizations, products and services that carry more value added. In turn, higher profitability, excellence and choice tend to attract more talent to the city, pushing rents higher still, fueling the need to find yet more productive activities. This feedback mechanism, in a nutshell, is the principal reason cities accelerate innovation, while diversifying and intensifying social and economic activity.