Anti-traffic safety is now a political platform

Three of the five men running for Mayor of Chicago have pledged to eliminate enforcing red light running with cameras. Many aldermen have done the same. The Chicago Tribune has factually pointed out that Mayors Daley and Emanuel have mismanaged the red light camera program, with the bulk of it falling upon staff in the Daley administration. (The only part of the program under Emanuel that could be considered mismanagement was changing the business rules to issue tickets when the yellow light was recorded as 2.90 to 2.99 seconds long; Emanuel’s administration changed the rule back and has implemented many other changes following the inspector general’s report.)

Red light cameras lead to an increase in rear-end crashes but decrease the more severe angle (T-bone) crashes, which the Chicago Tribune “sorta” pointed out when it looked at frequencies but not injury costs.

Current 2nd ward Alderman Bob Fioretti, Cook County commissioner Jesus “Chuy’ Garcia, William “Dock” Walls, and Willie Wilson all have decided that neither the facts nor safety for people inside and outside of multi-ton machines are important. They are supporting the right to endanger others by respecting the inconvenience of not always being prepared to stop at a traffic signal.

Fioretti has said he will introduce soon an ordinance to remove red light cameras by April, but I haven’t found it in the legislation database.

Even though Streetsblog Chicago is no longer publishing, John Greenfield is hustling to get us both working again. In the meantime I intend to cover parts of the election, which takes place February 24, with assistance.

The City of Chicago should implement filtered permeability immediately at Green Street and Milwaukee Avenue

This is not a bikeable block

The multiple threats to bicyclists, to pedestrians, and to motorists, are pervasive in the depicted scene. There is low visibility for turning motorists which in turn causes them to encroach on the right of way of other street users, including other motorists, bicyclists, and pedestrians.

This single image shows everything that is inefficient and dangerous to bicyclists and motorists about the intersection between north-south Green Street and diagonal Milwaukee Avenue in River West. Motorists should be physically blocked from entering or exiting Green Street at this point because of the danger inherent in the current intersection design. The block of Green Street south of Milwaukee Avenue is so insignificant to the driving network that no other design intervention should be implemented.

The situation depicted in this photograph demonstrates why the City should implement filtered permeability and close this entrance of the Green Street/Milwaukee Avenue intersection to motor vehicle movement. While motorists would be barred from entering or exiting Green Street here bicyclists would still have access to Green Street as part of the low-stress bicycling network of which Green Street is a part, between Milwaukee and Van Buren Street in Greektown.

I first wrote about this problematic intersection in June on Streetsblog Chicago and it remains an issue. One of the two motor vehicles ahead is blocking part of the bike lane while the second threatens to enter it. The bus on the left prevents the bicyclist in the bike lane from maneuvering around either vehicle. The photo below shows the situation from a different angle, that of the motorist wanting to turn left.

The vehicle operator on the left – a bus driver, in this case – has stopped the vehicle because they can occupy the intersection with the vehicle, or leave it open. Essentially, the bus driver has stopped to “let” the motorists on Green Street proceed across Milwaukee and further north into Green Street or turn left onto Milwaukee. Their movements would, again, put the bicyclist in danger, and put themselves and other motorists in danger because they are making nearly-blind turns into faster moving traffic.

The threats to the motorist are as limitless as the ones to the bicyclist (although the bicyclist will experience much greater injury if the threat is realized). As the motorist is paying attention to other motor vehicle traffic the bicyclist is coming down this bike lane – yep, I took the photograph – and is additionally obscured by the line of parked cars on the bicyclist’s right, and is in the shadow of the bus and the buildings. It’s like there’s a perfect storm of blind spots.

The quickest way to implement a system of filtered permeability and raise the significance and safety of the Green Street and intersecting Milwaukee Avenue blocks within the low-stress bicycling network would be to install a series of large planter boxes that prevent motorists from entering or exiting Green Street but allow bicyclists to filter through the planters.

And skip any traffic impact study. Not a single parking space will be lost or be made inaccessible with the implementation. A traffic study is not an experiment, but has practically been a foreboding document that has only ever said “things will be different”. (A good plenty of them have also suggested adding multiple $300,000 traffic signals.)

I read on Streetsblog USA recently about Pittsburgh’s new protected bike lanes:

“Instead of asking people to judge the unknown, the city’s leaders built something new and have proceded to let the public vet the idea once it’s already on the ground.”

Chicago maintains a Silver bike-friendly commenting ranking, yet my initial analysis shows that our metrics are below average among other Silver communities after which I’m led to believe we’re undeserving of the medal. Safety is a citizen’s number one concern when considering to use a bicycle for transportation and it will take an expansion of the low-stress bicycling network – currently not a priority – to deserve the current ranking or achieve anything higher.

Two bicyclists take different routes around this driver blocking the bike lane with their car

This photograph depicts a nearly identical situation but from the perspective of the motorist on Green Street approaching Milwaukee Avenue. Two bicyclists have taken two routes around the motorist blocking the bike lane with their vehicle.

Playing around with Chicago data: who’s running red lights?

Range Rover with Illinois license plate "0"

A Range Rover with Illinois license plate “0” is seen moving 40 MPH in a 30 MPH zone through a red light at Ashland and Cortland.

I’m just seeing who’s driving around Chicago one night, using the Tribune-published dataset of over 4.1 million tickets issued from red light cameras.

The City of Chicago has installed at least 340 red light cameras since the mid-2000s to reduce the number of people running red lights and crashing. They’re supposed to be installed at intersections where there’s a higher-than-average rate of right angle (“T-bone”) crashes, which are more injurious than other typical intersection crash types.

Assessing safety wasn’t the Tribune’s story angle, though. It was about showing spikes in the number of tickets issued, which I verified to some extent. The article called the tickets issued during these spikes “undeserved” and “unfair”. The data doesn’t have enough information to say whether or not that is the case; a video or extensive photo review is necessary to rule out rolling right turns while the light was red (a much less dangerous maneuver unless people are trying to cross the street).

The first query I ran assessed the number of people who get more than one ticket from a red light camera. Since I was tired my query was a little sloppy and it missed a lot of more useful order choices and didn’t select the right fields. I fell asleep and started again in the morning. This time, I got it right in just two tries – I needed to try again because I mistakenly put HAVING before the GROUP BY clause.

Here’s the first query, in its final form, to retrieve the number of tickets for each license plate in each state (I assumed there may be identical license plates among states).

select max(ticket_number), max(timestamp), license_plate, state, count(*) AS count FROM rlc_tickets group by license_plate, state HAVING count(*) > 1 order by count DESC NULLS LAST

It resulted in 851,538 rows, with each row representing a unique license plate-state combination and the number of red light violations that combination received. You can reasonably assert that cars don’t change license plates more than a couple times in a single person’s ownership, meaning you can also assert that each row represents one automobile.

851,538 vehicles, which make up 35.1% of all violators, have received 2,601,608, or 62.3%, of the 4,174,770 tickets. (There are 2,424,700 license plate-state combinations, using the query below.)

select count(ticket_number) from rlc_tickets group by license_plate, state

Here’re the top 10 vehicles that have received the most violations:

  1. SCHLARS, IL, 78
  2. 9720428, IL, 59
  3. 8919589, IL, 57
  4. A633520, IL, 52
  5. 3252TX, IL, 45
  6. A209445, IL, 44
  7. N339079, IL, 44
  8. X870991, IL, 41
  9. 239099, IL, 41
  10. 4552985, IL, 40

The next step would be to design a chart to show these vehicles’ activity over the months – did the vehicles’ drivers’ behavior change, decreasing the number of red light violations they received? Did the vehicle owner, perhaps a parent, tell their child to stop running red lights? Or has the vehicle owner appealed erroneously-issued tickets?

When I ran one of the first, mistaken, queries, I got results that put license plate “0” at the top of the list, with only nine tickets (license plates with two or more zeros were listed next).

I googled “license plate 0” and found a 2009 Tribune article which interviewed the Range Rover-driving owner of license plate “0” and the problems he encountered because of it. The City of Chicago parking meter enforcement staff were testing new equipment and used “0” as a test license plate not knowing such that license plate exists. Tom Feddor received real tickets, though.

I then looked up on PhotoNotice the license plate and ticket violation number to find, indeed, the license plate belonged to someone driving a Range Rover at Ashland Avenue and Cortland Street on July 17, 2008. An added bonus was Feddor’s speed in that Range Rover: the camera recorded the car going 40 MPH in a 30 MPH zone.

I was done browsing around for the biggest offenders so next I wondered how many tickets were issued to vehicles licensed in Arizona, where U-Haul registers all of its nationwide vehicles. Arizona plates came in 29th place for the greatest number of tickets.

select count(*) AS count, state from rlc_tickets group by state order by count DESC NULLS LAST

As you may have expected, four surrounding Midwest states, and Ohio, rounded up the top five states after Illinois – but this isn’t notable because most visitors come from there and they each only comprise less than 1.3% of the total tickets. The next state was Florida.

  • 3,986,739, IL
  • 51,104, WI
  • 40,737, MI
  • 27,539, IN
  • 8,550, OH
  • 7,684, MN
  • 7,139, FL

What’s next: I’m working on finding a correlation between the number of reported crashes, and type, at intersections with red light cameras and the number of tickets they issued. I started doing that before running the numbers behind this blog post but it got complicated and it takes a long time to geospatially compare over 500,000 crash reports with over 4.1 million red light tickets.

What else do you want to know?

I will delete all comments that don’t discuss the content of this post, including comments that call red light cameras, or this program, a “money grab”.

Red light camera ticket data insufficient to find cause for spikes

Spike in tickets issued at 119th/Halsted in May and June 2011. Nodes represent tickets grouped by week.

Spike in tickets issued at 119th/Halsted in May and June 2011. Nodes represent tickets grouped by week.

The Chicago Tribune published a dataset of over 4 million tickets issued to motorists for entering an intersection after the light had turned red. They analyzed the dataset and found unexplained spikes, where the number of tickets, being issued by the handfuls each day, suddenly tripled. (Download the dataset.)

I looked at the tickets issued by two of the 340 cameras. I didn’t find any spikes at Belmont/Sheridan (“400 W BELMONT”) but found a noticeable spike in May and June 2011 at the 119th Street and Halsted Street intersection (“11900 S HALSTED”).

I looked at three violations on one of the days that had an atypical number of tickets issued, May 12, 2011. Each motorist was ticketed, it appears, for turning right on red. It’s not possible, though, without watching the video, to see if the motorist rolled through the turn or indeed stopped before turning right.

The Tribune called tickets issued during these “spikes” “undeserved” but that’s hard to say without see the violations on video. The photos don’t provide enough evidence. The Tribune also reported that appeals during these spike periods were more likely to be overturned than in the period outside the spikes. The reporters discussed the possibility of malfunctions and malicious behavior, calling that an “intervention”.

The Chicago Department of Transportation, which oversees the program formerly operated by Redflex and now operated by Xerox, couldn’t refute either allegation, possibly with “service records, maintenance reports, email traffic, memos or anything else”. David Kidwell and Alex Richards report:

City transportation officials said neither the city nor Redflex made any changes to how violations were enforced. They acknowledged oversight failures and said the explosions of tickets should have been detected and resolved as they occurred. But they said that doesn’t mean the drivers weren’t breaking the law, and they defended the red light camera program overall as a safety success story. The program has generated nearly $500 million in revenue since it began in 2003.

The city was unaware of the spikes until given the evidence by the Tribune in January, said David Zavattero, a deputy director for the Chicago Department of Transportation. In the six months since, city officials have not provided any explanations.

“Trust me when I tell you that we want to know what caused these spikes you have identified as much as you do,” Zavattero said. “So far we can find no smoking gun.”

He acknowledged that faulty camera equipment likely played a role.

“I would say that is likely in some of these cases,” Zavattero said. “I cannot tell you that isn’t possible. It is possible. The old equipment was much more prone to break down than the equipment we are currently installing.”

You can download the data but you will likely produce the same results as the Tribune, but maybe a different conclusion. Their analysis has led people at all levels of the civic sphere to call for an investigation, including citizens, some of whom have filed a lawsuit, Alderman Waguespack and 19 other aldermen, and CDOT commissioner – who operates the red light camera program – Rebekah Scheinfeld.

I think they sufficiently identified a suspicious pattern. By the end of the long story, though, the Tribune didn’t prove its hypothesis that the tickets were “undeserved” or “unfair”.

In violation number 7003374335 you can see the driver of a Hyundai Santa Fe turning right at a red light. The Google Street View for this intersection shows that there is no RTOR restriction, meaning the driver is legally allowed to make a right turn here with a red light after coming to a complete stop and yielding to people in the crosswalk. But we can’t see if they stopped first. The next two violations that day at the same intersection I looked showed the same situation. (Find the violation by going to PhotoNotice and inputting 7003374335, NWD648, and CHI.)

I look forward to the investigation. The Tribune made a great start by analyzing the data and spurring the call for an investigation and it seems there’s not enough information in this dataset to explain why there are more tickets being issued.

One dataset that could help provide context – because these spikes, at least the ones that are sustained, don’t seem random – is knowing the number of vehicles passing through that intersection. The speed camera data has this information and allows one to show how different weekend traffic is from weekday traffic.

The "before" image showing the Santa Fe vehicle approaching the stop bar.

The “before” image showing the Santa Fe vehicle approaching the stop bar.

Violation photo 2

After: The Santa Fe vehicle is seen in a right turn.

Note: The Tribune identified 380 cameras but running a DISTINCT() query on the “camera name” field results in 340 values. Some cameras may have identical names because they’re at the same intersection, but you can’t discern that distinction from the dataset.

Going to worship at The Beer Temple takes too long

Minor suggestion to improve Elston-California-Belmont

A map of Belmont, Elston, California with lines and labels that show how I get to The Beer Temple and where I think the city should add car parking.

The Beer Temple opened two blocks from my house in Avondale last year, at 3185 N Elston Ave, on the six-way intersection of Belmont Avenue, Elston Avenue, and California Avenue. This intersection is beastly.

And it’s timed wrong. Since I live southwest of the great craft and imported beer store and it’s on the northwest corner of Elston (a diagonal street) and California, I have to cross twice. I make the first crossing, east-west across California at my street, and then walk north to the second crossing, north-south across Elston.

I cross at my street across California because there’s no light to wait for, and the crossing isn’t diagonal like my other option at Elston (which would mean I walk north, then diagonally south and east). Once I get to Elston, though, I’m screwed because the walk signal is about 15 seconds long but the wait for the next walk signal is about 90 seconds long.

It’s so long because the green for Elston is held for Elston traffic, but also held green for eastbound Belmont traffic that makes a right turn onto southeast-bound Elston. Instead of the walk signal being green for two phases of the cycle (for two of the three streets), it’s green for only one cycle: California’s.

This is because this six-way intersection is the less common type, the type with an island in the middle. It’s got the island because the three streets cross each other at different points and don’t share a common cross point. I’ve got to wait for two phases because Elston needs to stay green for Belmont traffic because you can’t have drivers waiting in the island area – too many cars may stack up and block cross traffic during another phase.

(At many intersections I would just cross whenever there’s a gap between fast-moving cars, but with six-way intersections you don’t always know from where a car will be speeding towards you.)

I get that, but that makes it suck for walking in this area. This design also makes it suck for people biking and driving to turn left from certain streets to other streets because they can’t make the left turn and keep on going. They make the left turn and then have to stop and wait for a second phase to keep going.

I’ve racked my mind for ideas on how to improve this intersection just mildly, in such a way that few would oppose (because that’s really the threshold you can’t cross to have a nice outcome in Chicago).

My idea? Add car parking in front of Dragon Lady Lounge in the “non-identified lane” there. It’s used as a travel lane, or a right-turn lane, depending on who’s driving and how they choose to maneuver their vehicle. It’s not needed for either because of the way traffic moves southbound on Elston past Dragon Lady Lounge and that Elston only has one travel lane in each direction on either side of this big intersection.

The parking would have the obvious benefit of putting customers closer to their destination, but would have the less obvious benefits of protecting people on the sidewalk, buffering noise and speeding vehicles from sidewalk users, and slow traffic past Dragon Lady Lounge when people are parking.

Why are children getting hurt in the street because of “looming”?

Adults are better than children at detecting the speed of a car that’s traveling faster than 20 miles per hour and are more likely to avoid crossing, thus not getting hit. 

Director of New York City-based Transportation Alternatives Paul Steely-White asked on Twitter for a plain English translation of this three-year old journal article about vehicle speeds and something called “looming”.

The article is called “Reduced Sensitivity to Visual Looming Inflates the Risk Posed by Speeding Vehicles When Children Try to Cross the Road”.

Skip to the end if you want the plain English translation, but I’ve posted the abstract below followed by excerpts from Tom Vanderbilt’s Traffic.

ABSTRACT: Almost all locomotor animals respond to visual looming or to discrete changes in optical size. The need to detect and process looming remains critically important for humans in everyday life. Road traffic statistics confirm that children up to 15 years old are overrepresented in pedestrian casualties. We demonstrate that, for a given pedestrian crossing time, vehicles traveling faster loom less than slower vehicles, which creates a dangerous illusion in which faster vehicles may be perceived as not approaching. Our results from perceptual tests of looming thresholds show strong developmental trends in sensitivity, such that children may not be able to detect vehicles approaching at speeds in excess of 20 mph. This creates a risk of injudicious road crossing in urban settings when traffic speeds are higher than 20 mph. The risk is exacerbated because vehicles moving faster than this speed are more likely to result in pedestrian fatalities.

The full text is free to download, but I think Steely-White needs to learn more now, so I pulled out my favorite book about driving, Tom Vanderbilt’s “Traffic”.

Page 95-97:

For humans, however, distance, like speed, is something we often judge rather imperfectly. Unfortunately for us, driving is really all about distance and speed. Consider a common and hazards maneuver in driving: overtaking a car on a two-lane road another approaches in the oncoming lane. When objects like cars are within twenty or thirty feet, we’re good at estimating how far away they are, thanks to our binocular vision (and the brain’s ability to construct a single 3D image from the differing 2D views each eye provides). Beyond that distance, both eyes are seeing the same view in parallel, and so things get a bit hazy. The farther out we go, the worse it gets: For a car that is twenty feet away, we might be accurate to within a few feet, but when it is three hundred yards away [900 feet], we might be off by a hundred yards [300 feet]. Considering that it takes about 279 feet for a car traveling at 55 miles per hour to stop (assuming an ideal average reaction time of 1.5 seconds), you can appreciate the problem of overestimating how far away an approaching car is – especially when they’re approaching you at 55 miles per hour.

[Here comes the keyword used in the journal article, “looming”]

Since we cannot tell exactly how far away the approaching car might be we guess using spatial cues, like its position relative to a roadside building or the car in front of us. We can also use the size of the oncoming car itself as a guide. We know it is approaching because its size is expanding or looming on our retina.

But there are problems with this. The first is that viewing objects straight on, as with the approaching car, does not provide us with a lot of information.


If all this is not enough to worry about there’s also the problem of the oncoming cars speed. A car in the distance approaching 20 miles per hour makes passing easy, but what if it is doing 80 miles per hour? The problem is this: We cannot really tell the difference. Until, that is, the car gets much closer — by which time it might be too late to act on the information.

[the topic continues]

Plain English translation

However, nothing I found in Traffic relates children and “looming”. The bottom line is that children are worse than adults at detecting the speed of a car coming in the cross direction and thus decide wrongly on when to cross the street.

Update: Based on Vanderbilt’s writing, it seems that humans cannot really be taught how to compensate for looming, to build a better perceptual model in the brain to detect the difference between cars traveling 20 and 80 MPH. If this is true, and I’d like to see research of pedestrian marketing and education programs designed for children, it may be that we should stop trying this approach.

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.

When did everyone start caring about bicyclists dying?

A Plague of Cyclists appear to run cars off the road on The Weekly Standard’s cover.

A couple weeks ago a bunch of journalists from major international news outlets were having drinks somewhere (maybe The Billy Goat Tavern in Chicago’s basement) and wrote the same story.

Actually, they didn’t, but it’s surprisingly weird how close they were.

On Sunday the New York Times published “Is It O.K. To Kill Cyclists?”. Next, on Monday, Crain’s Chicago Business published “Why everyone hates bicyclists—and why they hate everyone back”.

Daniel Duane’s op-ed in NYT garnered a lot of response (7 of them are linked here, which doesn’t include Crain’s or The Weekly Standard). The Economist responded to the NYT article with “Cycling v cars: The American right-of-way” saying we should adopt laws like the Netherlands and gave several examples there of who’s liable for a crash between a car and bike (nearly always the driver). Bike Snob wrote the response I most agree with. Karen Altes of Tiny Fix Bike Gang got pissedTwin City Sidewalks (in Minneapolis/St. Paul) wrote that “bicyclists need to stop blaming themselves for dangerous roads”, referring to the bicyclist in question, Daniel Duane, the NYT op-ed contributor.

Tanya Snyder, writing for one of my employer’s sister blogs Streetsblog Capitol Hill, headlined her own roundup post, “The Times Blows a Chance to Tackle America’s Broken Traffic Justice System”. Andrew Smith at Seattle Transit Blog said that he gave up cycling to work in the first week he tried it. Brian McEntee wrote on his blog Tales from the Sharrows about two scenarios to consider about “following laws” (which isn’t what cyclists or drivers should be aiming for).

David Alpert, who runs a Streetsblog-like blog called Greater Greater Washington, said that it’s not okay to kill cyclists, “but if a spate of other op-eds are any indication, it’s sure okay to hate them and the facilities they ask for in a quest for safety”. BikeBlogNYC later published myriad examples of how streets continue killing everyone who’s not driving a car.

Then The Weekly Standard published something very similar to Duane’s piece. I don’t know when – it’s in the issue marked for November 18, but I believe it went up Monday, with a sweet cover. It went by two names. On the cover, “A Plague of Bicyclists” (by Christopher Caldwell) and on the site, “Drivers Get Rolled: Bicyclists are making unreasonable claims to the road—and winning”.

Most of the proceeding discussions revolve around “who’s right”. And the Economist skirts discussing the answer and instead just gives the answer: the bicyclist, because they’re the ones who die.

When you are driving in the Netherlands, you have to be more careful than you would when driving in America. Does this result in rampant injustice to drivers when accidents occur? No. It results in far fewer accidents. As the ANWB [Royal Dutch Touring Club, like the AAA] says, some drivers may think the liability treatment gives cyclists “a blank check to ignore the rules. But a cyclist is not going to deliberately ride through a red light thinking: ‘I won’t have to pay the damages anyway.’ He is more likely to be influenced by the risk that he will land in the hospital.”

I like what Evan Jenkins, a sometimes urbanist blogger studying mathematics at University of Chicago, wrote on his Twitter timeline:

That’s encouraging. He linked to several of his past articles about cyclist murder.


What’s also funny about this weekend’s bike-journo-fest is that Whet Moser, writing for Chicago Magazine, interviewed me two weeks ago about bike infrastructure and penned this uncomplicated, unruffled but comprehensive article saying “drivers and cyclists don’t have to be angry and fearful…with smart planning, a city can design safe roads for all.”

Chicago has started on that path. You know what might influence more change than any bike lane built? Speed cameras. And no, I won’t let them be removed.

Updated multiple times to add more responses to Duane’s op-ed. 

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