Tagcrash data

Which is safer: Bike without helmet, or drive without seatbelt?

Cycling on Milwaukee Avenue at Grand Avenue and Halsted Street, one of the most crash-likely intersections on Milwaukee Avenue.

Someone asked me on Twitter: “What’s more dangerous, biking with no helmet or driving with no seatbelt?” It’s an odd comparison, but I decided to try to crack the question.

Here’s my answer:

If your definition of “dangerous” is “the likelihood that you’ll receive an injury while traveling in/on the vehicle”, assuming that the likelihood of being in a crash is the same*, then you are more likely to sustain an injury while cycling while wearing a helmet than while driving or being a passenger in a car while wearing a seatbelt.

Here’s the data, for crashes in Chicago in 2007-2010:

Table 1: Yes, recorded to be wearing a helmet while bicycling

Injury Type Frequency (each number is a person) Percent of total
No injury 3 7.32%
Possible injury 6 14.63
Non-incapacitating injury 26 63.41
Incapacitating injury 6 14.36
Fatality* 0 0
Total 41 100%

A value of 0 fatalities in four years for people wearing a helmet absolutely DOES NOT mean that a helmet prevented a fatality. The “contrary” data for “Recorded to not be wearing a helmet or having safety equipment” shows that there was 1 fatality in four years – the data do not suggest that the fatality would be prevented if the person was wearing a helmet. The sample size is so small that this data is meaningless.

Table 2: Yes, recorded to be wearing a seatbelt as driver or passenger

Injury Type Frequency (each number is a person) Percent of total
No injury 423,096 89.42%
Possible injury 21,667 4.58
Non-incapacitating injury 23,956 5.06
Incapacitating injury 4,338 0.92
Fatality 93 0.02
Total 473,150 100%

*I don’t think we can determine the likelihood of being in a crash when riding a bicycle because we don’t know the “device miles traveled” of Chicago cyclists. It’s probably possible to approximate the number of vehicle miles traveled by drivers in Chicago, though; I’m not sure about passengers.

Download the data for this article, which includes these additional tables:

  • Bicycling: All injuries
  • Bicycling: No safety equipment or helmet wearing
  • Bicycling: Unknown usage of safety equipment
  • Auto: All injuries
  • Auto: No safety equipment or helmet wearing
  • Auto: Unknown usage of safety equipment

Rambling about automobile crash data and cellphone distraction

How often do bicyclists get involved with crashes because of cellphone distraction? See the table below. And how many crashes are caused by the bicyclist being distracted by a cellphone? We won’t and don’t know. 

The Chicago City Council will vote tomorrow on ordinance 02011-7146 to add a new section in Chapter 9 of the Municipal Code of Chicago: “9-52-110 Use of communication devices while operating a bicycle.”

In a Chicago Sun-Times article today, Matthew Tobias, the Chicago Police Department’s deputy chief of Area 3 patrol, reported on the number of citations that the department has issued to drivers in violation of the cellphone ban: “from 2,577 administrative violations in 2008 to 10,920 in 2009 and 19,701 last year” (known as “citations issued” in the table below).

I looked at the crash data to see how many crashes were coded as having been caused by “Distraction – operating an electronic communication device (cell phone, texting, etc)”.

Out of 274,488 recorded crashes in 2008, 2009, and 2010, there were 331 crashes which had a Cause 1 or Cause 2 of “Distraction – operating an electronic communication device (cell phone, texting, etc)”. The table below compares the rates of crashes to the rates of citations issued and the number of crashes that the police noted were caused by cellphone distraction. It also shows the number of these “cellphone distraction” crashes that involved bicyclists and pedestrians.

Year Citations issued Automobile crashes Cellphone distraction crashes % of cellphone distraction crashes Involved with bicyclists? Involved with pedestrians? National VMT (billions)*
2008 2577 111,701 91 0.081 3 10 2973.47
2009 10920 81,982 130 0.159 1 7 2979.39
2010 19701 80,805 110 0.136 6 8 2999.97

Maybe this data shows that the increased enforcement is causing fewer crashes?
However data for cyclists’ involvement in crashes and their cellphone use WON’T BE recorded unless there’s a rule change as the cause is only recorded for the vehicle involved in the crash, and bicycles are devices, not vehicles.

None involved fatalities.

*Yep, that’s 2 thousand billion. Read it like this, 2 trillion 973 billion and 470 million. VMT data from Bureau of Transportation Statistics.

Are protected bike lanes going in the right places?

Bike crash map of Ogden, Milwaukee, Chicago

Common bike-car crash locations in West Town. The bottom blue circle identifies Ogden/Milwaukee, where there is a yellow trap for northbound, left-turning motorists (from Milwaukee to Ogden) that makes them run into southbound bicyclists who have a green light.

My contribution to a discussion on The Chainlink, Are protected bike lanes going in the right places?

Kelvin, Milwaukee/Ogden/Chicago is the intersection along Milwaukee Avenue with the highest number of bicycle crashes. I created this table and map to show them, using data from 2007-2009.

The blue rings on the map are called, in GIS parlance, “buffers” and are circles used to select things (in this case, bike crashes) within a certain distance of the circle center. In this map I used 50 feet radius buffers (100 feet diameter). While this distance encompasses the intersection from center to all four curbs, it doesn’t encompass the crashes that happened just outside the buffer that were still most likely influenced by the intersection (like drivers’ turning movements).

I am working on a project with three friends to create a better map and “crash browser”. I mentioned it in the last story on Grid Chicago in this post. For this project, we are using 200 feet radius (400 feet diameter) buffers to ensure we encompass the entire intersection and the area in which it still has an effect. This also grabs the bike lane “pinch points”, places where a bike lane doesn’t start until 100-200 feet beyond the intersection.

I am also concerned with the strategy and approach CDOT is using to choose locations. It’s not transparent; at MBAC, CDOT said they were choosing locations “without controversy and that could be implemented quickly”.

Read more about Kinzie Street, Chicago’s first protected bike lane, and my other thoughts on protected bike lanes

Just how many taxi vs. bicycle crashes are there? A Google Refine story

On this Chicagoist story about how IDOT will now collect data on doorings (instead of ignoring that crash type as they preferred), I opened the story photo entitled “Cabbie takes down another” by Moe Martinez. His photo caption reads, “you see it alot … thankfully this guy seemed to be relatively ok … coherent and what not.”

I wanted to know just how often “we” see taxi drivers crashing with people riding bicycles. You can’t filter by vehicle type in either mine or Derek’s bike crash maps, but you can via the Fusion Table.

I decided to get the answer via Google Refine and make a screencast to show you just how quick and powerful a tool it is.

It’s dead simple:

  1. Load a CSV of the data into Google Refine.
  2. Click on the VEH1_SPECL column’s down arrow, then Facet>Text Facet.
  3. In the facet box, sorted alphabetically, find “TAXI/FORE HIRE.”
  4. The number of rows that apply is listed: 353.
  5. Divide 353 by the total number of rows, 4931, multiply by 100, and you get your percentage.

Taxi drivers are involved in just 7.2% of bicycle crashes in Chicago in 2007-2009.

The majority of crashes, at 66%, involve people driving “PERSONAL” vehicles. And 80% of those crashes are with a passenger vehicle that’s not a van, minivan, SUV, truck, or bus (so probably a sedan or coupe). Let’s look at more data.

How many taxis are there and how many personal vehicles are there? Are taxicabs involved in a disproportionately higher number of crashes?

About 781,023 people drive to work, either alone or with someone else, in Chicago (data from 2005-2009 5-year American Community Survey). 1,063,047 households have 1,218,594+ vehicles available in Chicago. Let’s assume the 7,000 taxicabs in Chicago are not counted as a “vehicle available.”* That’s 1,225,594 “personal” vehicles. If all were on the road at the same time, only 0.57% of them would be taxicabs. But they’re not on the road at the same time. So let’s take that number of people who drive to work and add 7,000 vehicles to it. So of those 788,023 “vehicles” now on the road, just 0.88% of them are taxicabs.

So it does seem that taxicabs are involved in a disproportionate number of crashes when compared to their presence on the streets. However, taxicabs are most likely driven more more miles and for more time than personal vehicles thus making their exposure to people bicycling greater than drivers of other vehicles. (A majority of “personal” trips are very short.)

New data coming soon

I can’t wait to get the 2010 crash data. Here’s why: In 2007, students in a taxi driver training course at Harold Washington College received some education about sharing the road with bicyclists:

A pilot “Share The Road” education module was launched at the taxi training school at Harold Washington College. It includes a 25-30 minute lecture, with discussion. After the pilot, the class will be required for all people training to drive taxis in Chicago. In the future, bicycle questions will be included on the exams required to become taxi drivers. June 2007 MBAC meeting minutes (PDF).

The number of crashes between taxi drivers and people riding bikes jumped from 2007 to 2008, but declined heavily between 2008 and 2009. More data will show us a clearer trend that may lend insight into the impact of the “Share The Road” education module.


The question (PDF) on the American Community Survey asks, “How many automobiles, vans, and trucks of one-ton capacity or less are kept at home for use by members of this household?” This may or may not include taxicabs stored at home.

I don’t know how many taxicabs there are in Chicago, but the Chicago Sun-Times reported there are approximately 7,000.

Initial intersection crash analysis for Milwaukee Avenue

Slightly upgraded Chicago Crash Browser

This screenshot from the Chicago Crash Browser map shows the location of bike-car collisions at Ogden/Milwaukee, an intersection that exemplifies the yellow trap problem the city hasn’t remedied.

List of the most crash-prone intersections on Milwaukee Avenue in Chicago. Using data from 2007-2009, when reported to the Chicago Police Department. Dooring data not included on the bike crash map. I used QGIS to draw a 50-feet buffer around the point where the intersection center lines meet.

Intersecting street (class 4*) Bike crashes
Chicago Avenue (see Ogden below) 12 (17)
California Avenue 9
Halsted Street & Grand Avenue 7
Damen Avenue & North Avenue 6
Western Avenue 6
Ogden Avenue (see Chicago above) 5 (17)
Ashland Avenue 5
Diversey Avenue 5
Fullerton Avenue 5
Elston Avenue 5
Augusta Boulevard (not class 4) 5

Combine the six-way (with center triangle) intersection of Ogden, Milwaukee, Chicago, and you see 17 crashes. Add the 6 just outside the 50-feet buffer and you get 23 crashes. Compare this to the six-way (without center triangle) at Halsted, Milwaukee, Grand, where there’s only 7 crashes.

What about the two intersections causes such a difference in crashes? Let’s look at some data:

Ogden, Milwaukee, Chicago Halsted, Milwaukee, Grand
Automobile traffic Approx 58,000 cars per day Approx 50,000 cars per day.
Bicycle traffic Not counted, but probably fewer than 3,100 bikes More than 3,100 bikes per day*
Bus traffic Two bus routes Three bus routes
Intersection style Island; three signal cycles No island; one signal cycle


Traffic counts are assumed estimates. Counts are taken on a single day, either Tuesday, Wednesday, or Thursday. Bike counts at Halsted/Milwaukee/Grand were actually taken on Milwaukee several hundred feet northwest of the intersection so DO NOT include people biking on Halsted or Grand! This means that more than 3,100 people are biking through the intersection each day.

Intersection style tells us which kind of six-way intersection it is. At island styles you’ll find a concrete traffic island separating the three streets. You’ll also find three signal cycles because there are actually three intersections instead of one, making it a 12-way intersection. Also at these intersections you’ll see confusing instructional signage like, “OBEY YOUR SIGNAL ONLY” and “ONCOMING TRAFFIC HAS LONGER GREEN.”

These intersections are more likely to have a “yellow trap” – Ogden/Milwaukee definitely has this problem. The yellow trap occurs at that intersections when northbound, left-turning motorists (from Milwaukee to Ogden) get a red light but they still need to vacate the intersection. Thinking that oncoming traffic has a red light but are just being jerks and blowing the red light (when in fact they still have a green for 5-10 more seconds) they turn and sometimes hit the southbound traffic. The City of Chicago acknowledged this problem, for bicyclists especially, in summer 2013 but as of November 2014 the issue remains.

Here’s a more lengthy description of one of the problems here as well as an extremely simple solution: install a left-turn arrow for northbound Milwaukee Avenue. The entire intersection is within Alderman Burnett’s Ward 27.

Source and method

I can’t yet tell you how I obtained this data or created the map. I’m still working out the specifics in my procedures log. It involved some manual work at the end because in the resulting table that counted the number of crashes per intersection, every intersection was repeated, but the street names were in opposite columns.

Crash data from the Illinois Department of Transportation. Street data from the City of Chicago. Intersection data created with fTools in QGIS. To save time in this initial analysis, I only considered Milwaukee Avenue intersections with streets in the City of Chicago centerline file with a labeled CLASS of 1, 2, or 3.

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