Category: Transit

Developing a method to score Divvy station connectivity

January 4, 2014 / / 2 Comments

A Divvy station at Halsted/Roscoe in Boystown, covered in snow after the system was shutdown for the first time to protect workers and members. Photo by Adam Herstein.

In researching for a new Streetsblog Chicago article I’m writing about Divvy, Chicago’s bike-share system, I wanted to know which stations (really, neighborhoods) had the best connectivity. They are nodes in a network and the bike-share network’s quality is based on how well (a measure of time) and how many ways one can move from node to node.

I read Institute for Transportation Development Policy’s (ITDP) report “The Bike-Share Planning Guide” [PDF] says that one station every 300 meters (984 feet) “should be the basis to ensure mostly uniform coverage”. They also say there should be 10 to 16 stations per square kilometer of the coverage area, which has a more qualitative definition. It’s really up to the system designer, but the report says “the coverage area must be large enough to contain a significant set of users’ origins and destinations”. If you make it too small it won’t meaningfully connect places and “the system will have a lower chance of success because its convenience will be compromised”. (I was inspired to research this after reading coverage of the report in Next City by Nancy Scola.)

Since I don’t yet know the coverage area – I lack the city’s planning guide and geodata – I’ll use two datasets to see if Chicago meets the 300 meters/984 feet standard.

Dataset 1

The first dataset I created was a distance matrix in QGIS that measured the straight-line distance between each station and its eight nearest stations. This means I would cover a station in all directions, N, S, E, W, and NW, NE, SE, and SW. Download first dataset, distance matrix.

Each dataset offers multiple ways to gauge connectivity. The first dataset, using a straight-line distance method, gives me mean, standard deviation, maximum value, and minimum value. I sorted the dataset by mean. A station with the lowest mean has the greatest number of nearby stations; in other words, most of its nearby stations are closer to it than the next station in the list.

Sorting the first dataset by lowest mean gives these top five best-connected stations:

  1. Canal St & Monroe St, a block north of Union Station (191), mean of 903.96 feet among nearest 8 stations
  2. Clinton St & Madison St, outside Presidential Towers and across from Northwestern Train Station (77), 964.19 feet
  3. Canal St & Madison St, outside Northwestern Train Station (174), 972.40
  4. Canal St & Adams St, north side of Union Station’s Great Hall (192), 982.02
  5. State St & Randolph St, outside Walgreens and across from Block 37 (44), 1,04.19

The least-connected stations are:

  1. Prairie Ave & Garfield Blvd (204), where the nearest station is 4,521 feet away (straight-line distance), or 8.8x greater than the best-connected station, and the mean of the nearest 8 stations is 6,366.82 feet (straight-line distance)
  2. California Ave & 21st St (348), 6,255.32
  3. Kedzie Ave & Milwaukee Ave (260), 5,575.30
  4. Ellis Ave & 58th St (328), 5,198.72
  5. Shore Drive & 55th St (247), 5,168.26

Dataset 2

The second dataset I manipulated is based on Alex Soble’s DivvyBrags Chrome extension that uses a distance matrix created by Nick Bennett (here’s the file) that estimates the bicycle route distance between each station and every other station. This means 88,341 rows! Download second dataset, distance by bike – I loaded it into MySQL to use its maths function, but you could probably use python or R.

The two datasets had some overlap (in bold), but only when finding the stations with the lowest connectivity. In the second dataset, using the estimated bicycle route distance, ranking by the number of stations within 2.5 miles, or the distance one can bike in 30 minutes (the fee-free period) at 12 MPH average, the following are the top five best-connected stations:

  1. Ogden Ave & Chicago Ave, 133 stations within 2.5 miles
  2. Green St & Milwaukee Ave, 131
  3. Desplaines St & Kinzie St, 129
  4. (tied) Larrabee St & Kingsbury St and Carpenter St & Huron St, 128
  5. (tied) Clinton St & Lake St and Green St & Randolph St, 125

Notice that none of these stations overlap with the best-connected stations and none are downtown. And the least-connected stations (these stations have the fewest nearby stations) are:

  1. Shore Drive & 55th St, 11 stations within 2.5 miles
  2. (tied) Ellis Ave & 58th St and Lake Park Ave & 56th St, 12
  3. (tied) Kimbark Ave & 53rd St and Blackstone Ave & Hyde Park Blvd and Woodlawn Ave & 55th St, 13
  4. Prairie Ave & Garfield Blvd, 14
  5. Cottage Grove Ave & 51st St, 15

This, the second dataset, gives you a lot more options on devising a complex or weighted scoring system. For example, you could weight certain factors slightly higher than the number of stations accessible within 2.5 miles. Or you could multiply or divide some factors to obtain a different score.

I tried another method on the second dataset – ranking by average instead of nearby station quantity – and came up with a completely different “highest connectivity” list. Stations that appeared in the least-connected stations list showed up as having the lowest average distance from that station to every other station that was 2.5 miles or closer. Here’s that list:

  1. Kimbark Ave & 53rd St – 13 stations within 2.5 miles, 1,961.46 meters average distance to those 13 stations
    Blackstone Ave & Hyde Park Blvd – 13 stations, 2,009.31 meters average
    Woodlawn Ave & 55th St – 13 stations, 2,027.54 meters average
  2. Cottage Grove Ave & 51st St – 15 stations, 2,087.73 meters average
  3. State St & Kinzie St – 101 stations, 2,181.64 meters average
  4. Clark St & Randolph St – 111 stations, 2,195.10 meters average
  5. State St & Wacker Dr – 97 stations, 2,207.10 meters average

Back to 300 meters

The original question was to see if there’s a Divvy station every 300 meters (or 500 meters in outlying areas and areas of lower demand). Nope. Only 34 of 300 stations, 11.3%, have a nearby station no more than 300 meters away. 183 stations have a nearby station no further than 500 meters – 61.0%. (You can duplicate these findings by looking at the second dataset.)

Concluding thoughts

ITDP’s bike-share planning guide says that “residential population density is often used as a proxy to identify those places where there will be greater demand”. Job density and the cluster of amenities should also be used, but for the purposes of my analysis, residential density is an easy datum to grab.

It appears that stations in Woodlawn, Washington Park, and Hyde Park west of the Metra Electric line fare the worst in station connectivity. The 60637 ZIP code (representing those neighborhoods) contains half of the least-connected stations and has a residential density of 10,468.9 people per square mile while 60642, containing 3 of the 7 best-connected stations, has a residential density of 11,025.3 people per square mile. There’s a small difference in density but an enormous difference in station connectivity.

However, I haven’t looked at the number of stations per square mile (again, I don’t know the originally planned coverage area), nor the rise or drop in residential density in adjacent ZIP codes.

There are myriad other factors to consider, as well, including – according to ITDP’s report – current bike mode share, transit and bikeway networks, and major attractions. It recommends using these to create a “demand profile”.

Station density is important for user convenience, “to ensure users can bike and park anywhere” in the coverage area, and to increase market penetration (the number of people who will use the bike-share system). When Divvy and the Chicago Department of Transportation add 175 stations this year – some for infill and others to expand the coverage area – they should explore the areas around and between the stations that were ranked with the lowest connectivity to decrease the average distance to its nearby stations and to increase the number of stations within 2.5 miles (the 12 MPH average, 30-minute riding distance).

N.B. I was going to make a map, but I didn’t feel like spending more time combining the datasets (I needed to get the geographic data from one dataset to the other in order to create a symbolized map). 

Divvy isn’t a real bike

January 2, 2014 / / 0 Comments

Riding on Divvy in the snow.

Divvy, for the first time in its short, seven-month existence, shut down today at 12 PM on account of the weather and keeping members and workers – who move bikes, shovel snow, and drive vans around town – safe.

Every news media in town reported on the shutdown. Chicagoist, ABC 7, NBC 5, FOX 32, Chicago Tribune, Sun-Times – you name it they had it.

But nothing has been published, except parts of this story from DNAinfo Chicago, that discusses how bicycling – whether on Divvy or your own bike – is very difficult in Chicago winters because of the poor coordination between the Streets & Sanitation and Transportation departments’ snow removal efforts, and the slow pace at which CDOT gets around to removing snow from the protected bike lanes. (I was quoted, alongside someone I recommended the author get in touch with, and we have differing views on the matter.)

In winter the protected bike lanes are the only bikeable kind of bike lane as conventional bike lanes become snow storage areas because plows can’t reach further right when there are parked cars (to avoid knocking off car mirrors).

This problem is not unique to Chicago and other cities have solved it. The cold isn’t why people in Chicago stop biking: it’s that snow and ice make it even more difficult in a region with little, separated (meaning safe and desirable) cycling infrastructure. There are climes with similar and worse winters where a large portion of people who bike in the summer keep biking in the winter. Places like Boulder, Minneapolis, Montréal, and Copenhagen.

A well-plowed, separated bike lane in a Copenhagen winter. Stranded? Put your bike on the back of a taxi (their buses don’t allow bikes).

I think it’s good that news media have recognized Divvy’s position as a transit system in the area, which they do by holding it to the same, weird standard they do Chicago Transit Authority and Metra, and posting about it frequently. When the CTA or Divvy has some marginal or perceived issue with its finances or service, an article gets written. But when it comes to bicycle infrastructure they give the city a pass where it doesn’t deserve one.

The media cares about Divvy, but it doesn’t care about bicycling. It might be the 11,000 Divvy members (more than Active Transportation Alliance or The Chainlink), however, that gets the city to kick up its bike lane snow removal efforts up a notch and I anxiously await that day.

CTA fare breakdown for Ventra and fares it replaces

December 13, 2013 / / 8 Comments

This CTA graphic shows all the fare media Ventra replaces. 

The Chicago Transit Authority expanded its pilot contactless card fare payment technology systemwide in 2002, and introduced Chicago Card Plus, which added the benefit of linking to a credit/debit card, in 2004. After 11 years, the two cards were hardly “popular” as Jon Hilkevitch called them today. In the context of his article I believe he meant “liked” or “admired” and not widespread, as Ventra does not have the same admiration because of all of the issues people are experiencing.

While Chicago Card/Plus users likely preferred this fare payment over magnetic stripe, for their convenience and speed, a minority of passengers used it.

Data from CTA for January to July 2013, representing 1.6 million average weekday rides.

Magnetic Stripe: 75%
CCP & CC: 19% (17% & 2% respectively)
Bus Cash: 6%

Ventra? 69% this week.

Something new in Salt Lake City transit

November 30, 2013 / / 19 Comments

This is the fourth year in a row I’ve visited my mom in Salt Lake City and there’s a new transit line to gawk at. Three years ago it was the FrontRunner North commuter line between SLC and Ogden. Two years ago it was two new light rail lines (with new Siemens S70 vehicles). Last year was FrontRunner South to Provo (where my brother lives), and this year it’s the S-Line streetcar line.

On Wednesday, on my way back to SLC from Provo, I took a bus from my brother’s office to the Provo FrontRunner station, then the FrontRunner train to Murray station, where I switched to TRAX to ride up to Central Pointe station where the streetcar line terminates. A test vehicle was stopped at the single-track platform.

I wanted to see the route, the stops it makes, the station design, and the adjacent biking and walking path so I started walking up and down and across the blocks to check it out. I ran into two train several times while UTA staff tested them and made the video above.

Stop locking your bike at the Clybourn Metra station overnight

November 11, 2013 / / 6 Comments

Existing bike parking at the Clybourn Metra station

This is a resolution.

WHEREAS, I love GIS.

WHEREAS, I was reading this blog post on the Azavea company blog about bike theft prediction and trends in Philadelphia.

WHEREAS, I analyzed bike theft location in Chicago in 2012 and the Clybourn Metra station emerged as the most frequent Metra theft location.

WHEREAS, I searched the Chicago Stolen Bike Registry for “clybourn” and several thefts have been reported to the registry in 2013.

WHEREAS, I believe the Chicago Police Department still doesn’t allow searching of their database for bike thefts thus leaving the CSBR as the premier source of data.

WHEREAS, I am watching this show called The Bletchley Circle wherein a group of four fictional women who cracked codes in World War II are solving a murder mystery in 1950s London.

BE IT RESOLVED that you should not leave your bicycle parked at the Clybourn Metra station overnight as it is a terrible place to leave a bicycle parked. Why? No one is around most of the time to socially secure your bicycle.

New bike parking at the Clybourn Metra station

This is a great place to get your bike stolen. In the dark. Overnight. With no one around to see it happen.