Category: Development

Job Sprawl in the US vs. Europe

Both American and European cities have prominent central business districts with high job density. But when jobs sprawl beyond the CBDs, they do so in very different manners on the two sides of the Pond, which is both a cause and an effect of higher US automobile usage. Much of this job sprawl happens in places that people on the other side of the Pond would not recognize as part of The City. Besides the obvious misunderstandings, this can compromise the quality of analysis of urbanism and the transportation required to serve it. In short, the European model, for which my models are Stockholm and Paris, is that jobs sprawl contiguously from the CBD, enlarging its physical area, whereas the North American model, for which my models are New York and Washington, is that jobs leap large swaths of residential neighborhoods into auto-oriented suburbia.

CBDs and job density

Office towers are rare in European CBDs. Paris is largely built up to 6 to 9 stories, and the higher end is more common in residential areas like Nation than in the CBD, which stretches from Les Halles to Saint-Lazare and Etoile. Stockholm has a total of five towers in its center, none especially tall. Contrary to the common European belief that high-rises don’t add density, the mid-rise character of most European CBDs leads to a real limit on their ability to agglomerate. The job-densest arrondissement of Paris, the 2nd, has 60,000 jobs in a square kilometer (look for table EMP T6 here); Midtown Manhattan has about 800,000 jobs in 4 square kilometers.

Not all jobs are in the CBD. Some are local community facilities, such as schools, supermarkets, and hospitals. But even more exportable jobs are not all in the CBD. Some industries cluster in sections of the CBD (such as advertising on Madison Avenue in New York or, traditionally, the media on Fleet Street in London), and similarly some cluster in off-CBD locations, perhaps near one firm that located idiosyncratically. In the other direction, not even the job density of Midtown can contain every workplace that wants a central location, and this pushes out firms that can’t compete for CBD office rents. The difference between the North American and European models is where these firms are likely to locate.

Contiguous sprawl

In both Paris and Stockholm, the solution to the restricted job density of their city centers is, superficially, high-rise clusters in a particular suburban place: La Defense in Paris, Kista in Stockholm. The job density at the center of La Defense is actually higher than in the 2nd arrondissement, though it drops drastically outside the very center, whereas the Paris CBD maintains a density of about 50,000 jobs per km^2 over 4 or 5 square kilometers. In both cases, this leads to spatial inequality: in Paris, the richest suburbs are in the west and southwest, and La Defense is west of the city; in Stockholm, Kista itself is surrounded by working- and middle-class areas, and the favored quarter is separated from it by a lake, but the ill-favored quarter to the south is the farthest away.

However, there is much more to Paris employment than the CBD and suburban office towers. Paris has a total of 1.8 million jobs, with only around one eighth to one sixth of them in the CBD. There are corporate headquarters in La Defense and a number of other suburbs on the RER, but there are job clusters all over the city. My arrondissement, the 12th, has 120,000 jobs in a little more than 6 square kilometers, giving it the same job density as is average for the city. According to OnTheMap, Upper Manhattan, defined to be north of East 96th and West 110th Streets, has 150,000 jobs in 19 square kilometers, and the Upper East and West Sides, defined to be north of 62nd Street so as to exclude Columbus Circle jobs, have a total of 175,000 jobs in 9 square kilometers. While the Upper East and West Sides hold their own, in large part thanks to the hospital cluster around Weill-Cornell, Upper Manhattan does not.

These clusters in Paris are everywhere. In my arrondissement the cluster in question is Bercy, home to the Ministry of Finance; there’s also the university cluster in the Latin Quarter, an under-construction judicial cluster around Clichy-Batignolles, and high-end professional services spillover west of the CBD in the 16th and 17th arrondissements. In effect, office uses are sprawling into otherwise-residential neighborhoods.

In Stockholm, the same situation occurs. Spotify is headquartered two T-Bana stops north of T-Centralen, a short walk from where I used to live in Roslagstull (in fact, one of the people viewing my apartment as I was leaving it worked there). There’s also a prominent peak travel flow of students heading to KTH and the University on the trains from points south. In the south, Södermalm has its own secondary CBD around Slussen, the second busiest T-Bana station after T-Centralen.

Office park sprawl

North American cities do not have high overall job density in the core when one counts both the CBD and surrounding inner neighborhoods, which are typically entirely bedroom communities like Upper Manhattan. Instead, there is discontinuous job sprawl: jobs hop over residential areas into farther-away places, typically suburban office parks. The most famous in American urbanist discourse is Tysons Corner in Northern Virginia, but the Washington metropolitan area is generally replete with edge cities, including Reston, Bethesda, and Silver Spring, all located in the northern and western favored quarters of the region. Kista is really a high-rise version of these edge cities.

Washington is the purest example of office job sprawl. However, even there, there is a complication: there are some nearer job clusters like the Pentagon. New York and other large American cities are the same, with even more complications like this. In New York, the in-state side of the metro area has large suburban job clusters such as White Plains and Stamford, but the New Jersey side includes the formerly independent Downtown Newark, contiguous job sprawl in Jersey City directly facing Lower Manhattan, and very decentralized job sprawl in Middlesex County, contrasting with the centralized office sprawl of White Plains.

Robert Lang and Jennifer Lefurgy call Central Jersey edgeless cities and White Plains and Tysons edge cities. While edge cities exist in Europe, edgeless cities do not. Exurban retail in France resembles American exurban retail, with Carrefour inventing the hypermarket at the same time Wal-Mart did, but there is almost no equivalent of the small American office park. The closest I am aware of, Sophia-Antipolis, is an edge city with a large concentration of jobs, just built at automobile scale without any walkability.

New York is large enough to have an intermediate form: namely, a secondary CBD that’s not contiguous with the main city center. Downtown Brooklyn arose as such a CBD, serving Brooklyn, even though it’s contiguous with Lower Manhattan across the water. Jamaica is the best example, as it is quite far from Manhattan. La Defense should be put in this category as well – it is contiguous with the dense built-up area, if not with the CBD itself, and it is closer to Les Halles than Jamaica is to Midtown.

Favored quarters

There are multiple instances of large American firms moving their entire headquarters to be close to where the CEO lives. IBM moved to Armonk and General Electric moved to Fairfield, both leaving New York, to avoid making executives drive in Manhattan traffic. In Europe, too, the edge cities tend to be in rich areas. The corporate headquarters around Paris cluster in La Defense and to a lesser extent northwestern and southern suburbs, and not in Seine-Saint-Denis.

This is a straightforward consequence of the fact that rich Americans left city centers starting in the early 20th century, culminating in middle-class white flight in the 1950s, whereas Paris and Stockholm remain richer than their suburbs. The inhabitants of the 16th arrondissement of Paris are unlikely to be interested in job sprawl. Instead, the Paris CBD is slowly migrating westward, as retail and office rents at the western end (Etoile) are higher than in the center (the Opera) and eastern end (just west of Les Halles).

One would suspect that in American cities that are richer than their suburbs, the phenomenon of job sprawl would not occur. The problem is that there is no clean example today. Boston is still poorer than its suburbs; Cambridge is quite rich, but is functionally one favored-quarter wedge. San Francisco is overall richer than most of its suburbs, but really the entire strip of land from San Francisco down to San Jose is rich, and at any rate Silicon Valley formed in a then-independent metro region, rather than sprawling out of the center the way White Plains and Tysons Corner did.

However, as white flight is giving way to gentrification, and American cities are economically outgrowing their suburbs, this theory would predict that job sprawl should decrease, with more corporate jobs shifting back to the cities. This seems to indeed be happening: General Electric moved from Fairfield to Downtown Boston, and United-Continental moved its headquarters to the Sears Tower in the Chicago Loop; Aaron Renn periodically talks about the resorting of the American economy, in which the highest-end jobs are back to city centers whereas lower-end jobs are in the suburbs and smaller cities.

Is the US Europeanizing?

There is some evidence to suggest that American cities not only are reducing the extent of job sprawl in the highest pay categories, but also adopting the European pattern of contiguous CBD sprawl. This process is haphazard, and many urban boosters overrate the extent to which near-CBD locations like the West Loop in Chicago or the Seaport in Boston are attracting jobs, but these areas are nonetheless growing.

Boston is perhaps the best example of this trend. Locally, urban boosters anxiously talk about transportation connections to the Seaport, but the biggest action is happening in the other direction. Kendall Square is growing as the Cambridge CBD, with a cluster of tech firms, two stops out on the subway from the central transfer points. Boston is unique that Back Bay is a nearly-contiguous secondary CBD as well, based on extensive postwar urban renewal next to a rich residential neighborhood. This situation is especially notable given that both Cambridge and the Seaport are separated from the CBD by water, with unpleasant walking environments on the bridges, making the organic process of CBD extension more difficult.

Outside Boston, several more examples are notable. In San Francisco, tech jobs within the city cluster not in the Financial District but in the adjacent South of Market (“SoMa”) area. In Chicago, in addition to some growth in the West Loop, there is some job growth on the Near North Side. In New York, the tech jobs cluster in the Meatpacking District: the Google building, which I believe is the second largest Google office after the Googleplex, occupies the block between 8th and 9th Avenues and 15th and 16th Streets. But even before Google, there was growth in adjacent secondary CBDs across the water: Jersey City and Long Island City. Lang and Lefurgy’s writeup on edgeless cities classifies Jersey City as a secondary CBD rather than an edge city because “its context is old”: it’s built out of a near-CBD residential and industrial area, rather than developed from scratch near a road or rail intersection.

Starting last decade, urbanist writers in the United States noted that the US was Europeanizing in its pattern of rich cities and poor suburbs. Brookings was writing about suburbanization of poverty in 2010, describing a 2000-8 trend. The growth of near-CBD office clusters in Boston, New York, San Francisco, and Chicago suggests that the US is also Europeanizing in its pattern of how jobs spill over from the center. Instead of the traditional auto-oriented office park near the CEO’s residence, the highest-income, highest-prestige jobs in the US are decamping to the same near-CBD locations where they can be found in Paris or Stockholm, leaving the sprawl for the poor.

The Subway in New York is not at Capacity

It seems to be common wisdom that the subway in New York is at capacity. Last year, the New York Times ran an article that repeated the MTA’s claims that growing delays come from overcrowding (which they don’t). A few weeks ago the NY Times quoted Riders Alliance campaign manager Rebecca Bailin saying “Our system is at capacity” and “subways are delayed when people can’t fit in them.” So far so good: some parts of the subway have serious capacity issues, which require investment in organization and electronics (but not concrete) to fix. But then some people make a stronger claim saying that the entire system is at capacity and not just parts of it, and that’s just wrong.

A few days ago there was an argument on Twitter involving the Manhattan Institute’s Nicole Gelinas and Alex Armlovich on one side and Stephen Smith on the other. Stephen made the usual YIMBY point that New York can expect more population growth in the near future. Nicole argued instead that no, there’s no room for population growth, because the subway is at capacity. Alex chimed in,

People are not going to be willing to pay market rents for places they can’t commute from. A large number of folks underestimate the self-regulation of NYC housing–it just can’t get that bad, because people can always just move to Philly

Like, if upzone Williamsburg, people who move into new housing aren’t going to try to ride the L–they’ll only come if they can walk/bike or ride in off-peak direction. Just like people are leaving in response to the shutdown. Neighborhoods and cities are in spatial equilibrium!

I responded by talking about rents, but in a way my response conceded too much, by focusing on Williamsburg. The L train has serious crowding problems, coming from lack of electrical capacity to run more than 20 trains per hour per direction (the tracks and signals can handle 26 trains, and could handle more if the L train had tail tracks at its 8th Avenue terminal). However, the L train is atypical of New York. The Hub Bound Report has data on peak crowding into the Manhattan core, on table 20 in appendix II. The three most crowded lines entering the Manhattan core, measured in passengers per floor area of train, are the 2/3, 4/5, and L. Those have 3.6-3.8 square feet per passenger, or about 3 passengers per m^2, counting both seated and standing passengers; actual crowding among standees is higher, around 4 passengers per m^2. Using a study of seating and standing capacity, we can get exact figures for average space per standee, assuming all seats are occupied:

Line Peak tph Seats Standee area Passengers Passengers/m^2
1 18 7,920 3,312 13,424 1.66
2/3 Uptown 23 9,200 4,393 28,427 4.38
A/D 17 9,792 3,980 23,246 3.38
B/C 13 6,994 2,899 12,614 1.94
4/5 Uptown 24 8,640 4,752 28,230 4.12
6 21 9,240 3,864 21,033 3.03
F Queens 13 5,967 3,560 17,816 3.33
N/Q/R 23 10,908 6,179 29,005 2.93
E/M 22 8,568 5,856 22,491 2.38
7 24 9,504 5,227 20,895 2.18
L 19 6,080 4,321 23,987 4.14
J/M/Z 19 6,384 4,363 16,657 2.68
F Brooklyn 14 6,426 3,834 14,280 2.05
B/D/N/Q (4 tracks) 38 18,612 10,008 43,550 2.49
A/C 20 10,112 4,504 21,721 2.58
2/3 Brooklyn 16 6,400 3,056 13,536 2.34
R 8 4,608 1,873 5,595 0.53
4/5 Brooklyn 20 7,200 3,960 16,504 2.35

Three additional snags are notable: crowding in 53rd Street Tunnel looks low, but it averages high crowding levels on the E with low crowding levels on the M (see review), and the 1 and 7 achieve peak crowding well outside Midtown (the 1 at 96th at the transfer to the 2/3 and the 7 at Jackson Heights at the transfer to the E/F) whereas the table above only counts crowding entering Manhattan south of 59th Street. But even with these snags in mind, there is a lot of spare capacity on the Upper West Side away from 72nd and Broadway, and in Queens in Long Island City, where passengers can take the undercrowded 7 or M. Crowding in Brooklyn is also low, except on the L. In both Brooklyn and on the West Side locals there’s also track capacity for more trains if they are needed, but New York City Transit doesn’t run more trains since peak crowding levels are well below design guidelines.

This isn’t a small deal. Williamsburg is where there is the most gentrification pressure, but the Upper West Side is hardly a slum – it’s practically a byword for a rich urban neighborhood. The trains serving Brooklyn pass through some tony areas (Park Slope) and gentrified ones (South Brooklyn), as well as more affordable middle-class areas further south. From NYCT’s perspective, developing South Brooklyn and Southern Brooklyn is especially desirable, since these areas are served by trains that run through to Queens, Uptown Manhattan, and the Bronx, and with the exception of the B are all much more crowded at the other end; in effect, lower subway demand in Brooklyn means that NYCT is dragging unused capacity because of how its through-service is set up.

Actual perceived crowding is always higher than the average. The reason is that if there is any variation in crowding, then more passengers see the crowded trains. For example, if half the trains have 120 passengers and half have 40, then the average number of passengers per train is 80 but the average perceived number is 100, since passengers are three times likelier to be on a 120-person train than on a 40-person train.

Subway in New York has high variation in crowding, probably unusually high by international standards, on account of the extensive branching among the lines. The E/M example is instructive: not only are the E trains more crowded than the M trains, but also they come more often, so instead of a perfect E-M alternation through 53rd Street, there are many instances of E-E-M, in which an E train following the M is more crowded than an E train following another E train. I criticized NYCT’s planning guidelines on this account in 2015, and believe it contributes to higher crowding levels on some lettered lines than the table shows. However, the difference cannot be huge. Evidently, in the extreme example of trains with 40 or 120 passengers, the perceived crowding is only 25% higher than the actual average, and even the maximum crowding is only 50% higher. Add 50% to the crowding level of every branching train in Brooklyn and you will still be below the 2/3 and 4/5 in Uptown Manhattan.

So on the Upper West Side and in Long Island City and most of Brooklyn, there is spare capacity. But there’s more: since the report was released, Second Avenue Subway opened, reducing crowding levels on the Upper East Side. Second Avenue Subway itself only has the Q, and could squeeze additional trains per hour by shuffling them around from other parts of the system. In addition, the 4/5 and 6 have reportedly become much more tolerable in the last year, which suggests there is spare capacity not only on the Upper East Side but also in the Bronx.

Moreover, because the local trains on Queens Boulevard aren’t crowded, additional development between Jackson Heights and Queens Plaza wouldn’t crowd the E or F trains, but the underfull M and R trains. This creates a swath of the borough, starting from Long Island City, in which new commuters would not have a reason to use the parts of the system that are near capacity. It’s especially valuable since Long Island City has a lot of new development, which could plausibly spill over to the east as the neighborhood fills; in contrast, new development on the Upper West Side runs into NIMBY problems.

Finally, the residential neighborhoods within the Manhattan core, like the Village, are extremely desirable. They also have active NIMBY groups, fighting tall buildings in the guise of preservation. But nowhere else is it guaranteed that new residential development wouldn’t crowd peak trains: inbound trains from Brooklyn except the 4/5 are at their peak crowding entering Lower Manhattan rather than Midtown, so picking up passengers in between is free, and of course inbound trains from Uptown and Queens drop off most of their peak morning load in Midtown.

It’s not just a handful of city neighborhoods where the infrastructure has room. It’s the most desirable residential parts of Manhattan and Brooklyn, and large swaths of middle-class areas in Brooklyn and parts of Queens. In those areas, the subway is not at capacity or even close to it, and there is room to accommodate new commuters at all hours of day. To the extent there isn’t new development there, the reason is, in one word, NIMBYism.

Suburban Transit-Oriented Development

Here’s a Google Maps image of Southport, a section of Fairfield, Connecticut with its own Metro-North commuter rail station:

Here’s an image at the same scale of Bourg-la-Reine, an inner suburb of Paris on the RER B, at the junction between the line’s two southern branches:

At Bourg-la-Reine, the buildings just east of the station are high-rise. There are local community amenities, including walkable schools, supermarkets, and pharmacies, and people can comfortably live in this suburb without a car. This generates significant RER traffic at all hours of day: outbound trains are often standing-room only until they reach this station even in midday, outside rush hour.

At Southport, there are a few townhouses near the station. But the roads are wide and hostile to pedestrians, and the nearest supermarket closes at 6 pm, too late for commuters returning from the city. Car ownership approaches 100%, and nobody rides the trains except to get to office jobs at the traditional peak hour in Manhattan (or perhaps Stamford).

The difference between the two places is so stark that they can barely be compared. Southport has 317 inbound boardings per weekday. Of those, 263, or 83%, are in the morning rush hour; the Metro-North-wide average is 63%, and the average on the SNCF-operated parts of the RER and Transilien is about 46%. Bourg-la-Reine has 4.5 million annual riders, about 16,000 on an ordinary working day.

A huge part of the difference is about service provision – Bourg-la-Reine has a train every five minutes midday, Southport a train every hour. But it’s not just about service. The RER has stations farther out, with somewhat less intense service, such as a train every 15 minutes, with comparable ridership. And the LIRR and Metro-North have little off-peak ridership even at stations with more frequent service, such as Mineola and Hicksville. Transit-oriented development (TOD) is as important as good service in such cases.

I bring up Southport because the RPA just dropped a study about suburban TOD that grades every New York commuter rail station between 0 and 3, and gives Southport the highest mark, 3. The RPA study looks at zoning within 800 meters of each station and considers whether there’s a parcel of land that permits multifamily housing with a floor are ratio higher than 1.25. Southport has such lots, supporting some townhouses, so according to the RPA it gets full marks, even though, by RER standards, it is like every other American car-oriented suburb.

Based on this methodology, the RPA identifies a number of good suburbs, and even comes to policy conclusions. It proposes more TOD in the mold of existing exurban New York examples, such as Patchogue. The model for the program is the real reason the RPA study is so weak: rather than calling into attention the big differences between land use at suburban stations in New York versus in Paris (or any number of big European cities with suburban rapid transit), it overfocuses on small differences within auto-oriented suburbia.

Some of the ultimate conclusions are not terrible. For example, the RPA is proposing linking federal infrastructure development to permitting more multifamily housing. This would improve things. However, the problem with this is twofold. First, it is unrealistic – the federal government gave up decades ago on enforcing fair housing laws, and has no interest in attempting to make exclusionary suburbs behave. Were I to propose this, hordes of American commenters would yell at me for not understanding American politics. And second, it misunderstands the nature of the problem, and ends up proposing something that, while unrealistic, is still low-impact.

The best way to understand the problem with the study is what author Moses Gates told me on Twitter when I started attacking it. He said that the RPA was looking at zoning rather than actual development. Since there is zoning permitting multifamily development within the prescribed radius at Southport, it gets full marks. With my understanding of what good TOD looks like, I would be able to say that this is clearly so bad the methodology must be changed; on Twitter I suggested looking at zoning within 300 meters of the station rather than 800, since the highest-intensity development should be right next to the station. I also suggested looking at supportive nonresidential uses, especially supermarkets. A development that isn’t walkable to retail at reasonable hours is not TOD.

The RPA does not think in this language. It thinks in terms of internal differences within the US. Occasionally it deigns to learn from London, but London’s suburban development is auto-oriented by European standards (transit mode share in the London commuter belt is at best in the teens, often in the single digits). Learning from anywhere else in the world, especially places that don’t speak English, is too difficult. This means that the RPA could not reach the correct conclusion, namely, that there is no such thing as an American suburb with TOD. The only exception I can come up with in the United States involves Arlington, on the Washington Metro, and Arlington is no longer considered a suburb, but really a full-fledged city in a different state, like Jersey City.

The other thing the RPA missed is that it drew too large a radius. TOD at a train station should include townhouses 800 meters out – but it’s more important to include high-rise residential construction next to the train station and mid-rise apartment buildings 500 meters out. Giving American suburbs latitude to place TOD so far from the station means they will act like Southport and allow small amounts of multifamily housing out of the way, while surrounding the station itself with parking, a tennis court, and large single-family houses with private swimming pools. This is not hypothetical: suburbs in New Jersey have reacted to court rulings mandating affordable housing by permitting apartments at the edge of town, far from supporting retail and jobs, and keeping the town core single-family.

Because the RPA missed the vast differences in outcomes between the US and France, it missed some useful lessons:

  • States should centralize land use decisionmaking rather than give every small suburb full autonomy.
  • TOD doesn’t need to be fully mixed-use, but there should be some local retail right next to housing.
  • Housing should be high-density right next to the station. A floor area ratio of 1.25 is not enough.
  • Publicly-funded social housing should be next to train stations, in the city as well as in the suburbs, and this is especially important in expensive suburbs, which aren’t building enough affordable housing.

Without suburban TOD, any regional rail system is incomplete. I wish I could have covered it at my talk, but I didn’t have time. Good service needs to run to dense suburbs, or at least suburbs with dense development within walking distance of the station. It needs to extend the transit city deep into suburbia, rather than using peak-only commuter rail to extend the auto-oriented suburbs into the city.

Neighborhoods With Excess Capacity

In New York, the tech industry has clustered in the Meatpacking District, around 14th Street and 8th Avenue. Google’s building (the company’s largest office outside the Googleplex) is there, Samsung’s New York offices are there, startup incubators are there with co-working spaces. Stephen Smith has called for commercial upzoning there (on YIMBY three years ago, and on Twitter just now), despite NIMBY objections. He argues not only that there is pent-up demand for office space, but also that there is excess subway capacity there: “the L train’s capacity west of Union Square is essentially unlimited, after the hordes from Brooklyn headed to destinations east of Broadway change for the 4/5/6 and N/Q/R.” While his other arguments for upzoning are solid, this one is incorrect, and I’d like to explain which areas have excess capacity and which don’t.

Two years ago, I wrote this post about modeling transit crowding. The model is primitive – it assumes a one-dimensional city, 100% mode share, and independent job and residence distributions. For the purposes of this post, cities A, B, and C from the model are not relevant (they have perfect mixture of jobs and residences); cities D, E, and F, with separation of residences and jobs, are more relevant, with city F, with partial mixture, the most useful.

The results of the model are fairly predictable. In the morning peak, transit vehicles (or roads!) fill up toward the center as they pass through residential areas, and then empty in the commercial core. This means that more residences outward of the point of greatest congestion, and more jobs inward of it, add more crowding; more jobs outward of the point, and more residences inward of it, do not. More jobs on the other side of city center add to crowding, because people still ride through the point of greatest crowding.

On the L, the point of greatest crowding is between Bedford Avenue (the last stop in Brooklyn) and First Avenue (the first in Manhattan). This means that more residential development on the L in Brooklyn and more commercial development in Manhattan would add crowding – even commercial development on the West Side would attract riders living in Brooklyn, who would ride through the overcrowded segment under the East River. The other subway lines serving the Meatpacking District suffer from the same problem: those are the 2 and 3 at 7th Avenue and 14th Street, and the A, C, and E at 8th Avenue. With Second Avenue Subway having taken some crowds off the 4 and 5 on the East Side, it’s likely the 2, 3, and E are the most crowded subway lines in New York today (the A has more room). Yes, most riders on those lines get off in Midtown, but it doesn’t matter, because riders from the Upper West Side and Queens, attracted to new jobs in the Meatpacking District, would still ride through the most crowded point, at the entry to Midtown.

So if not the Meatpacking District, where is it better to add jobs, purely from the perspective of subway crowding? Superficially, the answer is to mix them across the residential parts of the city. But here, my model runs into problems with mode share. The model says that adding jobs in (say) Downtown Brooklyn increases subway crowding, because of riders from Uptown Manhattan riding to the south. Per the model, it’s best to add jobs on the side with more crowding, which is the north and Queens sectors, not the Brooklyn sector, where only the L is very crowded. This means, more jobs on the Upper East and West Sides, and maybe also in Long Island City, near Queensboro Plaza.

But in reality, there is some travel segmentation in New York. People who work on the Upper East and West Sides probably live in those neighborhoods or in Harlem and the Bronx, and people who work in Downtown Brooklyn probably live elsewhere in Brooklyn. Yes, it’s possible to commute between the Upper East Side and Downtown Brooklyn, but people would not ordinarily choose to do so – the commute is long and crowded (because of all the Midtown-bound workers), and there isn’t much saving on rent. People might still do it for various reasons, like a two-body problem or moving frequently between jobs – this is why through-running is important – but it’s much less common than living and working on the same side of city center.

So most likely, office development in Downtown Brooklyn would mainly attract ridership from within Brooklyn. Extra ridership from Uptown Manhattan and the Bronx is likely to be small. The upshot is that locations outside the most crowded point on each inbound subway line are likely to lead to large gains in subway ridership without much additional crowding.

I bring up Downtown Brooklyn and not just the Upper West and East Sides because it is better-connected to more bedroom communities by subway. These include the Lower East Side and Chinatown, Long Island City, and nearly all of Brooklyn. Long Island City is also highly accessible, from much of Queens and the parts of Brooklyn on the G train. But the Upper West and East Sides aren’t so accessible because of the lack of good east-west subway options.

Of course, the situation on the ground is different. New York is desperate to add tech jobs in Downtown Brooklyn, but the tech industry insists on clustering in the Meatpacking District. There’s only so much a city can force developers to site themselves in the areas most convenient for infrastructure. But from a long-term capacity standpoint, it’s in New York’s interest to encourage commercial development outside the Manhattan core, especially in areas that get decent subway service from multiple directions, like Long Island City, Downtown Brooklyn, and maybe Jamaica.

It would be easier if there were more service targeted at off-core destinations. This is part of why I harp on regional rail all the time – the LIRR would be able to serve Downtown Brooklyn and Jamaica better if it didn’t exist just for the benefit of suburban salarymen working in Midtown. But this also includes Triboro, which would give multidirectional service to nodes including Jackson Heights, the Bronx Hub, and Brooklyn College. This would encourage developers to build commercial at these nodes, which suffer from poor access to workers today.

Note that opening circumferential transit, in this model, has the opposite of the expected effect on radial lines. Normally, a new transit line reduces demand on parallel lines and increases demand on intersecting lines, which runs the risk of overloading them. But if a circumferential line encourages office development at intersection points with radials, it will still encourage more ridership on the radials, but this ridership will completely miss the congested inner portions of the radials.

Meme Weeding: Land Value Capture

Last month’s Patreon poll was about meme weeding – that is, which popular meme in public transit I should take apart. The options were fare caps on the model of London, popular among some US reformers; wait assessment, a schedule adherence metric for trains I briefly complained about on Vox as used in New York; and land value capture/tax increment financing/the Hong Kong model. The last option won.

Good public transit creates substantial value to its users, who get better commutes. It’s an amenity, much like good schools, access to good health care, and clean air. As such, it creates value in the surrounding community, even for non-users: store owners who get better sales when there’s better transportation access to their business, workers who can take local jobs created by commuters to city center, and landowners who can sell real estate at a higher price. All of these positive externalities give reason to subsidize public transit. But in the last case, the positive impact on property values, it’s tempting to directly use the higher land values to fund transit operations; in some cases, this is bundled into a deal creating transit-oriented development to boost ridership. In either case, this is a bad way of funding transit, offering easy opportunities for corruption.

Value capture comes in several flavors:

  • In Japan, most urban private railroads develop the areas they serve, with department stores at the city end and housing at the suburban end.
  • In Hong Kong, the government sells undeveloped land to the now-privatized subway operator, the MTR, for high-density redevelopment.
  • In the US and increasingly Canada, local governments use tax increment funding (TIF), in which they build value-enhancing public infrastructure either by levying impact fees on development that benefits from it or by programming bonds against expected growth in property taxes.

In both Hong Kong and the major cities of Japan, urban rail operations are profitable. It is not the case that value capture subsidizes otherwise-money losing transit in either country, nor anywhere I know of; this did not prevent Jay Walder, then the head of New York’s MTA, from plugging the MTR model as a way of funding transit in New York. What’s true is that the real estate schemes have higher margins than rail operations, which is why JR East, the most urban of the remnants of Japan National Railways, aims to get into the game as well and develop shopping centers near its main stations. However, rail operations alone in these countries are profitable, due to a combination of high crowding levels and low operating costs.

The Japanese use case is entirely private, and does not to my knowledge involve corruption. But the Hong Kong use case is public, and does. For all the crowing about it in Anglo-American media (the Atlantic called it a “unique genius” and the Guardian said it supported subsidy-free operations), it’s a hidden subsidy. The state sells the land to the MTR, and the MTR alone, at the rate of undeveloped outlying land. Then the MTR develops it, raising its value. Other developers would be willing to pay much better, since they can expect to build high-density housing and have the MTR connect it to Central. This way, the government would pocket the profits coming from higher value on its land. Instead, it surreptitiously hands over these profits to the MTR.

While Western media crows about Hong Kong as an example of success, local media excoriates the corruption involves. Here’s the South China Morning Post on the MTR model:

The rail and property model was never anything but a delusion to which only Hong Kong bureaucrats could be subject. It traded on the odd notion that you cannot assign a value to property until you actually dispose of it.

Thus if you give the MTR the land above its stations, these sites suddenly and magically acquire value and the proceeds cover the cost of building the railway lines. Ain’t magic wonderful? We got the MTR for free.

Stephen Smith dealt with this issue in 2013, when he was still writing for NextCity. He explained the local corruption angle, the fact that MTR rail operations are profitable on their own, and the lack of undeveloped land for the state to sell in most first-world cities. (Conversely, one of his arguments, about construction costs, doesn’t seem too relevant: Hong Kong’s construction costs are probably similar to London’s and certainly higher than Paris’s, and doing value capture in Paris would be an urban renewal disaster.)

Stephen also tackles American examples of value capture. With no state-owned land to sell to the public transit agency at below-market prices, American cities instead rely on expected property taxes, or sometimes levy special fees on developers for letting them build TOD. Stephen talks about scale issues with the TIF-funded 7 extension in New York, but there are multiple other problems. For one, the 7 extension’s Hudson Yards terminus turned out to be less desirable than initially thought, requiring the city to give tax breaks. See for examples stories here, here, and here.

But there are more fundamental problems with the approach. The biggest one is the quality of governance. TIF is an attractive-looking option in American jurisdictions that recoil at raising direct taxes to pay for service. This means that as happened in New York, it is tempting for cities to promise property tax windfall, issue bonds, and then let successor governments raise taxes or cut services to pay interest. This opaqueness makes it easier to build bad projects. When the government promises especially high benefit-cost ratios, it can also keep issuing new bonds if there are budget overruns, which means there is no incentive for cost control.

TIF also requires the city to use zoning to create a shortage of land in order to entice developers to pay extra to build where it wants them to. Stephen complains that New York reamed problems on upzoning in Midtown East, one of the few locations in Manhattan where developers are willing to build supertall office towers without any tax breaks; the new zoning plan, in the works since he was writing for NextCity in 2013, only just passed. Another such location is probably the Meatpacking District, near the Google building at 14th and 8th, now the city’s tech hub – there is no tall office construction there due to the power of high-income residential NIMBYs. Were the city to loosen zoning in these areas and permit companies that need a prime location to set up offices in these areas, it would find it even harder to entice developers to build in a lower-demand area like Hudson Yards. Midtown East and the Meatpacking District are replete with subway lines, but there are no new plans for construction there, so the city wouldn’t do a TIF there.

The same problem, of TOD-reliant funding requiring the city to restrict development away from targeted investment areas, also works in reverse: it encourages development-oriented transit. In 2007, Dan Doctoroff, then a deputy mayor and now head of Google’s Sidewalk Labs, opposed Second Avenue Subway, on the grounds that the area is already developed. Second Avenue Subway was eventually built, but the 7 extension omitted a stop in an already-developed area amidst cost overruns, as Bloomberg prioritized Hudson Yards. This is not restricted to New York: San Francisco is more interested in a subway to Parkmerced than in a subway under Geary, the busiest bus route, busier than the subway-surface light rail branch serving Parkmerced today. Smaller American cities propose core connectors, aiming promoting redevelopment in and around city center. This in turn means ignoring low-income neighborhoods, where there is no developer interest in new buildings except as part of a gentrification process.

These problems are for targeted investments. But when there is more widespread TOD, TIF ends up being a tax on transit users. Cities build roads without levying special taxes on sprawling development, whether it sprawls by virtue of being near the highway or by virtue of being far from public transit. When they build transit, they sometimes tax TOD, which means they are giving developers and residents tax incentives to locate away from public transit.

Hong Kong is not the right model for any TOD scheme; its corruption problems are immense. It’s a shiny object for Americans (and other Anglophone Westerners), who are attracted to the allure of the exotic foreigner, like a premodern illiterate attributing magic to the written word. Instead of replicating its most questionable aspect, it’s better to look at models that are attractive even to local corruption watchdogs.

This means funding public transit and other services out of transparent, broad-based taxes. Paris uses a payroll tax, varying the rate so as to be higher in the city (2.95%) than in the outer suburbs (1.6%). Everyone will hate them, especially people who don’t use transit and don’t view it as directly necessary for their lives. This is why they work. They compel the transit agency to run efficient service, to stave off opposition from aggrieved center-right middle-class voters, and to run it well, to stave off opposition from populists (“why am I being taxed for trains that break down?”). They leave no room for waste, for cronyism, or for slush funds for favored causes, precisely because they’re hard to pass.

It’s easy to see why politicians avoid such funding sources. The democratic deficit of local governance in the US is immense, and that of Canada is only somewhat better. Nobody wants to lose an election over raising taxes, even in cities where the political spectrum runs from the center leftward. Value capture sounds like a good, innovative idea to fund government without hated taxation, and its abuses are hidden from sight. Even as it forces city residents to endure opaque fees (never call them taxes!), it wins accolades to politicians who propose it. No wonder it continues despite its failures.

Climate Apartheid

There are floods in Houston, thanks to Hurricane Harvey‘s making landfall close to the city, dumping about a meter of rain within just a few days. So far, the best explanation I’ve seen about the city’s drainage system is Matt Corbett’s tweetstorm, about how the city keeps building flood control systems but due to population growth they are perpetually five years behind current development. The confirmed death toll so far is 30. There is a connection to climate change: warmer ocean temperatures make tropical storms more likely, and also make it likelier that they will move slowly and dump more rain onto one area; as a result, Harvey is the Houston region’s third 500-year flood in three years, while one neighborhood was hit by three such floods in a decade.

There are floods all over South Asia, thanks to unusually strong summer monsoon rains. Mumbai got about 200 mm of rain in 12 hours yesterday. The worst impact is in more rural areas and smaller cities in the north, including Bangladesh, West Bengal, Nepal, Bihar, and Uttar Pradesh. The confirmed death toll so far is 1,200, of whom only 6 are in Mumbai – Bihar and Nepal seem like the worst-hit areas. There is once again a connection to climate change: the seasonal monsoon rainfall in South Asia is fairly stable, but extreme events dumping more than 100 or 150 mm of rain on one day are happening at increasing frequency, and climate models predict an increase in extreme rainfall events.

It is not my intention to attack American media for undercovering India. Rather, it is my intention to attack American public intellectuals and wonks for proposing adaptation to climate change. This means building flood walls to protect low-lying cities at risk of storm surges like New York, using zoning and public investments to steer development toward higher ground, and building infrastructure to deal with higher future flood risk. This contrasts with reducing the extent of climate change in the first place, called mitigation in environmentalist parlance, by reducing greenhouse gas emissions.

Examples of calls for adaptation, in lieu of or in addition to mitigation, abound:

  • The Obama administration passed a directive requiring flood control standards for federally-funded projects in areas vulnerable to climate change. The Trump administration rescinded this directive, to widespread criticism from liberals in the media, for example in the Guardian.
  • ThinkProgress just published an article calling for both adaptation and mitigation.
  • ProPublica’s investigative reporting about Houston mentions that Fort Lauderdale and Boulder are both addressing adaptation in their long-term city plans, and compares Houston negatively with them.
  • CNN negatively compared US flood control efforts with the Dutch Delta Works. The article does mention American climate change denial, but talks about the Netherlands’ flood control and not about its pro-bike transportation policy, doing its part to mitigate catastrophic climate change.
  • In a personal conversation with ReThinkNYC‘s Jim Venturi about transit-oriented development near Secaucus Station, he said that the area is vulnerable to climate change, at only 2 meters above sea level. I don’t want to blame him, because he might have been channeling the Regional Plan Association or statewide plans, but one of those three (ReThink, RPA, the state) is giving up the United States’ best TOD spot on climate adaptation grounds.

All of these adaptation plans should be prohibited on grounds of climate apartheid. The term climate apartheid is not my own: it comes from Desmond Tutu, who says,

[Link, PDF-p. 181] For most people in rich countries adaptation has so far been a relatively painfree process. Cushioned by heating and cooling systems, they can adapt to extreme weather with the flick of a thermostat. Confronted with the threat of floods, governments can protect the residents of London, Los Angeles and Tokyo with elaborate climate defence systems. In some countries, climate change has even brought benign effects, such as longer growing seasons for farmers.

Now consider what adaptation means for the world’s poorest and most vulnerable people—the 2.6 billion living on less than US$2 a day. How does an impoverished woman farmer in Malawi adapt when more frequent droughts and less rainfall cut production? Perhaps by cutting already inadequate household nutrition, or by taking her children out of school. How does a slum dweller living beneath plastic sheets and corrugated tin in a slum in Manila or Port-au-Prince adapt to the threat posed by more intense cyclones? And how are people living in the great deltas of the Ganges and the Mekong supposed to adapt to the inundation of their homes and lands?

There’s a lot of nuance to add on top of Tutu’s admonition. The most important is that Mumbai is not Port-au-Prince or Malawi; it’s not even Bihar. But it’s not New York or the Netherlands either. Catastrophic flooding is still a serious risk, and its urban policy gives the poor a choice between substandard slum housing in flood-prone areas and housing projects in suburbs far from any jobs. And this is the richest city in India, while India is richer than practically every African state between South Africa and the Sahara. The poorest countries in the world, in turn, have very low emissions, even relative to their low GDPs – Bangladesh emits the equivalent of 1 metric ton of CO2 in greenhouse gases per capita annually.

Moreover, we can already know what climate adaptation will really mean. A 1-meter rise in sea level is projected to flood 17.5% of Bangladesh, corresponding at today’s population level to about 25 million people. Now, add the effects of crop failures: David Roberts quotes a paywalled Nature Climate Change article saying that rice yields go down by 10% per degree of nighttime temperature above 26. The minimal scenario would dwarf the Syrian refugee crisis, affecting 5 million people, by an order of magnitude. A more catastrophic scenario, involving flooding in Nigeria and increased droughts in the interior of Africa, could dwarf the Syrian crisis by two orders of magnitude.

The reaction to migration crises has been to militarize the border, to push the refugees away before they can get close and attract local sympathy. The US built a wall along much of the border with Mexico, long before Trump; Europe is stepping up patrols in the Mediterranean, and as we speak France is trying to open detention camps in Libya. This is not just a first-world reaction: India is fencing its border with Bangladesh. Climate adaptation means a little bit of money for flood control schemes, and a lot of money for pushing away refugees on threat of gunning them down, and building an entire apparatus of intermediate detention camps to be able to pretend that it’s not the fault of the US or Europe or Japan that the refugees are dying.

The implication is that, in parallel with the Anti-Ballistic Missile (ABM) Treaty in the Cold War, adaptation should be banned throughout the developed world. A country that spends money on trying to avoid the consequences of climate change is unlikely to be interested in avoiding it in the first place – just as a country with ABM protections is unlikely to be interested in avoiding nuclear war. Tackling a global problem like climate change requires ensuring that no single high-emissions country or economic bloc can insulate itself from its consequences.

Far from discouraging development in floodplains, first-world governments should prohibit any consideration of post-2000 temperature rise. If Secaucus is terra firma today, New Jersey should be compelled to treat it as terra firma forever and develop it as the TOD site that it is. If sea levels rise, none of the residents will be as negatively affected as the median Bangladeshi, but the residents might still agitate for future mitigation.

Infrastructure for Mature Cities

A post by Aaron Renn just made me remember something I said in the Straphangers Campaign forum ten years ago. I complained that New York was building too little subway infrastructure – where were Second Avenue Subway, Utica, Nostrand, various outer extensions in Queens and the Bronx that we crayonistas liked? Shanghai, I told people in the forum, was building a lot of subway lines at once, so why couldn’t New York? The answer is not about construction costs. Ten years ago, China’s construction costs relative to local incomes were about the same as those of New York; even today, the difference is small. Rather, it is that China is a fast-growing economy that’s spending a lot of its resources on managing this growth, whereas the US is a mature economy without infrastructure problems as urgent as those of developing countries.

Aaron posits that American cities are too conservative, in the sense of being timid rather than in the sense of being on the political right. He gives examples of forward-looking infrastructure projects that New York engaged in from the early 19th century to the middle of the 20th century: the Manhattan grid, the Erie Canal, the Croton Aqueduct, the subway, the Robert Moses-era highways and parks. Today, nothing of the sort happens. Aaron of course recognizes that “New, rapidly growing cities need lots of new infrastructure and plans. Mature cities need less new infrastructure.” The difference is that for me, this is where this line of questioning ends. New York is a mature city, and doesn’t need grand plans; it needs to invest in infrastructure based on the assumption that it will never again grow quickly.

If not grand plans like building the Manhattan grid far beyond the city’s then-built up area, then what should a mature city do? Aaron talks about dreaming big, and there is something to that, but it would take a profoundly different approach from what New York did when its population grew by 50% every decade. I stress that, as with my last post critiquing another blog post, I agree with a substantial part of what Aaron says and imagine that Aaron will treat many of the solutions I posit here as positive examples of thinking big.

Rationalization of Government

Mature societies have accumulated a great deal of kludge at all levels, coming from social structures and government programs that served the needs of previous generations, often with political compromises that are hard to understand today. Welfare programs are usually a kludge of different social security programs (for the disabled, for retirees, for various classes of unemployed people, sometimes even for students), housing benefits, reduced tax rates for staple goods like food, child credit, and in the US food stamps. A good deal of the impetus for basic income is specifically about consolidating the kludge into a single cash benefit with a consistent effective marginal tax rate.

In transportation, bus networks have often evolved incrementally, with each change making sense in local context. When a new housing development opened, the nearest bus would be extended to serve it. In Israel, which grew late enough to grow around buses and not rail, this was also true of dedicated industrial zones. In cities that used to have streetcar networks, some buses just follow the old streetcar routes; the Washington bus system even today distinguishes between former streetcars (which have numbers) and routes that were never streetcars (which use letters). Jarrett Walker‘s bus network redesigns are partly about reorganizing such systems around modern needs, based on modern understanding of the principles behind transit ridership.

Governance often needs to be rationalized as well. In the early 20th century, it was important to connect outlying neighborhoods to city center, and connections between lines were less important. This led to excessively radial surface transit (rapid transit is always radial), but also to rail lines that don’t always connect to one another well. Sometimes due to historical contingency the lines are run by separate agencies and have uncoordinated schedules and different fare systems charging extra for transfers. Occasionally even the same agency charges for bus-rail transfers, often because of a history of separate private operators before the public takeover. In the US and Canada, the special status of commuter rail, with different unions, fares, schedules, and management is of particular concern, because several cities could use commuter rail to supplement the rest of the transit network.

In New York, this points toward the following agenda:

  • Modernization of commuter rail, with full fare integration with the subway and buses, proof-of-payment fare collection to reduce operating costs, high off-peak frequency on the local lines, and through-running where there is infrastructure for it (i.e. Penn Station).
  • Some bus service reorganization. New York already has extensive frequent buses, but some of its network is still questionable, for example some branches of the Third/Lexington and Madison/Fifth one-way pairs in Harlem.
  • Subway reorganization. The subway branches too much, and at several places it could have higher capacity if it reduced the extent of reverse-branching; see discussion here and in comments here. Some elevated lines could also see their stops change to support better transfers, including the J/M/Z at Broadway and Manhattan to transfer to the G, and maybe even the 7 at 108th Street to enable a transfer to a straightened Q23 bus.
  • Fare integration with PATH, and demolition of the false walls between the PATH and the F/M trains on Sixth Avenue, to enable cross-platform transfers.

Serve, Don’t Shape

There are two models for building new infrastructure: serve, and shape. Serve means focusing on present-day economic and demographic patterns. Shape means expecting the project to change these patterns, the “build it and they will come” approach. When New York built the 7 train to Flushing, Flushing already existed as a town center but much of the area between Long Island City and Flushing was open farmland. I’ve argued before that third-world cities should use the shape model. In contrast, mature cities, including the entire developed world except a few American Sunbelt cities and analogs in Canada and Australia, should use the serve model.

The serve model flies in the face of the belief that public transit can induce profound changes in urban layout. In reality, some local transit-oriented development is possible, but the main center of New York will remain Midtown; so far Hudson Yards seems like a flop. In the suburbs, more extensive redevelopment is possible, with apartment buildings and mixed uses near train stations. But these suburbs, built after WW2, are less mature than the city proper. In fast-growing cities in North America outside the traditional manufacturing belt the shape model still has validity – Vancouver, still a relatively new city region in the 1980s, got to shape itself using SkyTrain. But in New York, there is no chance.

This also has some ethnic implications. Jarrett likes to plan routes without much regard for social circumstances, except perhaps to give more bus service to a lower-income area with lower car ownership. But in reality, it is possible to see ethnic ties in origin-and-destination transit trips. This is why there are internal Chinatown buses connecting Chinatown, Flushing, and Sunset Park, and a bus connecting two different ultra-Orthodox neighborhoods in Brooklyn. In Washington, there is origin and destination data, and there are noticeable ties between black neighborhoods, such as Anacostia and Columbia Heights.

In a mature city with stable ethnic boundaries (Harlem has been black for ninety years), it is possible to plan infrastructure around ethnic travel patterns. This means that as New York disentangles subway lines to reduce branching, it should try choosing one-seat rides that facilitate known social ties, such as between Harlem and Bedford-Stuyvesant. While New York’s ethnic groups are generally integrated, this has special significance in areas with a mixture of linguistic or religious groups with very little intermarriage, such as Israel, which has two large unassimilated minorities (Arabs, and ultra-Orthodox Jews); Israeli transportation planning should whenever possible take into account special ultra-Orthodox travel needs (e.g. large families) and intra-ethnic connections such as between Bnei Brak and Jerusalem or between Jaffa and Nazareth.

Integrated Planning

A few years ago, I wrote a post I can no longer find talking about building the minimum rail infrastructure required for a given service plan. In comments, Keep Houston Houston replied that no, this makes it really difficult to add future capacity if demand grows. For example, a single-track line with meets optimized for half-hourly service requires total redesign if demand grows to justify 20-minute frequency. In a growing city, this means infrastructure should be planned for future-proofing, with double track everywhere, no reliance on timed overtakes, and so on. In a mature city, this isn’t a problem – growth is usually predictable.

It is relatively easy to integrate infrastructure planning and scheduling based on today’s travel patterns, and impossible to integrate them based on the future travel patterns of a fast-growing city such as Lagos or Nairobi. But in a slow-growing city like New York, future integration isn’t much harder than present-day integration. Alone among North American cities, New York has high transit mode share, making such integration even easier – transit usage could double with Herculean effort, but there is no chance that a real transit revival would quadruple it or more, unlike in cities that are relatively clean slates like Los Angeles.

Since the mature city does not need too much new infrastructure, it is useful to build infrastructure to primarily use existing infrastructure more efficiently. One example of this is S-Bahn tunnels connecting two stub-end lines; these are also useful in growing cities (Berlin built the Stadtbahn in the 1880s), but in mature cities their relative usefulness is higher, because they use preexisting infrastructure. This is not restricted to commuter rail: there is a perennial plan in New York to build a short tunnel between PATH at World Trade Center and the 6 train at City Hall and run through-service, using the fact that PATH’s loading gauge is similar to that of the numbered subway lines.

In New York, this suggests the following transit priorities:

  • Open commuter rail lines and stations based on the quality of transfers to the subway and the key bus routes. For example, Penn Station Access for Metro-North should include a stop at Pelham Parkway for easy transfer to the Bx12 bus, and a stop at Astoria for easy transfer to the subway.
  • Investigate whether a PATH-6 connection is feasible; it would require no new stations, but there would be construction difficulties since the existing World Trade Center PATH station platforms are in a loop.
  • Change subway construction priorities to emphasize lines that reduce rather than add branching. In particular, Nostrand may be a higher priority than Utica, and both may be higher priorities than phases 3 and 4 of Second Avenue Subway. A subway line under Northern Boulevard in Queens may not be feasible without an entirely new Manhattan trunk line.
  • Build commuter rail tunnels for through-running. The Gateway project should include a connection to Grand Central rather than Penn Station South, and should already bake in a choice of which commuter lines on each side match to which commuter lines on the other side. Plan for commuter rail lines through Lower Manhattan, connecting the LIRR in Brooklyn with New Jersey Transit’s Erie Lines, and, accordingly, do not connect any of the lines planned for this system to Penn Station (such as with the circuitous Secaucus Loop in the Gateway project).

Conclusion

New York still needs infrastructure investment, like every other city. Such investment requires thinking outside the box, and may look radical if it forces different agencies to cooperate or even amalgamate. But in reality the amount of construction required is not extensive. More deeply, New York will not look radically different in the future from how it looks today. Technological fantasies of driverless flying cars aside, New York’s future growth is necessarily slow and predictable, and cities in that situation need to invest in infrastructure accordingly.

In my post about third-world transit, I posited an epistemological principle that if the presence of a certain trait makes a certain solution more useful, then the absence of the trait should make the solution less useful. The shape vs. serve argument comes from this principle. The same is true of the emphasis on consolidating the kludge into a coherent whole and then building strategically to support this consolidation. A fast-growing city has no time to consolidate, and who’s to say that today’s consolidation won’t be a kludge in thirty years? A mature city has time, and has little to worry about rapid change obsoleting present-day methods.

But at the same time, the same epistemology means that these changes are less critical in a mature city. In the third world, everything is terrible; in the first world, most things are fine. New York’s transportation problems are painful for commuters, but ultimately, they will not paralyze the city. It will do well even if it doesn’t build a single kilometer of subway in the future. Nothing is indispensable; this means that, in the face of high costs, often the correct alternative may be No Build. This illustrates the importance of improving cost-effectiveness (equally important in the third world, but there the problem is the opposite – too many things are indispensable and there isn’t enough money for all of them).

I emphasize that this does not mean transportation is unimportant. That New York will not be destroyed if it stops building new infrastructure does not mean that new infrastructure is of no use for the city. The city needs to be able to facilitate future economic and demographic growth and solve lingering social problems, and better infrastructure, done right, can play a role in that. New York will most likely look similar in 2067 to how it looks in 2017, but it can still use better infrastructure to be a better and more developed city by then.

Greenbelts Help Cars

A number of major cities, most notably London, have designated areas around their built-up areas as green belts, in which development is restricted, in an attempt to curb urban sprawl. The towns within the green belt are not permitted to grow as much as they would in an unrestricted setting, where the built-up areas would merge into a large contiguous urban area. Seeking access to jobs in the urban core, many commuters instead live beyond the greenbelt and commute over long distances. There has been some this policy’s effect on housing prices, for example in Ottawa and in London by YIMBY. In the US, this policy is less common than in Britain and Canada, but exists in Oregon in the form of the urban growth boundaries (UGBs), especially around Portland. The effect has been the same, replacing a continuous sprawling of the urban area with discontinuous suburbanization into many towns; the discontinuous form is also common in Israel and the Netherlands. In this post, I would like to explain how, independently of issues regarding sprawl, such policies are friendlier to drivers than to rail users.

Let us start by considering what affects the average speed of cars and what affects that of public transit. On a well-maintained freeway without traffic, a car can easily maintain 130 km/h, and good cars can do 160 or more on some stretches. In urban areas, these speeds are rarely achievable during the day; even moderate traffic makes it hard to go much beyond 110 or 120. Peak-direction commutes are invariably slower. Moreover, when the car gets off the freeway and onto at-grade arterial roads, the speed drops further, to perhaps 50 or less, depending on density and congestion.

Trains are less affected by congestion. On a well-maintained, straight line, a regional train can go at 160 km/h, or even 200 km/h for some rolling stock, even if headways are short. The busiest lines are typically much slower, but for different reasons: high regional and local traffic usually comes from high population density, which encourages short stop spacing, such that there may not be much opportunity for the train to go quickly. If the route is curvy, then high density also makes it more difficult to straighten the line by acquiring land on the inside of the curves. But by and large, slowdowns on trains come from the need to make station stops, rather than from additional traffic.

Let us now look at greenbelts of two kinds. In the first kind, there is legacy development within the greenbelt, as is common around London. See this example:

greenbelt1

 

The greenbelt is naturally in green, the cities are the light blue circles with the large central one representing the big city, and the major transportation arteries (rail + freeway) are in black. The towns within the greenbelt are all small, because they formed along rail stops before mass motorization; the freeways were built along the preexisting transportation corridors. With mass motorization and suburbanization, more development formed right outside the greenbelt, this time consisting of towns of a variety of sizes, typically clustering near the freeways and railways for best access to the center.

The freeways in this example metro area are unlikely to be very congested. Their congestion comes from commuters into the city, and those are clustered outside the greenbelt, where development is less restricted. Freeways are widened based on the need to maintain a certain level of congestion, and in this case, this means relatively unimpeded traffic from the outside of the green belt right up until the road enters the big city. Under free development, there would be more suburbs closer to the city, and the freeway would be more congested there; travel times from outside the greenbelt would be longer, but more people would live closer to the center, so it would be a wash.

In contrast, the trains are still going to be slowed down by the intermediate stops. The small grandfathered suburbs have no chance of generating the rail traffic of larger suburbs or of in-city stops, but they still typically generate enough that shutting them down to speed traffic is unjustified, to say nothing of politically impossible. (House prices in the greenbelt are likely to be very high because of the tight restrictions, so the commuters there are rich people with clout.) What’s more, frequency is unlikely to be high, since demand from within the greenbelt is so weak. Under free development, there might still be more stops, but not very many – the additional traffic generated by more development in those suburbs would just lead to more ridership per stop, supporting higher frequency and thus making the service better rather than worse.

Let us now look at another greenbelt, without grandfathered suburbs, which is more common in Canada. This is the same map as before, with the in-greenbelt suburbs removed:

greenbelt2

In theory, this suburban paradigm lets both trains and cars cruise through the unbuilt area. Overall commutes are longer because of the considerable extra distance traveled, but this distance is traversed at high speed by any mode; 120 km/h is eminently achievable.

In practice, why would there be a modern commuter line on any of these arteries? Commuter rail modernization is historically a piecemeal program, proceeding line by line, prioritizing the highest-trafficked corridors. In Paris, the first commuter line to be turned over to the Metro for operation compatible with city transit, the Ligne de Sceaux, has continuous urban development for nearly its entire length; a lightly-trafficked outer edge was abandoned shortly after the rest of the line was electrified in 1938. If the greenbelt was set up before there was significant suburbanization in the restricted area, it is unlikely that there would have been any reason to invest in a regional rail line; at most there may be a strong intercity line, but then retrofitting it to include slower regional traffic is expensive. Nor is there any case for extending a high-performing urban transit line to or beyond a greenbelt. Parts of Grand Paris Express, namely Lines 14 and 11, are extended from city center outward. In contrast, in London, where the greenbelt reduces density in the suburbs, high investment into regional rail focuses on constructing city-center tunnels in Crossrail and Crossrail 2 and connecting legacy lines to them. In cities that do not even have the amount of suburban development of the counties surrounding London, there is even less justification for constructing new transit.

Now, you may ask, if there’s no demand for new urban transit lines, why is there demand for new highways? After all, if there was not much regional travel into these suburbs historically, why would there be enough car traffic to justify high investment into roads? The answer is that at low levels of traffic, it’s much cheaper to build a road than to build and operate a railway. This example city has no traffic generators in the greenbelt, except perhaps parks, so roads are cheap to build and have few to no grade crossings to begin with, making it easier to turn them into full freeways. The now-dead blog Keep Houston Houston made this point regarding a freeway in Portland, which was originally built as an arterial road in a narrow valley and had few at-grade intersections to be removed. At high levels of demand, the ability to move the same number of people on two tracks as on fourteen lanes of freeway makes transit much more efficient, but at low demand levels, rail still needs two tracks or at least one with passing sidings, and high-speed roads need four lanes and in some cases only two.

The overall picture in which transit has an advantage over cars at high levels of density is why high levels of low-density sprawl are correlated with low transit usage. But I stress that even independently of sprawl, greenbelts are good for cars and bad for transit. A greenbelt with legacy railway suburbs is going to feature trains going at the normal speed of a major metro area, and cars going at the speed of a more spread out and less populated region. Even a greenbelt without development is good urban geography for cars and bad one for transit.

As a single exception, consider what happens when a greenbelt is reserved between two major nodes. In that specific case, an intercity line can more easily be repurposed for commuting purposes. The Providence Line is a good example: while there’s no formal greenbelt, tight zoning restrictions in New England even in the suburbs lead to very low density between Boston and Providence, which is nonetheless served by good infrastructure thanks to the strength of intercity rail travel. The MBTA does not make good use of this infrastructure, but that’s beside the point: there’s already a high-speed electrified commuter line between the two cities, with widely spaced intermediate stops allowing for high average speeds even on stopping trains and overtakes that are not too onerous; see posts of mine here and here. What’s more, intercity trains can be and are used for commutes from Providence to Boston. For an analogous example with a true greenbelt, Milton Keynes plays a role similar to Providence to London’s Boston.

However, this exception is uncommon. There aren’t enough Milton Keyneses on the main intercity lines to London, or Providences on the MBTA, to make it possible for enough transit users to suburbanize. In cities with contiguous urban development, such as Paris, it’s easier. The result of a greenbelt is that people who do not live in the constrained urban core are compelled to drive and have poor public transportation options. Once they drive, they have an incentive to use the car for more trips, creating more sprawl. This way, the greenbelt, a policy that is intended to curb sprawl and protect the environment, produces the exact opposite results: more driving, more long-distance commuting, a larger urban footprint far from the core.

A Theory of Zoning and Local Decisionmaking

This weekend there’s a conference in the US, YIMBY 2016, by a national network of activists calling for more housing. I am not there, but I see various points raised there via social media. One is a presentation slide that says “NIMBYism is a collective action problem: no single neighborhood can lower prices by upzoning; might still be in everyone’s interest to upzone at city/state level.” I think this analysis is incorrect, and in explaining why, I’d like to talk about a theory of how homeowners use zoning to create a housing shortage to boost their own property values, and more generally how long-time residents of a city use zoning to keep out people who are not like them. In this view,zoning is the combination of a housing cartel, and a barrier to internal migration.

For years, I’ve had trouble with the housing cartel theory, because of a pair of observations. The first is that, contra the presentation at YIMBY, zoning is driven by homeowners rather than by renters; for an overview, see the work of William Fischel. The second is that restrictive zoning typically correlates with local decisionmaking, such as in a neighborhood or small city, while lax zoning typically correlates with higher-level decisionmaking, such as in a city with expansive municipal boundaries or in an entire province or country; see below for more on this correlation. These two observations together clash with the housing cartel theory, for the inverse of the reason in the above quote from the YIMBY presentation: it’s more effective to create a housing shortage in a large area than in a small one.

To a good approximation, land value equals (housing price – housing construction cost)*allowed density. If a small municipality upzones, then as in the quote, housing price doesn’t change much, but allowed density grows, raising the price a homeowner can get by selling their house to developers who’d build an apartment building. In contrast, if a large municipality upzones then housing prices will fall quite a bit as supply grows, and depending on the price elasticity, land value might well go down. If x = housing price/housing construction cost and e = price elasticity for housing, i.e. price is proportional to density^(-1/e), then maximum land value occurs when x = e/(e-1), provided e > 1; if e < 1 then maximum value occurs when x is arbitrarily large. Price elasticity is much higher in a small municipality, since even a large increase in local housing supply has a small effect on regional supply, limiting its ability to reduce prices. This implies that, to maximize homeowner value, small municipalities have an incentive to set density limits at a higher level than large municipalities, which will be seen in faster housing growth relative to population growth.

What we see is the exact opposite. Consider the following cases, none a perfect natural experiment, but all suggestive:

1. In the Bay Area, we can contrast San Francisco (a medium-size urban municipality), San Jose and generally Santa Clara County (San Jose is medium-size for a central city and very large for a suburb), and San Mateo County (comprising small and medium-size suburbs). San Mateo County is by far the stingiest of the three about permitting housing: over the last three years it’s averaged 1,000 new housing units per year (see here); in 2013, the corresponding figures elsewhere in the Bay Area were 2,277 new housing units in San Francisco and 5,245 in Santa Clara County. Per thousand people (not per housing unit), this is 2.63 in San Francisco, 2.73 in Santa Clara, and 1.31 in San Mateo. In Alameda County, comprising medium-size cities and suburbs, with a less hot housing market because of the distance from Silicon Valley jobs, growth was 2,474 units, 1.51 per 1,000 people. In small rich Silicon Valley municipalities like Palo Alto and Menlo Park, NIMBYs have effectively blocked apartment construction; in much larger and still rich San Jose, the city has a more pro-growth outlook.

2. Among the most important global cities – New York, Paris, London, and Tokyo – Tokyo has by far the fastest housing stock growth, nearly 2% a year; see article by Stephen Smith. In Japan, key land use decisions are made by the national government, whereas in Paris, London, and New York, decision is at a lower level. London builds more than New York and Paris; its municipal limit is much looser than Paris’s, with 8.5 million people to Paris’s 2.2 million even though their metro areas have similar populations. New York has a fairly loose limit as well, but the development process empowers lower-level community boards, even though the city has final authority.

3. Canada has a relatively permissive upzoning process, and in Ontario, the planning decisions are made at the provincial level, resulting in about 1.3% annual housing growth in Toronto in the previous decade; in the same period, San Jose’s annual housing growth was about 1% and San Francisco’s was 0.9%.

4. France has recently made a national-level effort to produce more housing in the Paris region, especially social housing, due to very high housing prices there. Last decade, housing production in Ile-de-France was down to about 30,000-35,000 per year, averaging to 2.6 per 1,000 people, similar to San Francisco; see PDF-pp. 4-5 here and the discussion here. With the new national and regional effort at producing more social housing, plans appear to be on track to produce 30,000 annual units of social housing alone in the next few years; see PDF-p. 6 here. With 7,000 annual units within city limits, Paris expects to build somewhat more per capita than the rest of the region.

In France, the combination of a national focus on reducing housing burden and the observation that higher-level decisionmaking produces more housing makes sense. But elsewhere, we need to ask how come homeowners aren’t able to more effectively block construction.

My theory is that the answer involves internal migration. Consider the situation of Palo Alto: with Stanford and many tech jobs, it is prime location, and many people want to move there. The homeowners are choosing the zoning rule that maximizes their ability to extract rents from those people, in both the conventional sense of the word rent and the economic sense. Now consider decisionmaking at the level of the entire state of California. California can raise housing prices even more effectively than Palo Alto can by restricting development, but unlike Palo Alto, California consists not just of residents of rich cities, but also of residents of other cities, who would like to move to Palo Alto. In the poorer parts of the state, there’s not much point in restrictive zoning, because there isn’t that much demand for new housing, except perhaps from people who cannot afford San Francisco or Los Angeles and are willing to endure long commutes. On the contrary, thanks to the strength of internal migration, a large fraction of prospective residents of Palo Alto live elsewhere in California. Nor do people in poor areas, where houses aren’t worth much as investments, gain much from raising house prices for themselves; the ability to move to where the good jobs are is worth more than raising housing prices by a few tens of thousands of dollars. This means that the general interest in California is to make Palo Alto cheaper rather than more expensive. The same is true of Japan and Tokyo, or France and Paris, or Ontario and Toronto.

While superficially similar to the point made in the presentation quoted at the beginning of this post, my theory asserts the opposite. The issue is not that individual municipalities see no benefit in upzoning since it wouldn’t reduce rents by much. It’s that they see net harm from upzoning precisely because it would reduce rents. It is not a collective action problem: it is a problem of disenfranchisement, in which the people who benefit from more development do not live in the neighborhoods where the development would be taking place. High-level decisionmaking means that people who would like to move to a rich area get as much of a vote in its development policy as people who already live there and have access to its amenities, chief of which is access to work. It disempowers the people who already have the privilege of living in these areas, and empowers the people who don’t but would like to.

Individual rich people can be virtuous. Rich communities never are. They are greedy, and write rules that keep others out and ruthlessly eliminate any local effort to give up their political power. They will erect borders and fences, exclude outsiders, and demagogue against revenue sharing, school integration, and upzoning. They will engage in limited charity – propping up their local poor (as San Francisco protects low-income lifelong San Franciscans via rent control), and engaging in symbolic, high-prestige giving, but avoid any challenge to their political power. Upzoning is not a collective action problem; it is a struggle for equal rights and equal access to jobs regardless of which neighborhood, city, or region one grew up in.

Modeling Anchoring

Jarrett Walker has repeatedly called transit agencies and city zoning commissions to engage in anchoring: this means designing the city so that transit routes connect two dense centers, with less intense activity between them. For example, he gives Vancouver’s core east-west buses, which connect UBC with dense transit-oriented development on the Expo Line, with some extra activity at the Canada Line and less intense development in between; Vancouver has adopted his ideas, as seen on PDF-page 15 of a network design primer by Translink. In 2013, I criticized this in two posts, making an empirical argument comparing Vancouver’s east-west buses with its north-south buses, which are not so anchored. Jarrett considers the idea that anchoring is more efficient to be a geometric fact, and compared my empirical argument to trying to empirically compute the decimal expansion pi to be something other than 3.1415629… I promised that I would explain my criticism in more formal mathematical terms. Somewhat belatedly, I would like to explain.

First, as a general note, mathematics proves theorems about mathematics, and not about the world. My papers, and those of the other people in the field, have proven results about mathematical structures. For example, we can prove that an equation has solutions, or does not have any solutions. As soon as we try to talk about the real world, we stop doing pure math, and begin doing modeling. In some cases, the models use advanced math, and not just experiments: for example, superstring theory involves research-level math, with theorems of similar complexity to those of pure math. In other cases, the models use simpler math, and the chief difficulty is in empirical calibration: for example, transit ridership models involve relatively simple formulas (for example, the transfer penalty is a pair of numbers, as I explain here), but figuring out the numbers takes a lot of work.

With that in mind, let us model anchoring. Let us also be completely explicit about all the assumptions in our model. The city we will build will be much simpler than a real city, but it will still contain residences, jobs, and commuters. We will not deal with transfers; neither does the mental model Jarrett and TransLink use in arguing for anchoring (see PDF-p. 15 in the primer above again to see the thinking). For us, the city consists of a single line, going from west to east. The west is labeled 0, the east is labeled 1, and everything in between is labeled by numbers between 0 and 1. The city’s total population density is 1: this means that when we graph population density on the y-axis in terms of location on the x-axis, the total area under the curve is 1. Don’t worry too much about scaling – the units are all relative anyway.

Let us now graph three possible distributions of population density: uniform (A), center-dominant (B), and anchored (C).

cityA cityBcityC

Let us make one further assumption, for now: the distributions of residences and jobs are the same, and independent. In city (A), this means that jobs are uniformly distributed from 0 to 1, like residences, and a person who lives at any point x is equally likely to work at any point from 0 to 1, and is no more likely to work near x than anyone else. In city (B), this means that people are most likely to work at point 0.5, both if they live there and if they live near 0 or 1; in city (C), this means that people are most likely to work at 0 or 1, and that people who live at 0 are equally likely to work near 0 and near 1.

Finally, let us assume that there is no modal splitting and no induced demand: every employed person in the city rides the bus, exactly once a day in each direction, once going to work and once going back home, regardless of where they live and work. Nor do people shift their choice of when to work based on the network: everyone goes to work in the morning peak and comes back in the afternoon peak.

With these assumptions in mind, let us compute how crowded the buses will be. Because all three cities are symmetric, I am only going to show morning peak buses, and only in the eastbound direction. I will derive an exact formula in city (A), and simply state what the formulas are in the other two cities.

In city (A), at point x, the number of people who ride the eastbound morning buses equals the number of people who live to the west of x and work to the right of x. Because the population and job distributions are uniform, the proportion of people who live west of x is x, and the proportion of people who work east of x is 1-x. The population and job distributions are assumed independent, so the total crowding is x(1-x). Don’t worry too much about scaling again – it’s in relative units, where 1 means every single person in the city is riding the bus in that direction at that time. The formula y = x(1-x) has a peak when x = 0.5, and then y = 0.25. In cities (B) and (C), the formulas are:

(B): y = \begin{cases}2x^2(1 - 2x^2) & \mbox{ if } x \leq 1/2\\ 2(1-x)^2(1 - 2(1-x)^2) & \mbox{ if } x > 1/2\end{cases}

(C): y = \begin{cases}(2x-2x^2)(1 - 2x + 2x^2) & \mbox{ if } x \leq 1/2\\ (2(1-x)-2(1-x)^2)(1 - 2(1-x) + 2(1-x)^2) & \mbox{ if } x > 1/2\end{cases}

Here are their graphs:

cityAcrowd cityBcrowd cityCcrowd

Now, city B’s buses are almost completely empty when x < 0.25 or x > 0.75, and city C’s buses fill up faster than city A’s, so in that sense, the anchored city has more uniform bus crowding. But the point is that at equal total population and equal total transit usage, all three cities produce the exact same peak crowding: at the midpoint of the population distribution, which in our three cases is always x = 0.5, exactly a quarter of the employed population lives to the west and works to the east, and will pass through this point on public transit. Anchoring just makes the peak last longer, since people work farther from where they live and travel longer to get there. In a limiting case, in which the population density at 0 and 1 is infinite, with half the population living at 0 and half at 1, we will still get the exact same peak crowding, but it will last the entire way from 0 to 1, rather than just in the middle.

Note that there is no way to play with the population distribution to produce any different peak. As soon as we assume that jobs and residences are distributed identically, and the mode share is 100%, we will get a quarter of the population taking transit through the midpoint of the distribution.

If anything, the most efficient of the three distributions is B. This is because there’s so little ridership at the ends that it’s possible to run transit at lower frequency at the ends, overlaying a route that runs the entire way from 0 to 1 to a short-turn route from 0.25 to 0.75. Of course, cutting frequency makes service worse, but at the peak, the base frequency is sufficient. Imagine a 10-minute bus going all the way, with short-turning overlays beefing frequency to 5 minutes in the middle half. Since the same resources can more easily be distributed to providing more service in the center, city B can provide more service through the peak crowding point at the same cost, so it will actually be less crowded. This is the exact opposite of what TransLink claims, which is that city B would be overcrowded in the middle whereas city C would have full but not overcrowded buses the entire way (again, PDF-p. 15 of the primer).

In my empirical critique of anchoring, I noted that the unanchored routes actually perform better than the anchored ones in Vancouver, in the sense that they cost less per rider but also are less crowded at the peak, thanks to higher turnover. This is not an observation of the model. I will note that the differences in cost per rider are not large. The concept of turnover is not really within the model’s scope – the empirical claim is that the land use on the unanchored routes lends itself to short trips throughout the day, whereas on the anchored ones it lends itself to peak-only work trips, which produce more crowding for the same total number of riders. In my model, I’m explicitly ignoring the effect of land use on trips: there are no induced trips, just work trips at set times, with 100% mode share.

Let us now drop the assumption that jobs and residences are identically distributed. Realistically, cities have residential and commercial areas, and the model should be able to account for this. As one might expect, separation of residential and commercial uses makes the system more crowded, because travel is no longer symmetric. In fact, whereas under the assumption the peak crowding is always exactly a quarter of the population, if we drop the assumption the peak crowding is at a minimum a quarter, but can grow up to the entire population.

Consider the following cities, (D), (E), and (F). I am going to choose units so that the total residential density is 1/2 and so is the total job density, so combined they equal 1. City (D) has a CBD on one side and residences on the other, city (E) has a CBD in the center and residences on both sides, and city (F) is partially mixed-use, with a CBD in the center and residences both in the center and outside of it. Residences are in white, jobs are in dark gray, and the overlap between residences and jobs in city (F) is in light gray.

cityD cityE cityF

We again measure crowding on eastbound morning transit. We need to do some rescaling here, again letting 1 represent all workers in the city passing through the same point in the same direction. Without computing, we can tell that in city (D), at the point where the residential area meets the commercial area, which in this case is x = 0.75, the crowding level is 1: everyone lives to the west of this point and works to its east and must commute past it. Westbound morning traffic, in contrast, is zero. City (E) is symmetric, with peak crowding at 0.5, at the entry to the CBD from the west, in this case x = 0.375. City (F) has crowding linearly growing to 0.375 at the entry to the CBD, and then decreasing as passengers start to get off. The formula for eastbound crowding is,

(F): y = \begin{cases}x & \mbox{ if } x < 3/8\\ x(5/2 - 4x) & \mbox{ if } 3/8 \leq x \leq 5/8\\ 0 & \mbox{ if } x > 5/8\end{cases}

cityDcrowd cityEcrowd cityFcrowd

In city (F), the quarter of the population that lives in the CBD simply does not count for transit crowding. The reason is that, with the CBD occupying the central quarter of the city, at any point from x = 0.375 east, there are more people who live to the west of the CBD getting off than people living within the CBD getting on. This observation remains true down to when (for a symmetric city) a third of the population lives inside the CBD.

In city (B), it’s possible to use the fact that transit runs empty near the edges to run less service near the edges than in the center. Unfortunately, it is not possible to use the same trick in cities (E) and (F), not with conventional urban transit. The eastbound morning service is empty east of the CBD, but the westbound morning service fills up; east of the CBD, the westbound service is empty and the eastbound service fills up. If service has to be symmetric, for example if buses and trains run back and forth and make many trips during a single peak period, then it is not possible to short-turn eastbound service at the eastern edge of the CBD. In contrast, if it is possible to park service in the center, then it is possible to short-turn service and economize: examples include highway capacity for cars, since bridges can have peak-direction lanes, but also some peaky commuter buses and trains, which make a single trip into the CBD per vehicle in the morning, park there, and then make a single trip back in the afternoon. Transit cities relies on services that go back and forth rather than parking in the CBD, so such economies do not work well for them.

A corollary of the last observation is that mixed uses are better for transit than for cars. Cars can park in the CBD, so for them, it’s fine if the travel demand graph looks like that of city (E). Roads and bridges are designed to be narrower in the outskirts of the region and wider near the CBD, and peak-direction lanes can ensure efficient utilization of capacity. In contrast, buses and rapid transit trains have to circulate; to achieve comparable peak crowding, city (E) requires twice as much service as perfect mixed-use cities.

The upshot of this model is that the land use that best supports efficient use of public transit is mixed use. Since all rich cities have CBDs, they should work on encouraging more residential land uses in the center and more commercial uses outside the center, and not worry about the underlying distribution of combined residential and job density. Since CBDs are usually almost exclusively commercial, any additional people living in the center will not add to transit crowding, even as they ride transit to work and pay fares. In contrast, anchoring does not have any effect on peak crowding, and on the margins makes it worse in the sense that the maximum crowding level lasts longer. This implies that the current planning strategy in Vancouver should be changed from encouraging anchoring to fill trains and buses for longer to encouraging more residential growth Downtown and in other commercial centers and more commercial growth at suitable nodes outside the center.