Tagcrash data

Chicago Crash Browser, miraculously, has 2012 bicycle and pedestrian crash data

Screenshot shows that you can choose your own search radius. When researching, be sure to copy the permalink so you can revisit your results. 

I’ve upgraded the Chicago Crash Browser, my web application that gives you some basic crash and injury statitics for bicyclist and pedestrian crashes anywhere in Chicago, to include 2012 data. It took the Illinois Department of Transportation eight months to compile the data and it took me four months to finally get around to uploading it into my database. While I spent that time, I made some improvements to the usability of the app and output more information. Since the last major changes I made (back in February 2013) I’ve gained two code contributors (Richard and Robert) making this my first communal project on GitHub.

I know that it’s been used as part of research in the 46th Ward participatory budgeting process for 2013, and by residents in the 26th Ward to show Alderman Maldonado the problem intersections in the Humboldt Park area. Transitized recently included pedestrian crash stats obtained from the Crash Browser in a blog post about pedestrianizing Michigan Avenue in Streeterville.

The first change I made was adding another zoom level, number 19, so you can get closer to the data. I made some changes to count how many people were injured and total them. You can now choose your search distance in multiples of 50 feet between 50 and 200, inclusive. As is typical, I get sidetracked when I notice errors on the map. Thankfully I just fire up JOSM and correct them so the next person that looks at the map sees the correction. Future changes I want to make include upgrading to the latest jQuery, LeafletJS, and Leaflet plugins. I’d also like to migrate to Bootstrap to improve styling and add responsive design so it works better on small screens.

Sign up for the newsletter where I’ll send a couple emails each year describing new changes (I’ve so far only published one newsletter).

Why do speeding crashes in Chicago lead to worse injuries?

Don’t git behind me. Photo by Richard Masoner. 

A discussion about Chicagoans’ proclivity for tailgating (on a post about speed cameras) prompted me to look at the prevalence of this in causing crashes. I looked at the three-year period of 2010-2012 first, mainly so the numbers wouldn’t be so large, and left this information in a comment. But considering the prerequisites* for a crash to be reported in this dataset, and my desire to compare two multi-year periods, I switched my analysis to the single four-year period 2009-2012.


Total crashes: 318,193. Total fatalities: 554 people.

Tailgating crashes

62,080 crashes, 19.53% of all crash types

Tailgating crashes, injuries breakdown:

  • Killed: .0012 (this represents the number of deaths per crash). 75 people died in these crashes, representing 13.54% of all deaths.
  • Incapacitating injuries: 8.53% (the average distribution of people’s injuries in all tailgating crashes)
  • Non-Incapacitating: 46.32%
  • Possible injury: 45.15%

The share of all crash types that are tailgating has increased steadily from 18.11% in 2009 to 20.79% in 2012.

Speeding crashes

10,339 crashes, 3.24% of all crash types

Speeding injuries:

  • Killed: .0118 (this represents the number of deaths per crash). 122 people died in these crashes, representing 22.02% of all deaths.
  • Incapacitating injuries: 15.55% (the average distribution of people’s injuries in all speeding crashes)
  • Non-Incapacitating: 51.95%
  • Possible injury: 32.50%

The share of all crash types that are tailgating has decreased slightly from 3.72% in 2009 to 3.02% in 2012. While speeding leads to fewer crashes, it leads to a greater incidence of death and serious injury. The probability of a speeding crash leading to at least one death seems to stay steady through the period while the probability of seeing a person with an incapacitating injury versus a different kind of injury varies more, but not so much in a range that overlaps the rates for tailgating crashes.

A future comparison at injuries should look at the top crash causes for death and serious injury.

N/A and Unable to determine crashes

237,729 crashes, 74.71% of all crash types

N/A and unable to determine injuries:

  • Killed: .0013 (this represents the number of deaths per crash). 305 people died in these crashes, representing 55.05% of all deaths.
  • Incapacitating injuries: 9.38% (the average distribution of people’s injuries in all N/A crashes)
  • Non-Incapacitating: 48.26%
  • Possible injury: 42.35%


Updated December 4, 2013

I updated the wording on how to interpret these numbers. For example, previously for “killed” there was a percentage saying this number represented the amount of crashes that had at least one death. This wasn’t accurate: the same number represents a rate of deaths per crash of that type. Injury percentages represent the distribution of injury types experienced by all the people injured in crashes of that type.


Analyzing crash causes is not very reliable as 45.60% of the reported crashes in 2012 had “N/A” or “unable to determine” listed as the primary cause! The third and fourth most frequently ascribed causes were the two tailgating codes (described below). There are some crashes that had the one of these two causes in the secondary cause field but I haven’t calculated that.

Cause code descriptions

Each crash has two cause codes. For tailgating crashes I searched for reports where “failing to reduce speed to avoid crash” or “following too closely” in either the primary or secondary cause field (it’s possible that a report had both of these causes ascribed). For speeding crashes I searched for “speed excessive for conditions” or “exceeding speed limit” in either the primary or secondary cause fields.


This data excludes crashes where there was no injury or no property damage greater than $500 (2005 to 2008) and $1,500 (2009 to 2012). You cannot compare the two datasets when you want to see a share of all crashes because the number of “all crashes” will be underreported in the second dataset.


These are some of the MySQL queries I used to get the data out of my own crash database (I’m figuring out ways to make it public, using a shared login). “Cause 1 code” indicates the primary cause of the crash according to the police officer’s judgement. “Cause 2 code” indicates the secondary cause of the crash according to the police officer’s judgement.

1. Crash cause reliability: SELECt count(casenumber), sum(`Total killed`), `Cause2`, `Cause 2 code` FROM `CrashExtract_Chicago` WHERE year = 12 GROUP BY `Cause 2 code`  ORDER BY cast(`Cause 2 code` as signed)

2. Speeding crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 1 OR `Cause 1 code` = 27 OR `Cause 2 code` = 1 or `Cause 2 code` = 27) AND year > 8

3. Tailgating crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 3 OR `Cause 1 code` = 28 OR `Cause 2 code` = 3 or `Cause 2 code` = 28) AND year > 8

4. N/A and Unable to determine crashes: SELECT count(casenumber), sum(`Total killed`), sum(`totalInjuries`), sum(`A injuries`), sum(`B injuries`), sum(`C injuries`) FROM `CrashExtract_Chicago` WHERE (`Cause 1 code` = 18 OR `Cause 1 code` = 99) AND year > 8

The 2012 Chicago bike crash data is in

Crash statistics differences from 2011 to 2012

I posted this as a series of tweets on Friday night.

Links between pedestrian safety and crime

Chicago Pedestrian Plan

Safety item 20: Analyze the relationship between pedestrian safety and crime (download the plan)

The 2011 Chicago Pedestrian Crash Analysis identified a strong correlation between community areas with high numbers of pedestrian crashes and community areas with high crime rates. Correlation does not indicate causation and further study is necessary to understand this relationship and the potential broader benefits of pedestrian safety improvements. [From page 62 in the 2012 Chicago Pedestrian Plan.]


Short Term

  • Identify and obtain funding for this study.
  • Identify a location for safety improvements and obtain data for the “before” conditions.

Mid Term

  • Design and implement pedestrian safety improvements.
  • Develop a pedestrian safety enforcement plan for the area for the duration of the project.
  • Analyze the effects on pedestrian safety and crime.


  1. Initiate this study by 2013 and complete by 2015.


National Highway Traffic Safety Administration. Data-Driven Approaches to Crime and Traffic Safety (DDACTS). 2011. [I don’t fully see the connection, but this reference was linked to a page on NYC Department of Transportation’s website.]

Pedestrian Crash Analysis

The summary report didn’t contain the word “crime”. The technical report contained 2 mentions, with an additional chart. They are quoted in the ordered list below. Download the summary report.

  1. In an examination of various factors including crime, income, race, language spoken, and Walk Score®, the strongest correlation found was between pedestrian crashes and crime
  2. Finally, crime statistics were compared to pedestrian crashes to determine if a correlation could be identified, using data from the Chicago Police Department (CPD) annual reports for 2005 through 2009. The annual reports include incidences of crime by Chicago Community Area (CCA). The statistics for the years 2005 through 2009 were averaged and compared to the aver- age number of fatal and serious injury pedestrian crashes over the same time period in each CCA. Of these factors, crime was the only variable that correlated to pedestrian crashes. Figure 1 shows the correlation between crime and pedestrian crashes was very high. However, there may be many variables responsible for this correlation.
  3. Figure 1: Crime vs. Fatal and Serious Injury Pedestrian Crashes by Chicago Community Area

Figure 1.

I have a few criticisms of this analysis: it lacks raw data; the data tables included in the technical report are of limited length, listing only the “top” items of any metric; the summary report lists many silly factoids; the maps are low resolution and of a limited scale – their design could be modified to improve their usefulness in communicating the crash frequencies of the marked locations. The analysis is reliable.

The technical report includes the state’s guide on how police officers are trained to fill out a crash report form. It also includes relevant crash reporting laws in Illinois. Download the technical report.

Special post for S.M.

Stats from the OECD: Comparing traffic injuries of the United States and Netherlands

For an article I’m writing for Architect’s Newspaper about the Chicago Forward CDOT Action Agenda, I wanted to know about traffic injuries and fatalities in the United States, but compared to the Netherlands and Denmark and other places with a Vision Zero campaign (to have 0 traffic deaths each year).

I already knew the OECD had a good statistics database and web application. With a few clicks, I can quickly get a table of traffic injuries (casualties) listing just the countries I want. I can easily select the years I want, too.

In one more click the web application will show a time animated bar chart. A feature I’d like to see added is dividing the figure (in this case traffic injuries) by the population. Check out the video to see what it looks like. The United States looks to be in terrible shape, but our country has several times more residents.

I had trouble downloading and opening the CSV file of the data table I created. The XLS file was damaged, also. The built-in Mac OS X Archive Utility app couldn’t open the .gz file, but I used The Unarchiver app successfully.

My calculations, based on data from OECD (national population and traffic fatalities), Illinois Department of Transportation (IDOT), and the American Community Survey:

Fatalities per 100,000 in 2009

  • United States: 11.02472
  • Denmark: 5.48969
  • Netherlands: 4.35561
  • Sweden: 3.84988
  • Chicago: 16.74891
  • United Kingdom: 3.83555

Chicago’s fatality rate per 100,000 citizens in 2009 was 16.75 (473 deaths on the roads). The fatality rate dropped in 2010: just 11.65 deaths per 100,000 residents (315 deaths on the roads; the population also decreased).

Updated September 28, 2012, to add the United Kingdom. 

Terminology debate: crash versus collision

The following is an email conversation between myself and Travis Wittwer, a cool guy in Portland, Oregon, whom I stayed with in April 2010. We’ve had similar conversations before about the language writers (mainly newspaper article authors) use when speaking about and describing situations where “people and their bicycles make contact with people and their cars” (yes, there’s an easier way to say that, read on).

Travis: Continue reading

Slicing the crash data into interesting visualizations

The Chicago Crash Browser as it looks now. This only exists on my laptop and no place else. I can’t put it online because it’s so inefficient it would kill the server. 

I presented my Chicago Crash Browser to attendees of an OpenGov Hack Night three weeks ago and gathered a lot of feedback and some interest from designers and programmers there.

We collaboratively came up with a new direction: instead of focusing on creating a huge web application that I proposed, we (anyone who wants to help) would start small with a website and a couple of crash data visualizations. The visualizations would serve two purposes:

  • attract attention to the project
  • start building a gallery of data-oriented graphics that describes the breadth and extent of the crash data

Continue reading

How high (and low) expectations can make traffic safer

I have low expectations of fellow Chicagoans who are moving their vehicles on the same roads I cycle on. I expect that every door will fling open in my path, causing me to be doored. I also expect to be cut off at any moment, and especially in certain places like at intersections (where the majority of crashes occur), bus stops, or in places with lots of parallel parking activity. Because of these expectations I feel that my journeys have been pretty safe. My low expectations cause me to ride slower, ride out of the door zone, and pay attention to everyone’s maneuvers.

This is another post inspired by Traffic: Why we drive the way we do (and what it says about us) by Tom Vanderbilt. From page 227 of “Traffic”, about expectations :

Max Hall, a physics teacher in Massachusetts who often rides his collection of classic Vespas and Lambrettas in Rome, says that he finds it safer to ride in Rome than in Boston. Not only are American drivers unfamiliar with scooters, he maintains, but they resent being passed by them: “In Rome car and truck divers ‘know’ they are expect not to make sudden moves in traffic for fear of surprising, and hurting, two-wheeler drivers. And two-wheeler drivers drive, by and large, expecting not to be cut off.”

The scooter drivers have high expectations, and it seems that they’re being met.

This all plays nicely with the “safety in numbers” theory about cycling: the more people who are riding bicycles, the more visible bicycling is, and the more aware a driver will be around people who are bicycling, and the more they will expect someone on a bicycle. Awareness means caution.

It’s difficult to gauge the safety of cycling in Chicago as we’ve no exposure rate: we don’t know how many people are cycling how many miles (nor where).

A cyclist waits for the light to change at Milwaukee Avenue and Ashland Avenue. 

Exposure rate

Exposure rate in the sense I’m using it here means the number of times someone is in a crash or injury for each mile they ride. We know how many crashes and injuries are reported each year (in the Illinois Motorist Crash reports), but we don’t know how many miles people ride (neither individually nor an estimated average).

There was a limited household survey of Cook County residents in 2008 from CMAP, called Travel Tracker, that collected trip distance information for all trips members of a household made on all trip modes – I haven’t looked into this yet.

It would be highly useful if the Chicago Department of Transportation conducted ridership counts at the 10 intersections with the highest crash rates. And if the 10 intersections changed the following year, the new intersections would just be added to the initial 10 to track the changes of the initial 10. This would be one step closer to being able to determine a “crash rate” for each intersection.

Crashes by bike or by foot at different intersections

While working on a private web application that I call Chicago Crash Browser, I added some code to show the share of pedestrian and pedalcyclist crashes. The site offers users (sorry I don’t have a web server that can make it public) a list of the “Top 10” intersections in terms of bike crash frequency (that’s bike+auto crash). You can click on the intersection and a list will populate showing all the pedestrian and pedalcyclist crashes there, sorted by date. At the bottom of the list is a simple sentence that tells what percentage pedestrian and pedalcyclists made up at that intersection.

I’m still developing ideas on how this information may be useful, and what it’s saying about the intersection or the people using it.

Let me tell you about a few:

Milwaukee Avenue and Ogden Avenue

I mentioned in my article Initial intersection crash analysis for Milwaukee Avenue that this intersection is the most bike crash-frequent.

23 crashes within 150 feet of the center, 2005-2010

82.61% bike crashes **

17.39% ped crashes.

Ashland Avenue and Division Street

28 crashes within 150 feet of the center, 2005-2010

46.43% bike crashes

53.57% ped crashes **

Milwaukee, North and Damen Avenues

46 crashes within 150 feet of the center, 2005-2010

39.13% bike crashes

60.87% ped crashes **

Halsted Street, Lincoln and Fullerton Avenues

38 crashes within 150 feet of the center, 2005-2010

42.11% bike crashes

57.89% ped crashes **

Montrose Avenue and Marine Drive (Lake Shore Drive ramps)

11 crashes within 150 feet of the center, 2005-2010

90.91% bike crashes **

9.09% ped crashes

Why do you think some intersections have more of one kind of crash than the other?

People walking at Milwaukee-North-Damen.

The Chicago Crash Browser can be made public if I have a host that offers the PostgreSQL database. Do you have one to offer?

Some crash analysis based on gender/sex in Chicago

A friend sent me an article saying that in London, women were experiencing crashes more often than men while cycling (BBC article). He asked if this was true about Chicago. So I crunched the numbers. This is very low-level, initial, take it with a grain of salt analysis. It appears that that is not the case for Chicago.

It appears that men walking or cycling are involved in disproportionately more crashes with automobiles than women, but not very disproportional.

1. Men make up 72.73% of people cycling (to work, the only trip purpose for which I have data). And they experienced 75.75% of the crashes with automobiles (to where is unknown).

2. Men make up 48.45% of people walking (to work, again, the only trip purpose for which I have data). And they experienced 53.38% of the crashes with automobiles (again, to where is unknown).

This analysis also points out shortcomings in our data. Even with National Household Travel Survey I don’t think I could get very detailed (as the statistics would be too aggregated and the sample size for Chicago would be small). This data is based on two sources: motorist crash reports from 2007-2010 from the Illinois Department of Transportation (IDOT); and American Community Survey “means of transportation to work”. The last time a household travel survey was done in Chicago was 2008 and I just acquired that data last week. I need to figure out how to connect the people and trip tables and then I can do more analysis, getting exposure data for all trips, not just work.

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