Columbus, OH

Columbus, Ohio is one of the fastest growing cities in the north, with population growing by 17.5% from 2000-2014. It is the largest city in Ohio, and houses both the state capitol and the Ohio State University, with enrollment of over 50,000 students.

Thoughts from Columbus

MLK Way part 9: Summary data

As I’ve worked through this series I’ve had to improve my code to deal with more edge cases. I buffered the street by about 100 meters to smooth out inconsistencies in the census tract boundaries. I wound up splitting out the three New York City boroughs which have MLK Ways, because their stories are very different. I special-cased a few places where the tract selections were not comparable. I had to code for non-reporting Census tracts.

There are probably still some data issues, especially in cities I didn’t build a map for. But overall I’m fairly comfortable with the data set, given the caveats I’ve already raised. The biggest problem I see remaining is that the cost-of-living index is using national numbers, which understate the cost of living changes in expensive metros like Oakland. Some of the cities also have very small sample sizes, especially in the ACS data.

That being said, here are some summaries.


I find two things interesting about the real median income numbers. One is that they are entirely split down the middle: 29 cities are increasing and 29 are decreasing. The average decrease is 0.3%, a mere $72/year. So it’s more or less a wash; the developing cities are evenly balancing out the decaying cities.

The other thing is that the top two cities are Charlotte, NC and Columbus, OH, and the bottom is St. Paul, MN, all of which were part of my field work. That’s not entirely coincidental; the fact that Charlotte and Columbus are expanding is part of why they were selected. But it’s mostly just a cute data point; in all three cities the MLK Way is only a few blocks long, so they show up as outliers partly because the sample size is small.


The racial dynamics, on the other hand, aren’t balanced at all; the proportion of Black people in the tracts near MLK Way fell in 55 of 59 cities, with a median decrease of 11.9%. In aggregate, these areas lost 122K Black people.

St. Paul and Columbus show up again; this time as the top and bottom in proportional change. Black population grew by over 30% in St. Paul’s neighborhoods, more than three times the amount of the #2 city (Toledo, OH). Columbus’ proportion of Black people fell by a similar amount (28%). This is a much more common outcome; Black representation fell by more than 10 percentage points in 22 of the study cities.

Chart of changes in Black population, showing that many more cities lost than gained Black population

The change in proportion of Whites is not quite so dramatic, though the overall trend is the same. White representation increased by over 10% in nine of the study cities, which gained 101K White persons in aggregate.

Graph showing increase or decrease in White population in 58 cities, with 35 cities increasing.

The gap between the increase in white people and the decrease in Black people mostly is due to an increase in Hispanic people. Hispanic representation increased by over 10 percentage points in 11 of the study cities, and the total population increase was 98K persons.

Income and population

As a whole, income increased where the White population increased. Every city where real income increased by more than 20% also increased in White representation (top right quadrant). Every city where White representation declined by over 10% saw a decrease in income (lower left quadrant). The correlation between the two is regrettably strong (r=0.61).
Note: This chart omits St. Paul to improve the visual display.
Scatter plot, displaying a correlation between increase in White population and increase in income


Returning to the premise of the Bike Lab: Did an increase in White population lead to more cycling? Only a bit. Whiteness is correlated with cycling, but not nearly as strongly as with income (r=0.40). Cycling rose overall (by 0.5 percentage points), but there are cities like Columbus where White representation rose by over 20 percentage points while cycling rates went down.

Note: This chart omits Portland to improve the visual display.

Scatter plot showing a weak relationship of White population to cycling rates.

When I visited Columbus I stayed not far from MLK Blvd. That eastern area of town does face some real barriers to cycling; getting to downtown requires a harrowing freeway crossing and a jaunt on a one-way high speed three-lane road. So that might explain why cycling didn’t increase, but it doesn’t really explain why it decreased. My hypothesis would be that white people view poor infrastructure as more of a barrier to cycling than Black people do.

Map output

I wanted to get as much data analysis done on my home computer as I could before I head out for study abroad in Berlin. It’s not entirely perfect yet, but I have a workflow which generates these maps right out of Python for all of my cities, with some provision for longitudinal comparisons (limited by data availability). Code has improved enough that on that setup (Mac Pro 2013, 6-core, 12 GB) it takes about a day to generate these for 100 cities.

To do still are to deal with projection issues (they’re all in “web Mercator” because that’s what Leaflet uses), and centering issues caused by inconsistencies between folium, selenium, and PhantomJS. And to improve legends and captions. But it’s pretty cool, if you ask me.


[flickr_set id=”72157683818724493″ max_num_photos=”21″]

See all maps from 2015


[flickr_set id=”72157686472156785″ max_num_photos=”21″]

See all maps from 2010

Auto-generating maps

I’m working on scaling up the data analysis from the thesis, and I’m making some good progress, thanks to Folium. This is a really cool package for creating Leaflet.js maps from Python data, and writing them out to JSON, which means they can be used as interactive web maps. Or, what I’m going to be doing more of is grabbing the JSON/HTML output with PhantomJS and writing them out to image files, like the one included here (from Columbus, OH). This is one of the coolest-looking visualizations so far: census tracts are colored by percentage of workers with a commute less than 20 minutes (ACS 2015), and road segments are colored by access to jobs, attenuated by segment grade and circuity. Dedicated bike infrastructure (as reported by OpenStreetMaps) is colored in green.

I’m pretty close to being able to run this on an arbitrary number of cities: just need to make the code a little more robust.

Distance thresholds

After looking at the job connectivity maps, I was curious to explore the idea that densities above a certain level led to walking more than cycling. I don’t have enough data to make a definitive statement, but I did find an interesting phenomenon related to connectivity in Columbus. The two maps below are connectivity to jobs within 1.5 miles along the cycling network, and within 0.5 miles.

Access to jobs within 1.5 miles along the cycling network

Access to jobs within 0.5 miles along the cycling network

The difference between these two maps is stark, and curious. Basically what it’s saying is that Columbus has three disconnected zones where jobs are very close, broken up by areas of low job access. The north area is near OSU, the southeast is downtown, and the southwest is the Hilltop district, which appears to have access to a number of light industrial jobs. The disconnection between these areas partly stems from physical characteristics; the Scioto River, along with freeways and rail lines. But it also appears to be related to depressed areas of the city. Directly north of downtown, and south of OSU is the Old Towne East area, where I happened to be staying, and which features a lot of dilapidated buildings and not many businesses.

The 1.5-mile map shows more or less concentric circles radiating out from the central city, but job connectivity in that center can be seen as the sum of the job connectivity of the three less-central areas, where people might be walking to work.

Intersection density

OSMnx makes it easy to generate statistics on street networks by city extent, or by polygon shape. Here are the stats for the study cities, clipping the bike network (including bike paths, excluding freeways) to the 5-mile circle:

Austin Charlotte Columbus Minneapolis
Nodes (intersections) 10470 5417 12208 17892
Streets per node (avg) 2.98 2.82 3.16 3.13
Segment length (ft., avg) 323 394 321 299
Street segments, total 17733 9291 20976 30909
Bicycle mode share 3.6% 0.7% 2.3% 4.7%

This covers the entire circle, not just the census tracts, so the area is identical for the four cities. Once again these aggregated stats show very strong correlations, especially between bike mode share and intersection density (r=0.91). The fact that there are three times as many intersections in Minneapolis’ central city than in Charlotte’s is a somewhat remarkable finding, though it’s consistent with the experience of riding around those cities. Higher intersection density means that there are more choices for people getting around the city, which in turn means that the arterials have less traffic on average, and there are more alternate routes for bikes.

There’s an analogy to the geophysical concept of drainage density. A river network with fine drainage density (relatively small space between river channels) will drain very quickly during storm events because there are so many different ways for the water to reach a river channel. If you think about the networks below as representing the morning commute, the “coarse” network will require much larger, higher-speed roads than the “fine” network. And it’ll suck for bicycles.

Image depicting river systems with three different drainage densities (fine, medium, and coarse)

One of the problems of cycling advocacy is that there’s often not much that can be done to change this indigenous condition. Charlotte is already built out with high-volume streets and long blocks, and at this point you can’t make its street network look like Minneapolis’ (or Copenhagen’s).

Density = destiny?

One striking result from the target area analysis is the correlation between residential density and cycling rates in the target area.

Austin Charlotte Columbus Minneapolis
Target area (mi^2) 4.86 4.11 5.11 5.25
Population (persons) 147676 71312 129290 214592
Cyclists (work commute) 5380 464 3006 10040
Density (persons/mi^2) 30396 17342 25311 40850
Bicycle mode share 3.6% 0.7% 2.3% 4.7%
Source: ACS 2015 5-year estimates

Note that the square mileage in the above table is the square mileage of the census tracts fully contained within the target circle. Obviously the circles themselves have the same area, but because of differences in tract shapes, and non-land areas (lakes and rivers) within the circles, the census tract areas differ. This table reflects only the census tract areas, because it’s using census data.

For these four data points, the correlation between density and bicycle mode share is dramatic (r=0.97), which seems to speak to the importance of indigenous conditions in people’s mode choices. And there’s probably a real effect there. Unfortunately, the effect disappears when examined at the census tract level, becoming slightly negative in all four cities. My hypothesis is that there are threshold effects, where the most dense census tracts actually have less cycling because their walk mode share is higher. I didn’t have time to dig into that question, but it would be consistent with some of the work done by urban design superstar Jan Gehl.

Two things I would like to do are to automate the analysis to the point where I could examine this same question across many more cities, and, to combine the city data to see if the correlation flips back to the positive side when looking at census tracts across the four cities. I expect that both of those methods will show a strong positive correlation between density and bicycle mode share, but not near r=0.97.

Maps of network connectivity to jobs

One of the factors I’m trying to measure in valuing facilities is their usefulness; do they actually go where utility cyclists want to ride? I was unable to get BikeScore data (which looks pretty questionable anyway), so I had to build my own metric around it. One of the factors I used was access to jobs along the cycling network (with cycling network data obtained from OpenStreetMaps via Geoff Boeing’s awesome OSMnx tool). These maps show access to jobs within 1.5 miles from each intersection in the city; cycling routes through and near the dense areas here were scored higher.

One of the interesting things about these maps is how they visualize the density of the street network. The difference between Minneapolis’ densely connected street grid, and Charlotte’s broken-up and sparse network visualize really substantial differences in the indigenous conditions for cycling in the two cities.

These maps are using the same extents as the target area maps (the circle extenst rather than the census tract extents).

Maps of commute biking and value-added facilities

Combining my concepts of target area and value-added facilities, I developed maps for each city which select the circle of census tracts near the central city which have the great number of commute cyclists (by the 2015 ACS 5-year estimates). Each of these maps is equal area and uses the same scale.

One thing that’s visibly notable is the relatively weak connection of value-added facilities to cycling rates, by census tract; only in Columbus are they well-connected. Part of what I conclude is that cultural factors and the indigenous qualities of the street network are more important than value-added facilities in cycling mode choice.

One of my summer projects is to try to re-generate these in Python. I was trying to do all the GIS work in Python, but when it came to final output, I ran out of time and wound up having to export shapefiles and do the maps manually in QGIS. That’s why they’re not perfectly identical. Once it’s working in Python, I should be able to auto-generate the circles and the census tract data for any city in the U.S.

The methodology is potentially interesting for other kinds of analysis; I’m using bike mode share data, but you could just as easily optimize for any other census data, like median income, non-white population, educational attainment, etc. It could be a useful way to make urban areas more comparable for data analysis.

Target area data analysis

During the fall semester I took two relevant classes, one an introduction to GIS, and one on Active Transportation (with Professor Daniel Rodríguez, formerly of UNC-Chapel Hill, who will act as my thesis advisor). For my final project in the Active Transportation class, I used GIS tools to analyze bike mode share, bikeway mileage, and crash data for the four cities.

One problem I noticed while doing field research is that the city extents vary greatly; Austin, for example, has over six times Minneapolis’ land area, which means that Austin includes some sprawling, low-density areas with low cycling rates that are excluded from Minneapolis’ mode share numbers. Nathan Wilkes (from Austin’s Active Transportation Department) had done some work comparing mode share in the downtown area of Austin to San Francisco, to point out that the city has actually made more progress than would be evident from looking at the city-wide data.

Map of Austin bike mode share by census tract, showing much higher rates in the central city

Bike mode share by census tracts in Austin city extent (1:250,000)

Map of Minneapolis bike mode share by census tract, showing much higher rates in the central city

Bike mode share by census tracts in Minneapolis city extent (1:250,000)

I looked at creating a 4-mile radius circle to encompass the highest-cycling area of each city (by ACS mode share data), to be able to develop more comparable metrics from city to city. I think the method will prove very useful in the data analysis portion of the work, though I’ll make some tweaks to it in the final version. For this work I selected all census tracts which intersected the circle, but the resulting extents are still quite different in land area (by almost a factor of two). For the paper I’ll probably use a 5-mile radius and select only the census tracts which are completely included in the circle. I’ll also include census tracts outside of the central city if the circle includes them (which might affect Minneapolis, but not any of the other cities).

Bike mode share, facilities, and crashes within 4-mile radius circle in central Austin (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Austin (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Minneapolis (1:100,000)

Bike mode share, facilities, and crashes within 4-mile radius circle in central Minneapolis (1:100,000)

There are still a lot of issues with the data, but at least I’ll be comparing rotten apples to rotten apples.

Here’s the full paper if you’re interested.

Roads vs. streets

Urbanists like to distinguish streets from roads. There’s an interesting take on this from Charles Marohn, who’s a city planner from Minnesota, on his Strong Towns blog:

The function of a road is to connect productive places…In contrast, the function of a street is to serve as a platform for building wealth.

This is a useful distinction, but what makes it interesting is the framing; this is the neo-liberal, New Urbanist ideology laid bare. The purpose of a street (and by implication, the purpose of the city) in this view is to maximize economic return on investment. See also Marohn’s criticism of the “stroads” which constitute much of the U.S. infrastructure:

This economic view of the world is a self-consistent belief system, and it at least has a specific strategic goal. But, it also explains why these New Urbanist developments are designed for the affluent. They focus on increasing home values and developing retail businesses. Those who don’t own homes and can’t afford to eat out all the time are not included in the vision, and even less so the truly poor.

But, that’s not what this post is about. I was thinking as I was riding around that bikes need a distinction between streets and roads, too. Here are bike roads in Saint Paul and Columbus, both coincidentally named “Summit”:

Summit Avenue, St. Paul

Summit Avenue, St. Paul

Summit Street, Columbus

Summit Street, Columbus

Both Summits are long, straight roads with limited traffic interactions. Columbus’ Summit recently underwent a road diet, replacing one of three one-way traffic lanes with a two-way protected bikeway. Saint Paul’s has a decent bike lane and relatively little traffic. The thing that makes them bike roads rather than bike streets is that they don’t present a lot of opportunity to interact with the city.

In Saint Paul, Summit goes through a neighborhood of Victorian mansions, mostly set back far from the street, with fences and gates. The wide center median could be an inviting place for walking or picnicking, but the road goes for miles without so much as a bench in the median. The road is useful but you have to use it and move on. In Columbus, Summit has a lot of traffic, but the protected lane makes it reasonably safe, and the fact that it’s two-way makes it a pretty nice addition to the cycle network. But similarly, there are not many places where the rider is invited to interact with the street.

That’s fine; utility cyclists need ways to travel distances efficiently within the city as well as places that are cool to bike around. A good bike network will include both bike roads and bike streets. Summit in Saint Paul goes all the way from downtown to the East Mississippi bikeway, over 4 miles; Summit in Columbus goes 3 miles from downtown, paralleling the hip district on High Street, and connecting with Definite Article Ohio State University.

Scioto River Valley

Columbus is the host of the Tour of the Scioto River Valley (TOSRV), one of America’s longest-standing large group rides. TOSRV makes the claim, with some credibility, that its popularity led to the original U.S. bike boom back in the 1970s. The two-day, 210-mile event peaked at over 6,000 riders in the 80s, and helped establish the idea of the organized century ride as the foundation of recreational cycling in America. TOSRV, like most century rides, is not oriented towards utility cycling, though it helped inspire BikeCentennial in 1976, which led to the growth of bike touring culture in the U.S. Bike touring is closer to utility cycling than lycra rides are; Surly’s Long Haul Trucker is currently the best mass-produced bike for both touring and urban riding.

So, social riding is part of the culture around Columbus, and I was able to hook up with the regular Tuesday night social ride. I’d had a snafu with getting a loaner, so I rolled up on a klunky bike share bike, which earned me a few askance looks. But people warmed up once I introduced myself, and the leader of the ride, Ray George, is one of the founders of Yay Bikes!, and is still on the board,

Tuesday Night Ride

Tuesday Night Ride

One of the big things Yay Bikes! runs is social ride, “Bike the CBus“, which is a self-guided tour through urban Columbus neighborhoods. Definitely more oriented towards utility cycling than most event rides, Ray credits Bike the CBus with inspiring several cyclists to buy houses along the route in bikeable neighborhoods which they may not have known about. The route changes every year to highlight different areas of the city.

Ray gives the goals of Yay Bikes! as “safety and place-making.” Safety is a common goal among bike advocacy organizations, but place-making as an explicit goal is a little unusual. Certainly projects like North Minneapolis and Little Sugar Creek have place-making components, but the dialog tends to be more about safety and auto trip reduction.

The ride was mostly along the Scioto River. We got dinner at a taco truck, did a coasting race down a Soapbox Derby hill, released some candle balloons (there’s something you wouldn’t see in California), and then wrapped up at a hipster beer garden on High Street.

Subaltern cyclists

Motorists. In case you weren't sure who the roads in Columbus are for.

Motorists. In case you weren’t sure who the roads in Columbus are for.

Heading towards downtown Columbus from Old Towne East, you see a tall building with a sign that reads “Motorists.” It’s visible from lots of places in town, and while it doesn’t have any real significance, it seems to emphasize the transportation hierarchy here.

Before arriving in Columbus I’d reached out to Yay Bikes!, the city’s most prominent bike advocacy group, to ask to meet to chat about their work. Their director called me back and asked if I was trying to hire them, which I thought was an odd question. When I explained that I was just a student doing research, she told me that she and her staff wouldn’t have time to talk with me.

I was a bit flummoxed. I’ve never met a bike advocate who wouldn’t go on at length about bike advocacy at the slightest provocation. (And I’ll cop to that myself). It felt like something strange was going on with bike advocacy in Columbus.

Digging a little deeper, there apparently was some sort of conflict among bike groups in Columbus in the past. In addition to Yay Bikes!, there was a group named Consider Biking, and the two groups fell to in-fighting. Over what, I’m not sure, but it looks like it got nasty. Among the bits of detritus I’ve been able to find, there’s a message from 2009 on the Bike PGH (Pittsburgh) message board from someone with Yay Bikes! accusing Consider Biking of plagiarizing the “About” page of Bike PGH’s web site. Which seems quite petty. There was an article about the conflict in The Other Paper, a now-defunct Columbus alt-weekly, that I haven’t yet been able to find the full text of. While in town I asked a few people about the story, and no one really wanted to get into the details.

That led me to thinking about conflict between bike groups. In Charlotte I also felt some tension between the Charlotte Spokes People and Sustain Charlotte, which seemed to align along the classic bike advocacy conflict of vehicular cycling vs. infrastructure advocacy. Those kinds of disputes don’t seem to happen anymore in the Bay Area, and I didn’t notice them in Minneapolis or Austin, either.

One thing we know about subaltern groups is that there is a tendency towards infighting and competition amongst themselves. Instead of banding together to fight for their interests, they can fall into disputes over goals and methods–especially, whether to work within the system or to disrupt it. I’m going to look at some of the research on that question and see how it might apply to bike advocacy groups in cities where biking is marginal. And I’ll also look at what happens when a subaltern group becomes part of the hegemony, which has occurred in San Francisco and Portland.

Indigenous bikeways

As long as I’m talking about terminology, I’m thinking of using the term indigenous instead of natural to describe the existing infrastructure of a city prior to the construction of any bike-specific facilities. The OED defines indigenous as “Originating or occurring naturally in a particular place,” which I think captures the idea I want to get across. Indigenous bikeways aren’t entirely natural, but they exist (or don’t) based on decisions that were made decades or centuries ago.

The term also has a slightly unsettling connection to colonialism which I actually think is good, because I think urbanism often has a slightly unsettling connection to colonialism, or more specifically Orientalism.

And I happened to be visiting a city named for America’s favorite colonialist.

Typical street in Old Town East

Typical street in Old Towne East

It didn’t take long to note the difference in indigenous bikeways in Columbus vs. Minneapolis. I haven’t yet delved into the research on how road design affects driver behavior, but it’s quickly evident that a one-way road without trees generates traffic that’s faster and more oblivious than two-way tree-lined roads. I was staying in Old Towne East, and unlike my Minneapolis neighborhood, most of the streets were barren of trees, and several of the roads were essentially one-way, one lane thoroughfares. Some of these had bike lanes, but at best you would categorize them as useful rather than pleasant facilities.

Columbus has more topographic challenges than Minneapolis. The downtown is at the confluence of the Scioto and Olentangy Rivers, both of which run north-south, and Alum Creek to the east also runs north-south. That topography led to the city’s infrastructure being built mostly along narrow north-south corridors, and today the rail lines, freeways, and arterials really disrupt east-west access.

This isn’t a surprise. Old Towne East has been a low-income neighborhood for decades, like most of the other areas to the east of downtown. The siting of the freeways in U.S. cities was implicitly or explicitly intended to cut off the downtown from areas which were perceived as blighted. It turned out to be not only a moral error but an economic one, as splintering the city worsened the situation. That error is one of the enduring legacies of urban planning, and part of why urban planners need to double and triple-check their own assumptions when making proposals.

Bikeway taxonomy

There have been a number of different attempts to categorize bikeways based on different criteria. The most commonly used taxonomy in the U.S. divides bikeways into Class I (separated bike path), Class II (striped bike lane), and Class III (marked route on roads). Part of the impetus for my rolling surveys is that this classification is really not useful in considering the experience of bikeway users. Some Class I facilities are scary and dangerous, and some Class III facilities (like The Wiggle in San Francisco) provide great experiences. You really have to ride the facilities to determine if they’re any good.

Portland (of course) uses a typography based on the type of cyclists who use the facility. They break potential cyclists into four groups:

  1. “No way, no how”: No matter what you do, they won’t ride.
  2. Interested but concerned“: They might ride but are timid and want better facilities
  3. Enthused and confident“: They ride a lot but prefer their own facilities
  4. Strong and fearless“: They ride all the time and don’t mind sharing the road with cars

I have a number of issues with this taxonomy, but the main issue is that it equates cyclist skill with cyclist commitment. I know plenty of enthusiastic riders who really aren’t very good or safe cyclists. I know people who ride every day who are incredibly timid. In addition to the false dichotomies inherent in hierarchy, there are false equivalences within each tier. And it also ties the cyclist to the facility, which I think is too simplistic.

Bike map excerpt near downtown Columbus

Bike map excerpt near downtown Columbus

Columbus doesn’t directly adopt Portland’s model, but they have a bike map which indicate routes by “level of comfort”: Good, Moderate, Poor, Residential. I have linguistic issues with the terminology, and also don’t like that it ties facilities to cyclist skill. “Poor”, for example, is described as “Road suitable for bicyclists with advanced skills. Extreme caution should be used.” (It would be great if drivers exercised extreme caution, but I think they’re talking about the cyclists).

There’s another problem with this map: it looks super-scary. I was staying in Old Town East, on the east side of this map, and with the exception of the river trail, there seemed to be no way to get around without being on yellow or red roads. The bike network in Columbus isn’t great, but it isn’t that bad. And is it necessary to highlight poor roads? It doesn’t take more than a few seconds for cyclists to realize they’re on a dangerous road.

So while riding around I was thinking about a way to categorize facilities that’s really focused on the facilities. I came up with these terms:

  1. Pleasant: A quiet facility with few traffic interactions. Often tree-lined or attractive in other ways. It’s possible to carry on a conversation with a companion.
  2. Useful: A reasonably safe but not particularly pleasant facility which connects interesting places.
  3. Necessary: A facility which is unpleasant but required to get from one interesting place to another.

The Midtown Greenway is a pleasant facility. So is the series of roads parallel to Little Sugar Creek, and the residential road grid in Minneapolis. South Congress in Austin is useful (and used) but certainly not pleasant. And Park Road in Charlotte is a necessary section of busy six-lane road which connects two better facilities.

What I like about this taxonomy is that it’s focused solely on the facilities, not on the cyclists using them. Expert-level cyclists enjoy pleasant facilities, too. It also provides a way to distinguish between the two goals of bike facilities: increasing safety and reducing auto trips. Some bike facilities are designed to turn necessary routes into useful routes. These are likely to increase safety but unlikely to reduce auto trips. Others are designed to turn useful routes into pleasant routes. These might not affect safety but they might move the needle on reducing auto trips. It may be helpful to distinguish between these goals and the projects which support them.


Columbus as “Smart City”

I’ll take a pause from Austin for a moment to look ahead to one of my July destinations: Columbus, Ohio. My Facebook feed is atwitter with urbanist friends congratulating Columbus for winning the USDOT’s Smart City Challenge, getting $50M in federal funds for so-called “smart” transportation.

My take: No thanks..

Take a look at Columbus’ pitch video. What is it missing?

Did you notice? There’s not a single bicyclist or pedestrian. That’s because the USDOT program is based on old, broken ideas about what the street network is for. Ideas like platooning single-occupant cars to increase road capacity.

You know the best way to increase road capacity? Get people out of cars. Here’s an image that demonstrates the problem:

Canberra, Australia demonstration of road capacity

There’s no way technology can address the fact that cars take a lot more space in the city than bikes, pedestrians, or transit. Using platooning and signal timing to increase the single-occupant-vehicle capacity of freeways or arterial roads will make those facilities even more human-hostile and city-hostile.

Human-scale design is the only way to build good cities.