I was thinking about the same thing recently. Personally I am curious as to how many forum members have been hit by a car or something similar. For me it happened in 8th grade - T-boned by a white Ford Bronco. Been lucky ever since but I have had another dozen close calls. I know a fair number of people who have been clipped by cars, but nobody I personally knew has died. Alternatively, I have a few people I know who died in car accidents.

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me, i know a lot of triathletes. and i know everyone who's died while on a bike in the last 3 years. and if i forgot somebody, i could easily get hold of all the triathletes i know and ask them who i've forgotten.

now, accidents, that's a little tougher. that might be possible if we're talking a specific medical event, such as paralysis. but, road rash? broken finger? i'd like to start by getting our arms around a stat we can feel confident in, and i suspect death during cycling while cycling is the proximate cause or salient activity (rather than having a heart attack, or you got caught in a tidal wave while cycling.

this i think maybe we can do.

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Dan Empfield
aka Slowman

What is the metric you're trying to work toward?

% chance of being killed in a given year (independent of training volume)?

or

Risk per unit of activity (ex. hour or mile)?


The second one is harder to get (news articles don't typically include training history), but is more typical for safety statistics. We might be able to get that for a small subset (top AGers, pros) and extrapolate.

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I was hit by a car in 2000 and was lucky to walk away with just a few scrapes and bruises. Had I been a second quicker would have likely had a very different outcome.

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"You can never win or lose if you don't run the race." - Richard Butler

what are you trying to get to with the data? There is already a ton of data online, such as here: http://www.iihs.org/...talityfacts/bicycles

"What is the metric you're trying to work toward? "

what is my chance, your chance, of getting killed while riding a bike. per year, per 5 years. per decade. if you ride for 20 years, 30, 40, 50. your risk over your lifetime, depending on what you consider a lifetime of riding, is that your chances of dying while riding your bike on the road is 1 in 8. or in 80. or 800. or 8000. or 80,000.

that's it.

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Dan Empfield
aka Slowman

"what are you trying to get to with the data?"

see my post just above.

"There is already a ton of data online"

i know a guy who got killed riding his bike while drunk, home from a bar, and he was riding his bike because his license was taken away because of DUIs. that's not my cohort. i'm interested in MY risk associated with road cycling.

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Dan Empfield
aka Slowman

This might help you -http://www.rideofsilence.org/memoriam.php

Since you mention limiting this to triathletes, what I'm most interested in knowing as a triathlete is not my risk of death from cycling but my risk of injury from cycling that ends my ability to compete in triathlons.

I have a ridiculous fear of flying and found these stats ( or similar stats in many places), while trying to convince myself I would be safe flying. Apparently I should be more worried on my bike....a lot more worried.
DEATH BY: YOUR ODDS
Cardiovascular disease: 1 in 2
Smoking (by/before age 35): 1 in 600
Car trip, coast-to-coast: 1 in 14,000
Bicycle accident: 1 in 88,000
Tornado: 1 in 450,000
Train, coast-to-coast: 1 in 1,000,000
Lightning: 1 in 1.9 million
Bee sting: 1 in 5.5 million
U.S. commercial jet airline: 1 in 7 million

You might still start off by looking at all bicycle deaths, since that number is easy to find, and then get to the exercise related risk by excluding the ones that don't fit. If you start from scratch going only by what has been talked about on the internet, chances are there will be many that you missed.

i take it then that you think that what i'm proposing has no merit?

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Dan Empfield
aka Slowman

I 100% want this data. People right by me keep dying, I bike into southern Michigan.

If my risk of biking 10 hours per week outside was 1 in 10,000 to die per year I would still bike.

If it were 1 in 100 to die per year I would not bike.

Not sure exactly where my cutoff would be. Perhaps 1 in 5000 chance per year given 520 hours of biking a year, so approx 1 in 2,500,000 odds per hour biking I'd my drunk math us correct.

Before this thread breaks down into something else, I'll give a bumper sticker of my own experience: I was hit by a car in 1992 and got away with just a broken nose...I continued to train on the roads until my brother was hit and killed on the same road in 1998 (both took place in Bloomfield, NJ).

As long as this is not normalized to miles ridden, or time spent on a specific setting (urban/suburban/country) and road layout (w/o bike lane etc.pp), these data are not going to mean anything.

I actually encourage everybody to make their own assessment/quantification, by looking where they ride and when, take into account where chances are higher to get hit (or where people had been hit/killed).

Personal risk assessment is way more meaningful than calculating a generalized risk.

There are no guarantees in statistics, ever.

Statistics are a much much much much much better way to evaluate risk than to "look around when you ride".

Source - worked on wall Street.

I wouldn't ride Wall Street to begin with.

You might be able to calculate average risk for a population of cyclists. But I'm not sure you could get risk that's meaningful to you personally.

There are too many local variables. Your skill as a rider, and what type of riding you do. Where you ride. When you ride.

It's sort of like heart attack risk. We have good numbers for average. But # may be way, way, off for *you*.

Hard to say as riding your bike in a park on a bike path is very different than riding on the shoulder of a major road. The general bike stats will understate the odds for those of us on the road.

"As long as this is not normalized to miles ridden, or time spent on a specific setting (urban/suburban/country) and road layout (w/o bike lane etc.pp), these data are not going to mean anything."

okay, so you're out. anyone else like windshatten, who's out, please do me a favor and don't respond. anyone who's in, and who has worthwhile ideas to help the project, i welcome your commentary.

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Dan Empfield
aka Slowman

"There are too many local variables. Your skill as a rider, and what type of riding you do. Where you ride. When you ride... It's sort of like heart attack risk. We have good numbers for average. But # may be way, way, off for *you*."

okay, so you're out. thanks. for those who're out, not necessary to respond. maybe start another thread. only interested in those who creative, intelligent ideas on such a project.

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Dan Empfield
aka Slowman

I THINK if you could get to deaths per year among USAT members while cycling outside of competition it gives us some basic data. But really basic in the order of "deaths per 1000" in USA. I don't think it gives you any real idea on how to assess your personal risks riding. There are a zillion reasons why your own stats may be better or worse based on time of day, traffic, lighting, weather, your own awareness of the road, the awareness of that one stupid guy about to kill you or not, your own bike handling skills, were you riding tri or road bike or mountain bike, road surface, biking lane or not. I can personally play with most of these odds to improve my personal stats orders of magnitude better than the overall deaths per 1000 USAT members while cycling outside of competition....or I could make my personal stats worse by doing stupid things. Your findings would spread/normalize all these factors, however, one could say that all the deaths you come up with, the rider+terrain+traffic all worked in a perfect storm to result in death.

When I was in the Air Force and we were trained on flight safety investigations, it was all about the contributing factors in digging up "what happened". At the end of the year's annual flight safety briefing from the Director General we'd get all the weighing factors that applied to each accident/crash and you'd see a lot of patterns emerge. Since there is no traffic in the sky, the most important factor was pilot/human/aircrew misjudgement. In the case of cycling, we have the pilot of the bike and we have the pilot of the car...you just need one to do something stupid and the rider dies. If even one does 1 thing less stupid, often nothing happens.

All this to say, please collect the overall number and then it's up to us to make our odds better when we hit the road. Sometimes shit happens and you get hit. Hopefully nothing fatal happens. I've been hit by cars 5 times, 4 were pretty bad, but most of the nonesense happened commuting to work downtown by bike.

Since I started working in suburbia 20 years ago, I only got hit once, and of course it was at rush hour, in low light by someone late for work, in a congested spot and the guy hit me head on on a narrow road when he pulled out to pass the van in front of him and did not see me coming at him. My spider sense told me something bad was going to happen when I felt that car that pulled out in front of my face, accelerating and closing the gap on the vehicle in front. By then, the only thing I could do was vertically eject....thanks to years in Armed Forces vaulting shows, I kind of knew how to clear the pommel horse...but hard with a bike attached to your legs, so I saved my head, core upper legs but the lower legs clipped by the vehicle...I just found the thread from 2005:


http://forum.slowtwitch.com/...%20pocket%20#p444322


It's an example of "you can't do anything" at some point even when you do your best to reduce risk...I guess that's when the average stats kick in. Also that's my 39 year old self. My 50 year old self, on the same road at the same time, riding the same bike at the same speed, probably does not have the athleticm to pull off that life saving vault. On the one hand older more experienced riders have the smarts and more spider sense on the road. But young riders have better reflexes and general athleticism to pull out of near death jam with better chances of survival.

"I THINK if you could get to deaths per year among USAT members while cycling outside of competition it gives us some basic data."

i agree. but we can't get those stats. they don't have those stats. so we'd need to develop the stats ourselves.

"There are a zillion reasons why your own stats may be better or worse"

of course. i was wondering how long it would take to state the obvious, that depending on the roads you ride, your skill level, etc., your odds of a problem would be better or worse than the mean. i've now seen that 3 times in the first 12 posts. can we stipulate to that? can we get that out of the way? can we stipulate that if i wear dark clothing, ride at night, every night for 50 years, in the middle of the road, that my chances of dying are higher? if so, then perhaps we can get past the first batch of inane and useless posts. i understand it's the internet. i understand we need to wade through the bullshit before we get down to the good stuff the internet can be used for. i'm just hoping we can push through this quickly.

look, here's the point of this. people are actually deciding to not ride the roads anymore for one reason: the success of communities like this of letting us know every time one of us dies. people aren't swimming anymore, for the same reason. i'd like to quantify the risk. we have people making decisions on whether to fly on an airplane or run in a state park because of airline crashes and mountain lion attacks they've read about. people are afraid to rough water swim because of shark attacks they hear about. but when you do the math, the odds are low.

yes, if you decide to specifically swim only in shark infested waters, when sharks are infesting them, your chances go up. yes, there are things you can do to make YOUR chances of not getting hit on a bike better than the mean. but we still, i think, for the good of everyone, would feel better if we did actually establish data on things like bicycle deaths, heart attacks in the swim, and so forth. then we'd know the risk for ourselves and for new folks who might contemplate joining this sport.

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Dan Empfield
aka Slowman

Dan, related to my last post, if you could group the deaths by age group it may provide an additional layer of useful info. If you can possibly categorize the deaths further by urban vs rural it would be an additional useful bit of data. If you could group by "before 9 am and after 4 pm" as one group and 9 am to 4 pm as another group, this would further be useful data. Finally if you could group by Mon-Fri, vs Sat/Sun/Stat Holiday, this would be really useful.

Perhaps we'd see that the worst stats are before 9 am or after 4 pm, Mon to Fri, in urban areas, riders 50 and over. At least intuitively I'd expect that to be the case

If you want to take it further and divide your USAT numbers in percent males and percent females and then look at the death stats for each per 1000, maybe we might learn whose behavior is more risky on the road.

Hi Dan, see my last post. Let's work to compile the data and then let's see if we can further categorize them in buckets mentioned in my last post. Then you have the averages and then you can see when they get worse or better. If people see that the odds of riding in rural setting on weekend are really good in terms of not dying but Wed after 4 pm in downtown are bad, maybe they stick to the trainer during the week but at least get out on weekends because the odds are 100x worse in the weekday example.

I think you and I are aligned that we want people out there riding and true risks are already really low and we can make them even better with precautions.

You're being a dick. You asked, "do you think we could generate good statistics? what questions, limits, rules, processes, behaviors, cautions, have i not thought of? "

But you don't want questions or limitations. You want yes-men.

Have fun.

And don't try to play the death-card on me. I've watched cyclists, on the ground, in front of me.

What about using Strava or Training peaks aggregate data of miles ridden (or hours ridden) and compare that to the number of deaths in a given time period (which should be publicly available). I don't know if those sites would provide thisbinformation but it would at least be a start, and most folks on Strava / Training peaks are serious athletes. The idea is as such:

In California in 2016, x number of cyclists were killed compared to y number of miles ridden for that same time period.

This would likely overestimate the risk, as not everyone is on Strava or training peaks, but it would at least be a start. I'm interested in this risk, because I'm hesitant to ride outside (wife, two young kids and a mountain of medical school debt).

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Loosing battle.
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It has been made clear, that dissent contributions are not appreciated..

This is from 1993. Cycling fatality rates have declined since then.

If I ride for 10 hrs a week, 52 weeks per year, for 50 years, my total hours are 26,000. According to the list below the odds of being killed are 1 in 3.85M hrs, so I have 0.7% chance of being killed on my bike in my lifetime. That's if I'm average.

I think my odds are better than average. I try to be visible and avoid riding into the sun whenever possible. And I live in a place where the drivers seem to be courteous and aware. I'm mostly on rural roads with no shoulder, but traffic is light. I also have a lot of riding experience, which surely helps.

Fatalities per million hours
Sky diving
128.71
General aviation
15.58
On-road motorcycling
8.80
Scuba diving
1.98
Living (all causes of death)
1.53
Swimming
1.07
Snowmobiling
0.88
Passenger cars
0.47
Water skiing
0.28
Bicycling
0.26
Flying (scheduled domestic airlines)
0.15
Hunting
0.08
Cosmic radiation from transcontinental flights
0.035
Home living (active)
0.027
Traveling in a school bus
0.022
Passenger car post-collision fire
0.017
Home living (including sleeping)
0.014
Residential fire
0.003
Data from Failure Analysis Associates, Inc (now Exponent Inc), Design News, 10 April 1993.

I wonder if there is a proxy that can be used to determine if the risk has increased or not.

For car seatbelts it was fractured femurs. The increased use of seatbelts led to a decrease in incidence of femoral fractures.

I think you need:

• Population size - that ride outside, not total triathletes
• Time / Distance ridden
• Total Deaths minus obvious non cycling related causes
• I think you need to identify if there is an increase in car / bike interactions in general

I'm not entirely sure that this needs to be limited to deaths in the same way that hospital mortality is not the only indication of quality. Hospital acquired infections are a proxy for quality and tracking them overtime is an indicator of quality. Cyling incidents are not just limited to deaths and IF there were an increase in deaths, you'd expect (reasonably) to see a corresponding increase in other cycling incidents as its a reasonable assumption to think that drivers are not sat in their car saying if they can't kill them they won't hit them at all.


I would think its a reasonable starting point to look at both accidents / interactions that result in a police report irrespective of death and compare the total of those over time against deaths in the same number of reports.

Where do I get statistics for chance of being killed while not riding like a dumbass? Those are the only stats I care about.

i think the biggest problem here is probably going to be the denominator. (andrew's already pointed out that the numerator could be expanded, too - mortality's the most salient for riders, and the most unambiguous to measure, but there are plenty of people who survive accidents that are nonetheless life-changing, career-ending, etc.).

so:
-we're measuring deaths per X. person-miles? person-hours? cyclists? outdoor cyclists? total population?
-i think rates would be preferable to absolute numbers here, since the popularity of cycling (and driving) has definitely gone up and down, and all else equal, more riders and more cars on the road would mean more accidents overall, if not relatively.
-if you wanted to calculate and odds ratio or relative risk, you'd probably want to get an 'exposed' group (cyclists) and a control group (non-cyclists), follow them for, say, a year, and then it's simple arithmetic to work out risk of death in both groups. unfortunately this doesn't get us very far, since we can assume that the risk of being hit by a car while cycling is zero for non-cyclists.

-so on that front i think number of deaths per km (or hour) ridden would be 'best'.

-in terms of controls, i think there would be a lot of things to account for:
-seasonality comes to mind - probably far more accidents overall in summer. on the other hand, with so few people riding mid-winter, the 'effect size' of a single fatality would be larger.
-gas prices (more cars driving more miles when gas is cheap)
-time of day
-maybe proximity to weekends/holidays would put more drunks on the road?
-geography? many experienced cyclists will tell you that 'the drivers are good' in this area but 'bad' in that one. is there anything to that?
-maybe timing w/r/t advertising campaigns, police safety blitzes, etc
-helmet laws by region

that's about all i've got at the moment. the damn thing with stats is that it's easy to gather a huge pile of data that actually tell you nothing, or at least don't tell you what you think they're telling you.

-mike

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____________________________________
https://lshtm.academia.edu/MikeCallaghan

http://howtobeswiss.blogspot.ch/

when I responded I was thinking about the hours v's distance

Intuitively I would think hours on a bike is better than distance given the range of speeds in cyclists is so great, though with a big enough sample size there is a regression to the average. With cars, distance makes sense as I suspect the spread is slightly lower in terms of speed - I do not know why I think this

On the other variables in your post - I am willing to bet that one of the best sources for data would be ambulance services rather than the police

I worked with London Ambulance for a number of years. Call patterns lead to the constant movement of ambulances over a 24 hour period on standby. Mornings - inward commute, Afternoons - exodus. Weekends away from CBD's.

Patters with respect to accident locations, time of day, severity are all pretty predictable in the same way that ambulance calls first peak is 4-6am as people wake up realising they've had a stroke / other, steady call rates as they go to work, drop off till they go outside for a break mid-morning and get run over, decrease again till lunch, increase, then decrease post lunch until about 2.30-3pm onwards when they continue to steadily rise as people leave work and get injured.

I suspect that the other thing that can be done in terms of looking at the under-reporting of non-fatal incidents would be to undertake a survey of, lets say, the ST population comprising; hours / distance ridden per year, incidents involving medical care, incidents requiring band aids but no clinical input and of these two what percentage were not reported v's reported.

Anyway - as a back of a fag packet means of determining risk it might give some ideas.

I would speculate that much like well established patterns for hospital admissions, hospital mortality, unreported near-misses, ambulance patterns that there are probably some underlying patterns worth being aware of and I would guess that they might be; riding in commute time, lunch time, when schools break out and dawn and dusk, in addition to simply riding in high traffic areas

I agree. I assume bicycle fatality statistics include the drunk guys riding the wrong way in the middle of the night while wearing all black. As well as those blasting through red lights in NYC without looking. I want to know my risk--riding on good roads, in control, wearing reflective gear and lights as necessary, and obeying traffic rules that affect my safety.

Maybe the answer is in the OP, concentrate the data collection on dedicated cyclists in cycle and triathlon clubs. I'm sure that the clubs will remember deaths in the last few years and could give a good guess at the average mileage of their members. Combine the two and you get a pretty good idea of risk for cycle training rides. As stated above, general cycle fatality numbers will contain an awful lot of noise such as deaths due to drunk cycling or night riding without lights. This would also allow you to regionalise the data to see the differences between urban and country areas.

  I would argue that life altering injuries are just as important in telling the story of "risk" as measuring for death.

You would need countless variables to get to a true and meaningful picture. Probably the most important is the time/miles on different types of roads. It would seem obvious that your risk of death or major injury is greater on a busy roadway with minimal or no shoulder vs a bike path with very minimal traffic.

I have always done my outside ride from my house starting with riding on Hwy 49, which lots drive 70 plus and the shoulder is not super wide. But, I have decided
that this is not smart with the odds, so this morning I have putting the bike rack on, and driving to start where there are much few cars. And doing on a Sunday morning.
So yep, where, when and how you ride impacts the odds also.

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Jason80134 wrote:

You would need countless variables to get to a true and meaningful picture. Probably the most important is the time/miles on different types of roads. It would seem obvious that your risk of death or major injury is greater on a busy roadway with minimal or no shoulder vs a bike path with very minimal traffic.

I think you could collect the following data at a high level:

1. Total deaths of cyclists registered in clubs or triathletes in tri clubs
2. Make a guess for total hours ridden in a year. I would expect that USAT and USA cycling have some general stats on the average number of hours their members ride. This is very course but we're talking averages here. I realize some of this will be indoors, but you have to start somewhere. What does the average "serious" cyclist ride per year? 100 hours? 200 hours? I doubt it is 300 hours (almost 1 hour per day).
3. Perhaps get stats on overall bike fatalities nationally urban vs rural. Just apply those overall percentages to the "serious cyclists" we are trying to create stats for.
4. Get overall national ambulance calls to an accident that involves a cyclist and compare it with overall deaths of cyclists (this includes the drunk guy riding in black at 2 am from the bar against traffic, but it's OK, let's use him too). Let's say that ratio is 4:1, then apply the same to derive ambulance calls involving serious cyclists.
5. From the overall ambulance calls, you should be able to get a breakdown of how many urban vs how many rural to get the triathlete breakdown. Having said that, perhaps since we cover more distance, we hit "rural" more often than the average person on a bike.

As a starting point if Slowman can aggregate deaths of serious cyclists from bike and tri clubs, we can then have a basis to overlay some of the general stats on us and perhaps infer how all that applies to our levels of risk.




Maybe an interesting starting point is two ST member polls


Poll 1: How many years have you been riding? (answers 1, 2, 4 , 5-10, 10+)
Poll 2: How many times have you been in an accident where an ambulance was called or should have been called (answers 1, 2, 3, 4, 5+).


If we can get 1000 people answering each poll (and I realize that the same people will not answer both), we can get an idea of experience level distributions of some hard core cyclists, and how often they are involved in a serious crash with a car. Maybe that alone is more useful data for "us".

I think I agree with this idea- the data is most likely to come from collecting data directly from the groups you wish to analyze. Cyclists, triathletes, etc I wouldn't think would be too hard to collect data from. The former tend to congregate in groups, and the second seem to not be strangers to collecting loads of data anyway. I guess you could collect as much as you want and then see which parts are useful, such as age, time of day riding, relative traffic load, etc. I imagine that on a first go, you won't get what you need there without a large and focused study.

But getting data on say, incidents or deaths per hour ridden, should be relatively easy, assuming that self report of those hours and report of incidents is accurate enough.

I would think finding as many cycling groups as possible- teams, clubs and such, sending questionnaires for distribution, and the same to bike shops all over the place, would be a great start. You could target each shop on the Road Show stops and send questionnaires there as well.

Might include:
Hours ridden per week on average, divided by traffic volume, traffic aggression, time of day, day of week, lighting (sunlight and use of lights)
Number of "close calls"
Number of incidents while riding- could split into "by motor vehicle, pedestrian, animal, road conditions, user error"

Information on deaths will be interesting- Since the actual number of deaths during cycling is small and the number of cyclists or cycling hours is big, I'm not sure how much specific data from that cyclists history would really sway the data, but since it's not going to be self report in that instance anyway, you could ask in your questionnaire if the responder knows of anyone that has been killed while riding, and then ask for estimates of hours ridden per year, for how many years. That data is going to be flimsy, but I'm not sure if there's an easy way to collect it. You'd always have duplicates that you'd have to weed out by maybe location of incident, first initial/last name, but that isn't going to be a huge number I'd imagine. Also would like circumstance if known surrounding event.

I think what we really want to know is incidents and/or deaths per hour. Dividing by circumstance is going to be hard. In deaths in air travel, the rate is going to vary based on if you fly in a small plane heading to Martha's Vineyard at night (Kennedy?), weather, experience of the pilot probably... but I'm certain those are grouped into the overall risk. Just as swimming in the ocean at dusk in South Africa probably increases likelihood of getting eaten.

Collect and see what falls out?

Ultimately events/hour is going to be helpful, assuming we are targeting recreational and up.

"Where do I get statistics for chance of being killed while not riding like a dumbass? Those are the only stats I care about."

i agree with you. the one thing i do worry about is the drunk or the phone user. this i can't inoculate myself against. there are a lot of distractions in cars now. navigation systems and other screen touch systems that require i make numerous and rather complicated transactions in the car, while driving.

i'd like to see how often this kind of thing nails us.

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Dan Empfield
aka Slowman

"-we're measuring deaths per X. person-miles? person-hours? cyclists? outdoor cyclists? total population?"

deaths per relatively serious triathletes per time increment. i think the operative guidelines should be:

- a cohort that resembles us, otherwise the data won't pertain to us.
- a cohort that is of a size we can measure accurately.
- incidences we can measure. that we can count.

for example, swim deaths in triathlon don't give us a good measure of the risk, because dale basescu died last month while swimming in a masters workout. if i'm dead i'm dead and i don't feel any better about it because i didn't have a race number written on my shoulder. so, those 3 above, if we keep it simple, keep it to that, i think it's possible.

other bad stuff that can happen, broken leg, whatever, i don't think we should try to count those. deaths only. from deaths i suspect we could refer back to generalized data, such as, for x number of deaths there are y number of broken legs.

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Dan Empfield
aka Slowman

If you could get to "for x number of deaths involving a car and a person on a bike, there were Y number of ambulance trips to get a cyclists hit by car" that might be a useful extra piece. it would allow US serious cyclists to infer the risk of an incident.

To some degree, aside for my loved ones i don't care if I die in car+bike incident. If I die pretty well instantly, I'm dead and gone. Limited pain, no change in quality of life (since the life is over at that moment and financially everyone is taken care of). It's just painful for those who I leave behind, but speaking completely objectively, I'm done over here and left quickly. But if I am in an incident that dramatically changes my quality of life and that of my loved one, this is a risk I also care about.

I think the best way to get a number of data set opportunities is to collect what can be collected by those on this forum that choose to participate.

From x date to x date, name, city, state and notes (hit by drunk or hit by distracted driver etc. if know) and during training/event ride or casual rider and finally - outcome (death, serious injury, walk away).

A spreadsheet on a page that we can go back to and update as we remember or get research collection (over say the next 30 days) for information during the past year.

Then see what can be inferred by what has been accumulated.

Edit: there is a "bike law group" I follow on Linkedin that could also provide a lot of information on incidents over the past year.

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Dan Kennison

facebook: @triPremierBike
http://www.PremierBike.com
http://www.PositionOneSports.com

"for x number of deaths involving a car and a person on a bike, there were Y number of ambulance trips to get a cyclists hit by car"

again, i don't see the relevance. what if we find out that for x number of cycling deaths involving a car, three-quarters of x are people who are commuting to work, or are hit while riding on the wrong side of the road, or for running stop lights?

you know the people on this forum. the minute data is released, any data, regardless of how well constructed, regardless of anything, we'll get 40 posts on why the data is meaningless. therefore, i think any successful data should have a goal of relevance. for it to work, again:

1. the data should be relevant to us, to what we do;
2. the numerator - number of instances - needs to be a number we can count with confidence;
3. denominator, same thing, a number we can count with confidence.

if asking ambulance companies to provide us their data will do that, i'm all for it. but don't know ambulance companies. i do know 800 tri club presidents. i do know 1100 local bike shop owners and 700 run store owners. i do know 700 tri coaches. i do know all of you. and, you all know each other, or, you all are within 2 degrees of separation from every triathlete in north america.

triathletes are a pretty good cohort, in terms of relevance to us.

---

Dan Empfield
aka Slowman

When I watched the new report the other night, when they said car accidents have gone way up, after years of going down, and they had no idea why.
Cyclists, 13% up. Pedestrians 10% up.

Seems so easy the answer, you stated most but missed the more "drugs" being used. We have had pedestrians killed with the driver using Ambian as the excuse, so a drug,
anything which impacts the body or mind, is much larger than most think about.

I sure am NOT looking forward to my outside bike ride coming up in a little.

---

Dave Campbell | Facebook | @DaveECampbell | h2ofun@h2ofun.net

Boom Nutrition code 19F4Y3 $5 off 24 pack box | Bionic Runner | PowerCranks | Velotron | Spruzzamist

Lions don't lose sleep worrying about the sheep

OK fair Dan. What if we can manage to get incidents involving ambulances for us from the clubs and coaches. Dying is the extreme case. We are all afraid of dying, but the real thing to fear is a dramatic transformation of quality of life from an accident and living through that hell. If we can come up with the risk that compasses death and injury (rather than death alone), it would be awesome.

"Dying is the extreme case. We are all afraid of dying, but the real thing to fear is a dramatic transformation of quality of life from an accident and living through that hell."

i think what might be relevant from GENERAL statistics is, for every cycling death, there are x number of cycling limb breaks; y number of paralyses; z number of just accidents for which an ambulance is dispatched. fine. but we have to start with a manageable enterprise and unless you are willing to volunteer to find out how many times a triathlete has broken his finger or gotten a road rash i don't know who you think its going to find that data or how you think it could be found.

the problem with a simple survey is that people self-select what they want to answer. if put up a poll and ask how many people got their fingers stuck in a coke bottle when kids, i'll have an over-representation of people reporting yes, because people to whom that did not happen will not find the question interesting enough to answer.

also, from a simple survey of a discrete number of clubs i'm afraid we won't have enough size for statistical relevance. i'm not statistician, but if we ask x number of clubs about their club experience, how many people have died while cycling, and we have clubs responding that total 10,000 triathlete club members, if the incidence is 1 in 30,000 then i don't know that 10,000 respondents is a large enough cohort from which to draw relevance.

---

Dan Empfield
aka Slowman

Close to zero for triathletes. Using 2014 statistics:

• About 700 bicyclist fatalities per year in the nation. So if all of them were triathletes, and there are 500,000 registered with USAT, each of us has about 1,000:1 odds of dying in a given year. Do the math using realistic numbers.
• About 20% of bicycle deaths involved the bicyclist being legally drunk. So cancel that out of the stats, reducing them further
• About 20% of bicycle deaths were around dusk.
• About 70% occurred in urban areas. How much triathlon training occurs in urban areas?
• Maybe 40% of the fatalities occurred in California, Texas, or Florida. Why there? More triathletes? Or more immigrants riding to/from work during rush hours?
• About 1 in 6 riders killed were wearing a helmet. Nearly all triathletes wear helmets while riding.

I've been riding in NJ for 30 years or so, and I can recall only one triathlete fatality (and it was likely her fault) a couple of decades ago. (I swam with Chrissie, and she died about a mile from my house). Perhaps others in NJ can remind me of other fatalities among the triathlete community.


To try to be "constructive", here's one reference for how NYC derives its findings:




To describe bicyclist fatalities in New York City for the period 1996–2005, several data sources were analyzed. All known bicyclist deaths were included. The primary data source was the NYC DOT Fatality Database. This database is routinely cross-referenced and reconciled with the NYPD Accident and Investigation Squad (AIS) Database. For this study, recon- ciled deaths from 1996-2005 were then cross-referenced with death certificates maintained by DOHMH to confirm the cause of death and to identify any additional bicyclist fatalities. All fatalities with an underlying cause of death indicating the person to be a bicyclist were included, based on International Classification of Disease (ICD) codes (for a listing of these codes, please see the Technical Appendix).
Through a match with NYC death certificates, an additional 46 possible bicyclist deaths were identified. Records at the Office of the Chief Medical Examiner (OCME) for each of these possible additional deaths were reviewed by a team of three DOHMH investigators. Thirteen of the 46 possible deaths were deemed to be bicyclist fatalities occurring in NYC and were added to the NYC DOT Fatality and NYPD AIS databases (for additional details, please see the Technical Appendix).

---

----------------------------------
"Go yell at an M&M"

Generating statistics on this would be possible. Generating some meaningful numbers? That I am a lot less sure about.

The biggest issue that I see is that the risk of road cycling is wildly not uniform.

For some riders/situations, the risk of road riding is very very very low.
However, for other riders/situations, the risk of road riding is remarkably high.

I think we all want to know a number that meaningfully represents our own risk. And there are many things that highly affect our own risk. For example, if I live and ride in pretty deserted part of Idaho, I would imagine that my bike risk is very very different from a rider who starts his ride in downtown chicago.
Etc.

---

Advanced Aero TopTube Storage for Road, Gravel, & Tri...ZeroSlip & Direct-mount, made in the USA.
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--

I think you want time of day and road type and rural / urban if you're doing a survey.

So numerator and denominator and then if the sample size is large enough it can be analysed a variety of ways but time of day and location would be of value in any analysis

PM rchung about the statistical questions, and you'll have an answer in the time it take him to type it.

(I'm not a statistician, but a survey of about 1,000 respondents yields an error margin of about 3%, if done correctly)

---

----------------------------------
"Go yell at an M&M"

"Or more immigrants riding to/from work during rush hours?"

exactly!

"About 1 in 6 riders killed were wearing a helmet."

now we're talking. this is why we can't use stats that are generalized.

still, we get these threads weekly. so the odds aren't nothing. i'd like to quantify the odds, so that we can present to ourselves, to other athletes, to prospective athletes, what is the real likelihood of death from road riding.

i promise you that if you talk to a lot of triathletes this issue will come up. somebody will say that he or she does not ride on the road any longer, or is considering stopping. this is a threat to the well being of our industry, and i suspect the threat isn't cycling death, but rumors of cycling death.

but i don't think lew kidder would be responsive to this argument today, as his wife, my longtime friend, died day before yesterday from being struck by a car while riding. likewise bridget dawson's family. so we cannot blithely say you're more likely to be struck by lightning. because we knew these people, know their grieving families, i think we need to apply more care to the project.

---

Dan Empfield
aka Slowman

This is all great stuff, and the metrics - whatever they end up being - will certainly be fascinating to breakdown and ponder.

I'm no engineer, but am a bit of an analyst and it seems like overlaying all the data onto a map would be the most telling and be actionable data. For instance, when my hometown bike coalition started mapping car vs bike incidents and ranking them by severity it became very obvious which intersections and roads were the most dangerous to bikes. The city and county governments responded to the bike coalition's advocacy and as a result we now have upgraded intersections at the "death intersections" (where the probability of death was unreasonably high) and we now have colored bike lanes (the entire bike lane is filled in with green or yellow paint) and rumble strips, etc. It has been a game changer for bikes, yet we still have had numerous cyclists hit and killed in the past year.

Despite the incredible work of our local bike coalition and the responsiveness by city and county government to improve bike safety, sometimes the improvements can't be made fast enough. Bridget Dawson was struck and killed on a stretch of road that had a very wide shoulder, was well traveled by bikes, and DOES have a higher than acceptable rate of car vs bike incidents, but the road improvements haven't been made yet (still in the planning stages with the County). Perhaps if the rumble stripping or upgrading the class of the bike lane had already been done, the accident might not have occurred.

Regardless of what metrics emerge, I hope the ultimate goal would be to arm local bike advocacy groups (whether formal or informal groups) with the data so they can lobby their local governments for road safety improvements. If you generated these statistics, overlaid on a map, and said that my county's weighted rate of incidents were higher than the national average it would give us tangible reason to insist that the government focus on road safety improvements. On the flip side, if you said that my county's weighted rate of incidents were lower than the national average, or we were in the top 10 safest bike counties nation-wide, we'd have reason to celebrate and could serve as an example to other communities on how they could improve their safety. Being able to compare safety metrics before the improvements versus after the improvements is the proof that the changes/improvements worked.

It is really easy to be horrified by the accidents that get posted here regularly, especially when they happen in your own backyard and on routes you travel regularly. And it is really easy to generalize the drivers of the cars as complete morons (which they are). Hell, the lady who mowed down Bridget Dawson claimed to be praying (had her eyes closed) when she drifted into the shoulder. Now we all need to be afraid of praying drivers! We really can't change individual behaviors without constant mass communication - such as "don't pray and drive" and "don't drink and drive" and ..... on and on. That is already happening all around us. But we can try and change road conditions. And I think that is where our power is. Get us data, get us lots of advocates, and insist that the government create a safe traveling experience for bikes and cars alike.

Not to derail the topic, but risk assessment is a constant for us athletes, and especially for female athletes. Every solo run I take I am risking rape and murder. Every outdoor ride I take I am risking a car strike. Swimming is pretty safe, but there are some pool denizens that creep me out (like the underwater guy in the corner). Unless I swim in the ocean and get eaten by a shark (happened to a gal at my local beach - Avila), which is pretty deadly. I decided a long time ago that I wouldn't live in a bubble (aka pain cave) and would instead be an advocate for safety. Through that effort, I learned that the government needs data if they are going to be a partner is safety. So, yes, please more data and statistics. Rock on, Dan.

---

Hillary Trout
San Luis Obispo, CA

Your trip is short. Make the most of it.
https://www.slogoing.net/

This site could be useful, too: https://bikemaps.org . It tracks and maps collisions and problems. It can be slow to load.

LOL

---

http://www.jt10000.com/

We can be careful, and respectful. My heart goes out to the families of these incidents.

However, what you are doing is the same thing that states do to promote their lotteries. The law of large numbers allows us to make the following predictions with near certainty about the next 12 months:

• one or more triathletes will die in a bike accident; and
• one or more people will win the Power Ball lottery.

The law of large numbers also says the following, also with near certainty:

• neither of the above will be you.

We all know someone who lives near to us, or works in the same field, or to which we might have some vague connection, and who has won the lottery. Because it is a smaller cohort, we all know someone directly or through one or two connections, who was killed in a bike accident. These things bias our perception of reward/risk.


I'm one of the more ardent (or strident, depending on your point of view and my mood) supporters of riding on the road. If I were to die in a bike accident tomorrow, some here would take that as confirmation that their risk perception is accurate, when in fact it would merely be irony.


The number of hours, or miles, or riders, is so big, and the number of fatalities among triathletes is so small, as to make the risk very, very small. Throw out the deaths that were entirely preventable by the rider (riding in sketchy conditions, riding drunk, riding through intersections illegally, riding without a helmet, etc.), and the risk is made vanishingly small.

---

----------------------------------
"Go yell at an M&M"

"overlaying all the data onto a map would be the most telling and be actionable data."

when i was walking the dogs this morning, and thinking all this over, that very thought came to me as well. we already do have mapping technology on slowtwitch (go on our home page and scroll down to the "map of everything"). what we have now is just the beginning of what i want that map of everything to do. i want specific data sorts to be available to readers via a pulldown menu, so, bike fitters who have met a certain floor standard; or where triathlon bike fatalities have occurred since whatever the inception date we choose. when you click on the incident icon you get a pop up of all the relevant data about that incident.

---

Dan Empfield
aka Slowman

http://www.cityam.com/...-are-there-each-year

heat maps, hour of day, location, severity - self reporting would get you a long way. I suspect the problem is outside of urban areas the geographical spread of cyclists is so thin that its difficult to see patterns in incidents

I think this is well said. Looking at the NTSB data for 2014 (just published in 2016), the actual risk is 0.23 deaths per 100,000 people.

I'm not sure how you could use the site here to get meaningful data. Defining the actual number of rider hours/miles per year would help in knowing what the denominator is to more accurately calculate the risk, but how to do it and how to use social media/ST/internet sites?

Education about how to ride appropriately and safely is likely the best thing that can be done, IMO. The highest death rate is older males and the second risk population are adolescents. Quite a different population/skill set etc. Being a good role model/parent and wearing a helmet and making your child wear one is a good start. We're not going to change risk behavior on a website, but education is key. Safer roads would help, but we all make choices. I know I am not stopping riding outside.



Here is a link to the 2014 data on cycling fatality-cycling information starts on page 152
https://crashstats.nhtsa.dot.gov/#/DocumentTypeList/12

http://www.pedbikeinfo.org/...tsheet_crash.cfm#No1

and a nice article about 'fear mongering' related to cycling.
http://bfw.org/...g-a-bicycle-is-safe/

And simply having a helmet on reduces the risk by 50%.
http://www.bhsi.org/stats.htm

The data suggest that the actual injury rate over the past 20 years is going down and the death rate had been unchanged year over year.



Quick Facts

• Bicyclist deaths in 2001: 732
• Bicyclist deaths in 2014: 726 (NHTSA Traffic Safety Facts)
• Reduction in bicyclist deaths between 2001 and 2011: 7.5 percent
• Bicyclist injuries in 2001: 45,000
• Bicyclist injuries in 2014: 50,000 (NHTSA Traffic Safety Facts)
• Increase in bicyclist injuries between 2001 and 2014: 11.1 percent
• The total cost of bicyclist injury and death is over $4 billion per year (National Safety Council).

Highlights of US statistics available from the US Department of Transportation: Traffic Safety Facts - 2014 Data (released in June, 2016 and still the most recent). Their data has only deaths and injuries related to car crashes.

• 726 bicyclists died on US roads in 2014 (743 in 2013, 726 in 2012. 1,003 back in 1975)



• 50,000 bicyclists were injured in traffic 1n 2014, up from 48,000 in 2013 (52,000 in 2010)





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• Bicyclists 14 and under killed in 2014: 50 (7% of total. Injured: 6,000



• Bicyclist deaths represented 2.2% of all 2013 traffic fatalities. (2.3% in 2013)



• Average age of a bicyclist killed on US roads: 45 (36 in 2002)



• Males killed 87%. Males injured 83%.
  
  
• Nearly one fifth (24%) of the cyclists killed had been drinking. (Blood alcohol over .01 g/dl)



• Fatal crashes typically were urban (71%) and not at intersections (56%).
• Fatalities were about half during daylight hours.

Who is getting killed in bicycling crashes?
A detailed breakdown of the age, gender, and location of bicycle crash victims is available from The National Highway Traffic Safety Administration. Some of the more noteworthy trends and numbers are:

• The average age of bicyclists killed in crashes with motor vehicles continues to increase, climbing to 45 years old in 2014, up from 39 in 2004, 32 in 1998, and 24 in 1988.
• 88 percent of those killed were male.
• 71 percent of bicyclist fatalities occurred in urban areas.
• 20 percent of bicyclist fatalities occurred between 6 and 8:59 p.m.
• 19 percent of bicyclists killed had blood alcohol concentrations of 0.08 g/dL or higher.
• In 35 percent of the crashes, either the driver or the bicyclist had blood alcohol concentrations of 0.08 g/dL or higher.
• California (128), Florida (139), and Texas (50) lead the nation in the number of bicyclist fatalities.
• Just two states, Rhode Island and Vermont, reported no fatalities in 2014.
• 
  

Causes of injury
According to the 2012 National Survey on Bicyclist and Pedestrian Attitudes and Behaviors, nearly a third of all injuries are caused when bicyclists are struck by cars.
Six most Frequent Sources of Injury Percent Hit by car 29 Fell 17 Roadway/walkway not in good repair 13 Rider error/not paying attention 13 Crashed/collision 7 Dog ran out 4

How can bicycle-related injuries and deaths be prevented? Effective InterventionsEffective interventions to reduce injuries and fatalities to bicyclists include the following: Bicycle helmets
Bicycle helmets reduce the risk of head and brain injuries in the event of a crash.5 All bicyclists, regardless of age, can help protect themselves by wearing properly fitted bicycle helmets every time they ride.
Bicycle helmet laws for children
These laws are effective for increasing helmet use and reducing crash-related injuries and deaths among children.6
Bicycle helmet laws for adults
These laws increase helmet use among adults.6
Promising Interventions
Interventions that have shown promise for reducing injuries and fatalities to bicyclists include the following:
Active lighting and rider visibility

• Fluorescent clothing can make bicyclists visible from further away than regular clothing during the daytime.6
• Retro-reflective clothing can make bicyclists more visible at night.6
• Active lighting can include front white lights, rear red lights, or other lighting on the bicycle or bicyclist. This lighting may improve the visibility of bicyclists.6

Roadway engineering measures
Information about roadway engineering measures, like bike lanes, that can improve safety for bicyclists is available from The Pedestrian and Bicycle Information Center.



2014 NATIONAL STATISTICS-US Dept of Transportation
PedalcyclistsKilledorInjuredandFatalityandInjuryRatesper100,000Population,byAgeandSex-by cause

Failuretoyieldrightofway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
196
27.0
Failuretoobeytrafficsigns,signals,orofficer . . . . . . . . . . . . . . . . . . . . . . .
70
9.6
Notvisible(darkclothing,nolighting,etc.). . . . . . . . . . . . . . . . . . . . . . . . . .
61
8.4
Undertheinfluenceofalcohol,drugs,ormedication . . . . . . . . . . . . . . . . . .
49
6.7
Wrong-wayriding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
35
4.8
Makingimproperturn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
4.7
Dartingorrunningintoroad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
3.9
Impropercrossingofroadwayorintersection . . . . . . . . . . . . . . . . . . . . . . .
24
3.3
Operatingwithoutrequiredequipment . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
3.0
Ridingonwrongsideoftheroad . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
22
3.0
Improperorerraticlanechanging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
1.8
Failingtohavelightsonwhenrequired . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
1.5
Inattentive(talking,eating,etc.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.4
Failuretokeepinproperlaneorrunningoffroad . . . . . . . . . . . . . . . . . . . .
8
1.1
Makingimproperentryorexitfromtrafficway . . . . . . . . . . . . . . . . . . . . . . .
7
1.0


Some older data here:
Cyclist fatalities occurred more frequently in urban areas (66%), at nonintersection locations (67%), between the hours of 5 p.m. and 9 p.m. (30%), and during the months of June, July, and August (36%). (NHTSA, 2004)
89% of fatal bike crashes in NYC occurred at or within 25 feet of intersections. ("Bicyclist Fatalities and Serious Injuries in New York City", PDF, NYC government, 2005)
33% of cycling fatalies were at intersections. (Treehugger, 2009)
The most common bike-car collision is a driver failing to yield at a stop sign. (Bicycling Life, 1995)
Study shows the most common kinds of bike-car collisions. (Bicycling Life, 1995)
Riding the wrong way (against traffic) is three times as dangerous as riding the right way, and for kids, it's seven times as dangerous. (Bicycling Life, 1985-89)
Riding on the sidewalk is several more times more dangerous than riding in the street. (William Moritz, 1998) Another study says it's twice as dangerous. (Bicycling Life, 1985-89)
Most deaths on major roads. Fifty-seven percent of bicycle deaths in 1999 occurred on major roads, and 37 percent occurred on local roads. (6)
Streets with bike lanes have a significantly lower crash rate then either major or minor streets without any bicycle facilities (38 and 56% respectively). (William Moritz, 1998)
Streets with bike lanes are safer than those without. Article also has information about the safety of bike paths. (BicyclingInfo.org, 2004)
Texas leads cycling deaths. Texas ranks 14th in number of cyclist fatalities per capita. (5)
Four states lead cycling deaths. Four states (California, Florida, New York, and Texas) accounted for 43% of bicycle deaths in 1999. (6)
Austin stats. Statistics specific to Austin are listed below.


And Austin, Tx used to be a pretty dangerous place to ride. (old data)
Stats specific to Austin
A good collection of statistics regarding car-bike collisions in Austin is available on the bicycle section of the City's Safe Communities page. Here are some excerpts from their report (percentages have been rounded):

• An average of 2.4 cyclists dies each year in Austin. (From 1980-96, the fewest in any one year was 0, and the most was 7.)
• Of the 41 cyclists killed between 1980-1996, 39 were struck by motor vehicles. (>95%)
• 80% of the victims were age 39 or under.
• From 1990-96, 12 cyclists were killed, but another 1,222 were injured. Of those injured, around 15% suffered incapacitating injuries (i.e., needed help leaving the scene of the crash).
• Surprisingly, from 1994-96, nearly 78% of cyclist injuries occurred during daylight hours, which a huge chunk of the total (42%) occurring during just the four-hour period of 4-8pm.
• However, although nighttime collisions are more rare, they're also more dangerous. While only 22% of all injuries occurred at dusk or at night, 46% of fatal and incapacitating injuries did.
• From 1994-96, 57% of bicycle injuries either occurred at intersections or were intersection-related. Another 16% occurred at commercial or residential driveways. Since driveways are really a kind of intersection, ~73% occurred at some kind of intersection.
• Bicyclists were judged to be in violation of the law in 62% of crashes from 1994-96.
• Of the 180 cases in which the police noted motorist-related crash factors, citations were issued to motorists only 35% of the time. (1994-96) For example, in the 39 cases in 1998 where drivers caused or contributed to bike collisions by failing to yield the right of way, citations were issued in only 14 cases.
• While most collisions occurred on city streets, those occurring on the frontage road of I-35 or on county roads were about twice as likely to be fatal or incapacitating (probably due to the increased speed of automobiles on these roads).

Another link to helpful suggestions about avoiding different types of collisions.
http://bicyclesafe.com/


It's always about choices and education about actual risks. I feel bad when I see or hear people who have been involved in bike tradegies, and as a medical professional myself, I hear about it a lot-because people know I bike.

Seems to me there are some obvious take home messages that we should personally always do, and I'm sure most here already do these things.
Wear a helmet and make your child.
Don't drink and cycle.
Be extra cautious between 6 and 9pm on a weekend or 3-6pm on a weekday-or choose very safe roads as this appears to be a high risk time of day.
Ride in numbers-safely, following the rules of the road.
Wear bright clothing.
Ride on safe streets with bike lanes.

And do not worry about the risk as much as people would have you believe. The risk is low, but the choice is always our own.

Dale

I think that confirms what you might intuitively think.

• Morning and afternoon rush hours
• I suspect lunchtime is another factor - the run up to it and a drop between 2 and 3
• Weekends would be different
• Dusk and Dawn - riding in to or away from low sitting sun
• Lorries turning left (right in US)
• riders and drivers dont look at whats coming

http://www.rospa.com/road-safety/advice/pedal-cyclists/facts-figures/

Going back to university I vaguely remember my sampling techniques course (1999!). You can

either
- do a census, ie count the population (both deaths and number of triathlons)
--> due to limited data availability likely associates with substantial measurement error

or
- sample a subset that you can count well and then extrapolate to the whole population
---> this is what I suggest. Less cumbersome

Which subset to choose?
- professional triathletes (you should find all deaths, the total #, and have a good estimate about miles p.a. they do)
- you could also go for region maybe?
- slowtwitchers
- deaths during triathlon races
- maybe corroborating with professional cyclists as a cross check
- combining different subsets can further improve the quality of the estimate

As death is likely a rare event you need to ensure to have enough events so its not subject to randomness. I really don't have signifcance thresholds ready but a guesstimate would be a dozen or more events needed to be able to extrapolate reasonably.

Hope that contributes somehow to start to get going.

Roberto

classic heat map. john snow is your man on this.

another sad fact about stats, though, is that if you want them to be meaningful they have to be complete. the more fields you add, you more likely each observation is to be incomplete. example: i was trying to calculate death rates at a clinic i worked at. i wanted gender, age, cause of death and date of death. if a patient was missing gender on their file, i had to throw the whole thing out.

so data quality is key, and if you're going to do the map, you've got gather solid and consistent location data for all accidents . . .

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____________________________________
https://lshtm.academia.edu/MikeCallaghan

http://howtobeswiss.blogspot.ch/

Thank you klehner and dtoce - good info and perspective.

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http://www.jt10000.com/

Here's my +1

Redneck in a Dodge Ram tried to run me off the road on purpose. In a rural area.

A road near a lake on a warm summer day. Boat trailers. Lots of vehicles, usually large vehicles if they are towing, with boats. The boat trailers are often very wide. This one had its right wheels in the bike lane which forced me all the way to the right and I hit a guard rail.

Agreed about additional danger during rush hour (never ride in rush hour), and early morning / late evening with the sun on the horizon.

my thinking is to track the following fields:

activity type: in a race, in training.
nature of accident: collision with car, no car involved.

if no car involved, then the following =>
cause of crash: cyclist lost control; collision with another cyclist; collision with animal (dog, wildlife, etc.); road impairment.

if car involved, then the following =>
driver behavior: alcohol, texting, other distraction, no-fault
driver history: alcohol, license suspension, etc.
vehicle behavior: drifted into cyclist, turned into cyclist, ran stop sign or light, no-fault
nature of accident; vehicle was traveling: parallel to, perpendicular to, cyclist
time of day: morning, daytime, dusk
nature of roadway: bike had a discrete riding space, bike and vehicle shared same space
vehicle density: rural road, semi-rural road, urban road
demographics of cyclist (age, gender, etc.)
experience level of cyclist: beginner, recreational, expert
cyclist equipment: blinking rear light?

what am i missing? mind, either we can track these for substantially ALL fatalities or they will be meaningless fields. so please don't ask for fields not obtainable for every cyclist.

what i imagine doing is setting up some type of database. google doc or something. something online probably. maybe we build something custom and host it here, i don't know, we do host a lot of dbases here. then we just starting listing all the fatalities we know of, and crowdsource the effort among everybody on our own social networks, to find EVERY cycling fatality in triathlon over 3 years.

i can't imagine there's more than several dozen deaths in training and racing in triathlon in north america over 3 years. maybe more. maybe over 100. i just don't know. maybe it's hundreds. i don't think we'll know unless we begin and then see.

what do you think? achievable?

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Dan Empfield
aka Slowman

It sounds like you are looking for positive predictive value and liklihood ratios. If given a certain set of circumstances, then how likely is it the next event will actually occur. My thought is to be looking for those things that are common in accidents. From what others have reported- drinking, no helmet, urban area, riding at dawn or dusk, specific age, experience. If each of these items are present at different amounts bike deaths, then a likelihood ratio could be developed to say that if you have 3 or more risk factors you have a 1:100 (example) change of being hit and killed. This is similar to heart attacks- high blood pressure, stroke history, diabetes, high cholesterol are all risk factors that point to an increased risk. Realize that there are risk factors that we would need to identify that may not be bike related at all and just intrinsic to a person- age, sex, weight, temper, etc. In the medical field, when you are looking for the best predictor of an event happening to someone is whether they've experienced that event in the past.

"sounds like you are looking for positive predictive value and liklihood ratios"

yes. i'd like to establish a general risk. what is your risk? what is my risk? as a mean? and then look at what causes deviations from the mean, for example, what kind of road i ride? the nature of the road. when i ride THAT kind of road, and so on.

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Dan Empfield
aka Slowman

How do you define which deceased cyclists are eligible to be included this database? Someone who has competed in a triathlon in the past year? Dan, I think you meet that definition currently because I remember reading about your off-road tri, but I think I've also read that there have been years when you haven't done any races but still considered yourself a triathlete.

Also does semi-rural = suburban?

"What is the definition of a triathlete for this database? Someone who has competed in a triathlon in the past year?"

no. someone who is part of the triathlon community whether or not they've raced in the prescribed time period. i think there are roughly 500,000 triathletes in north america, but i suspect only half of them have raced in the last year. i haven't raced in about a year, but i've spent a lot of time riding my bike on the roads. if i die riding tomorrow, which i might, i should be a part of the cohort.

so, someone who:

1. has raced triathlons competitively;
2. remains part of the triathlon training/racing ecosystem.

that established, i welcome any insight as to how to identify the cohort and identify the cohort size (beyond what i already do).

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Dan Empfield
aka Slowman

Dan
I would point out that this data is pretty much collected by several agencies already for deaths, so the only unique information would be for triathletes, racing or training, that resulted in a fatality. And if an accident results in death, it would be quite challenging, if not infringing, to try to find the data needed to populate the required fields ideally. And would the families involved want information about accidents that resulted in the loss of a loved one even published on a website?

"I would point out that this data is pretty much collected by several agencies already for deaths, so the only unique information would be for triathletes, racing or training, that resulted in a fatality."

as was pointed out earlier in the thread, only 1 in 6 bicycle fatalities were wearing a helmet. does that sound like relevance for us? i would argue there are almost no stats that you and i can reliably use.

"would the families involved want information about accidents that resulted in the loss of a loved one even published on a website?"

two answers. first, you never know unless you ask, do you? second, i wouldn't assume i'd publish the details of each accident individually, rather the data in the aggregate.

some data might not be reasonably collectable. in which case, we tried, but that becomes an unreliable field. nothing ventured, nothing gained. we have other, reliable fields.

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Dan Empfield
aka Slowman

Patient identifiable information in clinical research is basically a no no

I'm not sure how a private survey sits.

I can in theory tell you that bob was killed athe 7.06am as the sun rose in Feb on an East facing road being stuck from the rear

How bobs family feels about that is not part of the process though they may not be happy

A more palatable way of doing this might be to simply have an online form that collects the data

It is aggregated up and effectively annonymised off line - an exception might be if x number of people were killed on same stretch of road ( i think the likelihood of this is low)

The data is published as a list of factors common to fatalities and serious injuries specific to the trip community

If this is what you intend to do. I apologise as I misunderstood your explanation

I would be curious to know how many are accidents involving solo cyclists and how many involved cyclists riding with others. My gut tells me i know which one is more prevalent but would like to see if the facts back that up.

"A more palatable way of doing this might be to simply have an online form that collects the data"

that's what i had in mind. but i do imagine we'll need to make public the top line, the name, so that people know whether their entry has already been made.

can you imagine an issue here? the subject is no longer living. we're not asking for medical info in any case. USAT has pretty much already done something similar, just with cardiac events. some of these people were still living, some not.

but i don't know what i don't know, and can't see what i'm blind to. hence my interest in what you all counsel.

just, i hope nobody else just posts, "there is a lot of data on this that the government already has compiled." no, there isn't, as i think we've already properly concluded if anyone reads the ENTIRE thread.

i am in touch with folks from certain bicycle advocacy groups and i do think i'll ask them about methodology.

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Dan Empfield
aka Slowman

I see 3 ways of pulling this off, and just doing a little mental gymnastics tells me that 2 of them would be just as accurate or inaccurate for different reasons.

It would be possible to manipulate the government data mathematically to get the information you need, but it would require a lot of assumptions and ball park estimates. For instance, there is data for all the accidents involving death of a cyclist and data for how many were wearing helmets (1 in 6 I believe). You could make a reasonable assumption that all triathletes wear helmets, so take 5/6 of the deaths out. But you can't assume that no one else wears a helmet, so ... Then you would have to make all sorts of other assumptions based on the knowledge of the sport. The margin of error would quickly add up to something that is outside of what you are looking for as useful.

Or you could take a poll, but that is going to create some errors as well. You would need to include enough information that repeats did not occur, and a method for someone entering the data to check for a repeat. Again, the data errors are going to add up pretty quickly, probably to the point where it would soon be worthless again.

The 3rd option, the one that is the most labor intensive but potentially the most accurate, would be to dump the [government] data in small chunks to this community and let the community sort out the data into the correct categories. For instances, the data for Long Island is going to show, if my memory serves me, 7 people killed by a vehicle in the last 24 months. I know, as do several other people on this board that live here, that none of them were Triathletes. In fact none of them were cycling for exercise. The same would hold true for just about any region in the US - everyone of them is represented by someone on this board. The problem with this approach is the government is slow in releasing data, so it would be many months behind. There is probably a better clearing house somewhere on the internet with up to date data, and someone here probably knows where it is. As long as it is recent enough, with enough detail for a local to recognize the event, your data should be pretty damn close to what you want, easily within the margin of error.

---

"...the street finds its own uses for things"

I think if a name is in the public domain. Providing that list would be fine

I think the point would be that detailing all the factors surrounding it should be annonymised and aggregated.

No one would want to see a conclusion that uncle bob headed out at dusk dressed in black riding in to on coming traffic. Whether he did something stupid or whether he, as a result of the data, is demonstrated to have increased his personal risk. No family is going to want to read it. I suspect that the info could also be used in legal cases against their families interests if it was in the public domain and there were witnesses who provide responses that at the conclusion of the analysis show the rider increased their risk.

You may want to reach out to the 'League of American Bicyclists' as they have done this in 2014 (based on data collected during 2012-2013), and found that the specific data collected and reported by the government was quite limited. Their own data collection included: time of day; land use; road type; collision type; driver data; alcohol use; source to links of information; legal status; charges. They also wrote bios of the deceased as a memorial.

They came to some interesting conclusions.

http://bikeleague.org/...ery-bicyclist-counts

But the events you are more specifically interested in, which would include all triathlete deaths, would be a much smaller number than the ~700 or so nationwide deaths/year in the US. I've read through the FARS data and NHTSA stuff (and the entire thread, FTR) and as you have noted, the data is somewhat limited. Collecting data as you are suggesting is somewhat different than the above noted 'Every Bicyclist Counts' initiative as they got the information from the internet-newpaper/TV reports and blogs. You are suggesting more direct contact and I would urge caution in dealing with grieving families.

The data that the 'Every Bicyclist Counts' initiative found was largely consistent with FARS. They suggested that national performance measures for safety need to be set and met. The most interesting information that I took away from their data collection was that the highest percentage of fatal crashes were 'hit from behind' and that high-speed urban and suburban streets with no provisions for bicyclists are over-represented locations.

They also noted that when there was an additional factor reported for the driver-3 things stood out. 42% were operating their vehicle in a careless manner, 36% were reported to have committed 'hit and runs' and 12% were under the influence of alcohol/drugs. When an additional factor was reported for the cyclist-23% were riding the wrong way, 17% failed to yield the right of way and 9% were riding on the sidewalk.

They also noted a surprising lack of information, lack of outrage over these deaths and lack of action.

But using the numbers that are known, about 700 cycling deaths/year=a risk of 2.2 deaths per 1 million people. That number has not changed over the past 20 years. The circumstances certainly may have changed but the total numbers haven't changed much.

There does appear to be a lack of uniformity of data collection and a paucity of overall data but the specific death numbers are known. The numbers do not give us reasonable quantification of time/hours/miles cycling either.

I remain interested to see what ST can do to help.

"I see 3 ways of pulling this off"

or the 4th option? the one i suggested? which seems to me the simplest most logical, straightforward way of doing it. maybe you could tackle any of your 3 options, i'll tackle mine, and we'll compare results and see if they agree. that might be powerful.

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Dan Empfield
aka Slowman

"But using the numbers that are known, about 700 cycling deaths/year=a risk of 2.2 deaths per 1 million people."

i would be willing to be the incidences of death by wingsuit are even less. this is why the denominator is important. it's not death per the population. it's death per the user population. if there are 700 cycling deaths per year, there are probably only a dozen triathlon cycling deaths per year. or fewer. this is why i think our own cohort risk should be pretty easy to estimate.

but we don't know unless we investigate.

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Dan Empfield
aka Slowman

So if USATriathlon lists 500,000 members, and there are only a dozen triathlete deaths per year, then the odds are around 40,000-to-1 that a given member will die in a cycling incident.

Is that not a sufficient estimate? I don't know that you'd be able to change the order of magnitude of that estimate, other than in the "closer to zero" direction. For instance, show that half the deaths occurred in urban zones: you change it from 1/40,000 to 1/20,000. Does that matter?

(it's like buying two lottery tickets instead of one. While your odds of winning just doubled...)

---

----------------------------------
"Go yell at an M&M"

"So if USATriathlon lists 500,000 members,"

it's got more like half that or fewer, but when you add in triathletes who just didn't buy a USAT membership or one-day (triathletes who are still training but are on a racing sabattical) i think it's more like 450,000 in the U.S.

"then the odds are around 40,000-to-1 that a given member will die in a cycling incident."

in a year. but if i race and train for 40 years then it's 1 in 1000, yes? unless i do something to make my odds better than the mean.

but i think we need to count the number of deaths, just to be sure we have a hard number and not a guesstimate.

i just got of the phone with a USAT vice president, and they just had a meeting about this. i don't think it's the wars, but the rumors of wars, that present an optics issue. we need to either solve or validate the optics by telling ourselves the truth, that this is a dangerous sport, or that it appears to be a dangerous sport but, really, isn't. then we need to figure out how to make the sport even less dangerous through best practices.

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Dan Empfield
aka Slowman

Dan, that's now how the stats work. Your probability is 1 in 40,000 per year every year. So is mine. You could go out once this year for 15 minutes, 365 times this year for 800 hours and your odds of death on any ride remain the same. It's not cumulative and year over year, it's not cumulative...it is constant every year (whatever the global number is)....in Poisson distributions the arrival rate of the next occurrence of the event is not correlated with the most recent one and your odds don't increase the more you are out there. This is the type of statistic that's "wrong place wrong time". Same deal with a plane crash.....whether you flew 100,000 miles this year or 1 when 2 people step on the next plane, their personal odds are identical to the global odds....every passenger has those odds every moment they are flying, but they don't change the more you fly.

In the age of the internet, everything SEEMS dangerous. Back in the day, you heard of every plane crash on the 3 networks. People feared flying because they heard about every plane crash in the world. Meanwhile, they never thought twice about driving their car.

In that same time in the 70's and 80's we'd just get out and ride our bikes. Once in a while someone we don't know of in Holland, or South Africa, or Nova Scotia, or Japan would get hit by a car and die. We never heard about those guys. We only heard of the very few in our immediate community that got hurt/died. It was like car accidents....you just heard about the local ones, so you never thought it was unsafe because it was exceedingly few.

But now due to the internet, bike crashes are like plane crashes in 1980. It does not matter where it happens, we hear about it instantly. It seems like there is someone dying every day, and yes, around the country or around the globe that's what is going on. But, for example in my local tri community, I am not aware of a single person who has died in a bike crash in 10 years. Probably 20, likely 30 years. I'll take a stretch and say in Ottawa Canada, a city with over 1M people with around 200 IM finishers every year and several 1000 triathletes doing races, no one gets hits and dies...EVER (someone jump in if you are aware of a single triathlete who died in this city since tri was invented as a result of getting hit). People get hit and get hurt.

The point being, triathlon did not get unsafe. From a mortality perspective its pretty darn safe. I look forward to the stats so we can prove this out and dispel the fears people may have because they see the aggregated incidents on the internet.

One more thing, I would like to apologize if earlier in this thread any of my commentary sounded mechanical and heartless. I was unaware when this thread started that one of your peers was on the wrong end of the bike-car crash and worse yet, that she's a co founder of Triathlon Today that I would eagerly read every month in the late 80's and early 90's thanks to her and Lew. Her and Lew touched more of us than they may be aware of.

"that's now how the stats work."

fine. i'm not a statistician. i'll generate the data, with your help and the help of other slowtwitchers. you do the stats.

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Dan Empfield
aka Slowman

I'm not a stats guy, but had to take enough of those courses in engineering school....the main education point is "the more you ride, your odds don't go up"....they always stay constant. That's a good thing for the education process. You can improve your personal odds within the odds by doing things better than the median, but the chance of a texting driver plowing into your back won't go up the more you ride...it's wrong place at the wrong time....but we can improve our odds over the median, by riding when there are less cars, better light, better weather etc etc. I believe this can be an important part of the USAT/ST education once the raw data is compiled....thanks for doing this. I DON'T want to see people abandoning riding out of fear. If they do that, they should abandon getting in their car to go to the hardware store too.

Explain it like I'm stats-challenged, because I am. Knowing the 1/40000 per year figure, what are the odds of getting hit and killed in a 40 year career? Is it the same probability as during a 1 year career? Intuitively, I say no, but suspect this is somewhat like the gamblers fallacy, but in reverse.

I spent a little time doing some searching.

The most notable study was by someone you've quoted back in 2012, Dr. Creswell. He looked at 109 deaths during triathlon events between 1985 and 2015 and there was an article which came out in ESPN earlier this year at/after the ACC (American College of Cardiology) meeting which I did not go to (but I am a member), however several of my colleagues went, so I heard about this earlier this year. Of these deaths, 19 happened during the bike leg, most involving crashes/trauma according to the article (the study itself is not published yet).

http://www.athletesheart.org/...m-the-acc16-meeting/
http://www.espn.com/...eed-health-screening
http://www.slowtwitch.com/...y_Released_3190.html

I don't think finding specifics about these 19 deaths during a race are what you are looking for.
Finding and describing triathlete deaths recreationally, not during a race, IS what you are looking for and that will be a challenge to get details.

I still contend that you will not get much new information to help further reduce the risk of death, other than sensible things that are already known. Generating the data could prove useful, however, but isn't known.

As a cardiologist, I'm more worried about the fact the majority of deaths during triathlon are from undiagnosed ischemic heart disease. There should be a push for better screening to find heart disease in triathletes before they die during ANY race or in training (but to be honest, in general, athletes are at much lower risk than the general population).

But you still have to remember, we are dealing with a VERY LOW RISK population cohort-triathletes during cycling in training and during races.

"As a cardiologist, I'm more worried about the fact the majority of deaths during triathlon are from undiagnosed ischemic heart disease. There should be a push for better screening to find heart disease in triathletes before they die"

http://www.slowtwitch.com/...the_Water_j3378.html
http://www.slowtwitch.com/...Screening_j3400.html
http://www.slowtwitch.com/...wim_Alive_j3374.html
http://www.slowtwitch.com/...D_in_Tri__j3382.html
http://www.slowtwitch.com/...u_to_Move_j3437.html

and as a result:

http://www.slowtwitch.com/..._Ironmans_j3599.html

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Dan Empfield
aka Slowman

Those are great articles and all relevant, even though all came out in early 2013. I did not say you weren't an advocate of screening for CAD and those are a testament to the fact you are moved to use your position here to do a 'greater good'.

Keep up the good work, Dan.

(I'm just trying to help out with data interpretation)

MadTownTRI wrote:

Explain it like I'm stats-challenged, because I am. Knowing the 1/40000 per year figure, what are the odds of getting hit and killed in a 40 year career? Is it the same probability as during a 1 year career? Intuitively, I say no, but suspect this is somewhat like the gamblers fallacy, but in reverse.

I'm not a statistician, but part of my job is data analysis sooo maybe I'll get it right? Your intuition seems correct.

Think of it this way:

You have a 20 sided die, and if you roll an 11 you're dead.

• Day one: 1/20 chance you'll roll and die. You roll a 9, whew.
• Day two: 1/20 chance you'll roll and die. You roll a 15, whew.

Tomorrow? You'll have a 1/20 chance again. But if you roll once per day over the next 10 days? That is more than a 1/20 chance of rolling an 11 when looked at as a group. I don't need to get into the statistics of what the odds are, because the only thing that is important is that when you look at the opportunity to roll 11 beyond the scope of an individual occurrence the probability increases.

So of course your odds of dying on the bike are greater if you bike 365 days per year vs. 10 trips to the store.

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Too old to go pro but doing it anyway
http://instagram.com/tgarvey4

Hello Slowman and All,

I understand that you want to reinvent the wheel here but sometimes helpful valuable information can be gleaned from the past efforts ...

Three studies that are interesting for methodology and results ... though the data input is not from a triathlete base ..

I have read this entire thread and apologies if these reports have been noted already ...

You could use some of the methodologies as a template and input the triathlete crash data.

I think is should be noted that the origins of fatal crashes data can be quite different than the origins of injury crashes data ... often because of the speed of the motorists traveling on long straight sections as compared to the speed of motorists in and near intersections ... so fatal and injury reports are often developed from different sources ..... injury crashes from hospital and EMS data and fatal crashes from police reported data .... although there is some overlap.

It would be useful if you could entice someone working on their PhD or other academic endeavor (with the influence of a University) to join the project ....... and could make getting records from hospitals/EMS or coroners (with names redacted if required) easier.

The City of Boston Cyclist Safety Report http://www.cityofboston.gov/news/uploads/16776_49_15_27.pdf is quite transparent in the methodology and show results in easy to understand color graphs .. for instance on page 62/94 .. Appendix C - Selected Maps .... all reported bicycle collisions for period 2009-2012 are depicted on the map so 'hot spots' are easily detected.

For example some of their approach is shown here:

"Approach ...

The 2013 Cyclist Crash Report is comprised of multiple sections.
The Findings section consolidates information from the BPD, EMS and Boston Bikes to provide a holistic picture of crashes in Boston.

The report then makes recommendations based on the findings.

Lastly, the document provides the original crash report prepared by the BPD and EMS. By offering the original information from each department, the reader can most accurately and fully understand the data.

Data for this report comes primarily from two sources: the BPD and EMS.
Supplementary data comes from Boston Bikes.

Boston Police Department

The BPD collects standard variables from Boston Police incident reports related to each collision, such as da te and time of occurrence, details on involved parties and their property (i.e., motor vehicles), and an open-ended description of the circumstances surrounding a collision. The majority of these variables are collected into fields that are easily transferred into the CAD database, with the exception of the narrative section, written by the police officers that respond to the scene. As such, in order to extract relevant data for this section of the report, the group reviewed and coded thousands of narrative files originating from these incident reports.

The BPD, in collaboration the Boston Area Research Initiative at Harvard’s Radcliffe Institute, the Harvard Injury Control Research Center, and the Boston Cyclists Union reviewed BPD crash data to generate baseline crash estimates and
maps for planning purposes.
•
BPD’s Office of Research and Development provided senior research analysts and interns.
•
The Radcliffe Institute and the Rappaport Institute provided funding for a graduate level research consultant to conduct bicycle and pedestrian injury research studies as a part of her PhD dissertation.
•
Harvard University’s Institute for Quantitative Social Science also provided an in kind PhD level computer scientist to assist the student with data manipulation, programming, and advanced modeling.
•
Boston Cyclists Union supplied volunteer data coders for the project."

Examples shown below with some excerpts on the most common fatal motorist/cyclist crash ...

While the Cross/Fisher report is dated (1977) ..... it is the Rosetta Stone for bicycle crash methodology.

http://ntl.bts.gov/...9/DOT-HS-803-315.pdf

Excerpt: [page 229]

CLASS D PROBLEM TYPES

Class D includes five problem types that occurred when (a) a vehicle
overtook and collided with a bicyclist traveling in the same direction, or
(b) the threat of an overtaking motor vehicle caused the bicyclist to
collide with an object that obstructed the path he would have taken if the
obstruction had not been present. Class D does not include cases in which
the bicyclist turned or swerved into the path of an overtaking motor
vehicle.

Table 36 lists the problem types and subtypes for Class D and shows
the proportion of fatal and non-fatal cases that were classified into each
problem type and subtype. It can be seen in Table 36 that Class D accounted
for nearly 38% of all fatal cases and that nearly one-fourth of all fatal
accidents were classified into Problem Type 13.

http://bikeleague.org/...EBC_report_final.pdf

Every Bicyclist Counts

It is interesting to note that this data below on rear end collisions is still similar to the findings of the Cross Fisher report of 1977.

Excerpt:

Common Collision Types

Through Every Bicyclist Counts, we were able
to find out significantly more about fatal bicycle
crashes than is publicly available in FARS. This
reinforces the idea that better data collection and
reporting is possible and should reaffirm the commitment
of NHTSA to improve FARS reporting.
In 2012, FARS experimented with providing data
based upon the Pedestrian and Bicycle Crash
Analysis Tool (PBCAT), but abandoned that effort
in the hope to resume it in 2015 or 2016 when it
reports data from 2014. The crash typology used in
our initiative does not exactly match that of PBCAT,
but it is broadly smiliar. For example, the most common collision type in
our Every Bicyclist Counts data is a rear end collision.

Approximately 40% of fatalities in our data
with reported collision types were rear end collisions.

This is higher than what was found in the
2010 FARS release that included PBCAT-based
crash types (27% of fatal crashes with reported
collision types), although the crash type “motorist
overtaking bicyclist” was the most common collision
type in that data as well.

---

Cheers, Neal

+1 mph Faster

For very low odds like this, I would just add then up. Every year you have a 1/40000 chance, so 40/40000 or 1/1000.

Dev said earlier to Dan in the thread that 1/1000 wasn't how the stats worked, though, so maybe he can clarify. To me, 1/1000 vs 1/40000 is enough to get my attention. It's basically someone in my section vs someone in the entire football stadium. Feels more proximate, though still very insignificant chance.

"Hello Slowman and All, I understand that you want to reinvent the wheel here but sometimes helpful valuable information can be gleaned from the past efforts"

you haven't read the thread, have you?

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Dan Empfield
aka Slowman

I started triathlon coming from running and I have been also scared of going out with the bike.
I live in a safe place but I always wanted to minimize the risks, training 90% of the time indoor with a powermeter. it was very effective but long term I started to love cycling. I started then cycling more and more outside, seeing bigger improvements with this type of training.
I went into Granfondo events and similar cycling races just to use them as ironman preparation.

Then, all of a sudden, one morning, alone on the road, no cars, nothing around me, straight road with cycling path....bum, 17mph full ahead against a tree. nobody knows and I don't remember why.
4 operations, shoulder, collarbone, lungs... but legs and arms were safe
now it is 3 months from the crash and i can bike and run but swimming is at the moment impossible because of shoulder operation.

What will happen now is that I will go back training 90% indoor as I was doing at beginning

I think it is a matter of minimizing the risks. if you are not into cycling racing, you can train most of the times indoor without any issue. of course, your mind needs to be ready for it.

good luck

Bit late to the thread but my thoughts:

- Suggestion to try and get data from Strava seems like a great one. If they were prepared to share it (a big if...) it would offer a pretty comprehensive insight into the riding habits of the majority of people riding for sport, which would help to quantify how risks vary by road type, region, time of day, etc
- I think a cohort of "triathletes" is too narrow. A lot (most?) triathletes will also have a road bike and take part in non-tri events, and vice versa for cyclists doing the occasional triathlon. I'm not even sure if I would define myself as a "triathlete" at the moment (have done a lot in the past, still S, B and R regularly, but haven't done an actual triathlon in a while). Seems to me that riding X miles per year on a road/TT bike in a certain area carries the same risks regardless of whether or not I'm running and swimming as well. I knew a guy who died cycling last year, he did a lot of tris but for the last year or so of his life was focused on pure TTing and wasn't swimming or running much. Would he fall into the "triathlete cohort" or not?
- Crowd-sourcing the accident data is an interesting idea. I'd look to do that but also pull in any other stats you can as well and try to cross-reference. Fatal accidents nearly always lead to a Facebook page and typically some fund-raising efforts for example.
- Fatalities is certainly going to be the easiest to establish. Life-changing damage (brain damage, paralysis, etc) shouldn't be much harder though, and arguably is at least as relevant for most of us in terms of evaluating the risk

For me I think how worthwhile the stats would be depends on how much you can break them down. Stats by country or by region are interesting in terms of understanding the impact of cycling policy and infrastructure on risk, but it's not going to mean much for individuals. I'm certainly not going to convince the wife that we move to a different country or even region in order to reduce my chances of being killed while cycling from 1 in 15,000 to 1 in 20,000. If you can start breaking them down by road type and type of day that gets interesting though. E.g. if my commute (done at rush hour on fairly urban roads) was 5x riskier than my weekend rides (done early in the morning in light traffic on fairly rural roads) that would really make me start to evaluate whether that was a smart choice.

Most interesting of all for me, but probably hardest to establish, is cause of accident. When I see the stats on cycling deaths, I like to reassure myself (and my wife) by comparing my riding to some of the people I see out on the roads who are flying through red lights, undertaking moving traffic at junctions, etc. Would be great to have numbers to back up how much (or little) we can actually mitigate risks through our own behaviour vs how many deaths are completely out of the cyclist's control.

I get where you are headed, Dan. And I agree - we are a different subset of riders than those collected and tracked through big agencies - the ones who aren't wearing helmets, are on BMX bikes, riding to the store, or whatever. We are a very small slice of the total population and if we figure there are 500,000 (or 450k or whatever) triathletes, how many of them are laying down the hours on the road (versus inside or inactive or ??)? In your data collection I'd wonder if you could collect average hours/week victim cyclist spent riding outdoors. This would be very telling in how much exposure the individual had.

Also, regarding "cyclist equipment: blinking rear light? " you might broaden it to be "safety equipment in use" because if they are hit from the front, did they have a front-flashing or steady light? Any reflectors? Was their jersey neon green? My husband looks like a neon christmas tree while I only have a rear flasher any my jersey is whatever I pulled out of my drawer.

I'm most curious about gender of rider, which you have listed. Nationwide, it is 88% or something that are male, which leads me to believe that as a female I have less probability, yet two recent deaths were women. I'm not sure the % of triathletes that are women, but I wonder if the national average of 88% male would hold in our population.

I think that if you are successful in collecting this data, overlaying it on a map, and comparing it to total tri population you would get very different statistics than the national stats. On the total tri population you could probably extrapolate number of active athletes by extrapolating race data and applying some assumptions on amount of training time might be included. It would be a lot of work, but maybe an engineer could data mine that for you. And perhaps some polling to get within an acceptable standard deviation of assumption parameters.

Also, it would be very interesting to see the risk exposure on hours on road versus total triathlete road hours. For instance, if of 500,000 athletes, 300,000 are actively training through the year, and each athlete trains 16 weeks at an average of 6 hours/week and half of that is outdoors, there are 14.4 million road hours of exposure. Obviously, the extrapolation would be more complex and hopefully scientifically derived. I'm actually not sure where to take that data point, but perhaps it would be "athletes training 100 hours for a race of which 50% of the time is on the road risk death by 2%" or something. I dunno. Just spitting stuff out here... but your project is intriguing and I think it has merit to evaluate our unique population and our exposure to death or injury.

The information is out there.... just need to go get it.

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Hillary Trout
San Luis Obispo, CA

Your trip is short. Make the most of it.
https://www.slogoing.net/

"I think a cohort of "triathletes" is too narrow."

i agree. but are you tied into all the road racing cyclists in your state? the issue here is getting reliable, comprehensive stats.

"Crowd-sourcing the accident data is an interesting idea. I'd look to do that but also pull in any other stats you can as well and try to cross-reference."

how? if you use general stats on bicycle fatalities, only 1 in 6 wore a helmet. does this sound like your cohort? so, what accident data are you referring to?

so, again, and again, and again, what we need are:

1. stats that are RELIABLE.
2. stats that are RELEVANT.

if you can help me figure out a way to broaden our cohort while not wrapping our arms around people on bicycles who are not really relevant to our style of riding, i'm all for it. open to suggestions.

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Dan Empfield
aka Slowman

I'm as tied into the road racing cyclists in my area as I am the triathletes. If anything I might be more tied in in terms of being aware of any serious accidents, since road cyclists who race are pretty much all members of a club and go out on group rides, whereas triathletes are more likely to train solo. There are 6 cycling clubs in my immediate locality that I can think of, most have some sort of triathlon wing or at least a subset of riders who also do tris, and there's also a fair bit of cross-fertilisation with riders who go out on rides with multiple clubs to suit their schedule. So although I only do rides with one of those clubs I'd be confident that I'd hear about any serious accidents involving any rider from any of those clubs. You obviously have a lot more data on the make-up of your membership than I do, but from the posts I see I'd be surprised if this forum didn't have very good ties into the road racing community. If you're prepared to crowd-source, you could also consider crowd-sourcing through other sites, though I think cycling sites are a bit more numerous and fragmented, I can't think of one that has the same pre-eminence that ST has in the triathlon community.

You can use the general stats as a sense check to gauge the reliability and coverage of the numbers you're getting from your crowd-sourcing. The 1 in 6 wearing helmets you quote is a great example, I'd agree that anybody who races even occasionally in any kind of cycling event is almost certainly going to be wearing a helmet most of the time, so straight away you've established an upper limit for how many fatalities you'd expect to uncover. It's also why I thought the suggestion of Strava was a great one for establishing what the denominator is in terms of your stats. Certainly from the people I know, a heavy majority of those who I would consider to be in "my cohort" are on Strava (i.e. they either race or are at least treat cycling as a sport not as a convenient means of transport or family activity), and equally people who only use their bikes for popping to the local shops or going out with the kids are almost certainly not on Strava.

If you want to be more rigorous about it, I think this is correct. The odds of not dying are (39,999/40,000)^40, or .9990. Which is 1/1000, 0.1% chance of dying. Same result as adding the yearly chance. So like I said, for low odds like this you can just add them.

If the yearly odds of dying were 1/400, then it would be (399/400)^40, or .9047, 9.53% chance of dying vs 10% chance if you just did 40 x the yearly chance.

I don't believe that stat, but if you did, you could simply say that 5 out 6 fatalities do *not* have anything to do with our cohort. Which leaves a potential of a little over 100 fatalities per year in the US which do. And a large percentage of those get posted on ST.

It's tough to do what you are wanting, since no one is collecting the data at the basic level where it needs to happen. Which is why I just look at the agencies that publish WAGs on rider hours and miles along with the fatality stats. Even if you assume everyone is in your cohort, the odds are very low, like 1 per 5M hours. Actually lower than auto travel! IMO that should be good enough to satisfy anyone who has the perception that cycling is too dangerous, since no one gives a 2nd thought about riding in a car. And if you ride a lot your whole life it comes out to under 1% chance, out of a 100% chance you will die of something. You could go to a lot of effort to show that it is really a bit higher or lower for our cohort, but I don't know that anyone cares enough. Would be cool to know, but it's like a major research project and a lot of approximations will still need to be made.

Isn't it the same way that you'll find out about triathlete deaths? We read (or have read) about a cycling fatality, do some research online to find out if the cyclist was "like us" and let you know/add to database. I read today about a cyclist killed while cycling to work last week in the town where I grew up. Looks like he died from brain trauma. Happened early in the morning and could have been a hit and run. I found a couple of articles about the accident, found out via a search that he was registered for the current National Bike Challenge and had done a couple of 100+ mile days in the past two months. I doubt he has ever done a triathlon but I'm guessing he paid as much attention to safety as the average triathlete. I could see from photos that he had a mirror on his bike. It looks like he did most of his recreational riding on multiuse trails, so he might not have been wearing a helmet during his final ride. That's the only question I have at this point whether he was a cohort of mine, since I also commute to work via bike.

Hello cartsman and All,

You bring up and interesting point .....

Cyclists that ride like triathletes (often not in a group) should be included in the database since their risk is not very different than a triathlete considering road position .... although a cyclist riding a road bike would seem to have more situational awareness (especially to the rear) and maneuverability than a triathlete in the aero bars .... and triathletes would be a less visible to a motorist by virtue of their position.

If we sent out 500 robot cycles that rode in the same fashion as triathletes they would be included also .... they could put in 24 hours days ..... reporting their position with near misses and collisions so we would soon have realtime streaming data.

Whether the robots wore helmets or not would only be useful if it indicated a certain style of riding .... as a vehicular cyclist riding in the track of the motorist's left wheel or as a cyclist riding far to the right of the travel lane .... or some other style of riding .... and also indicated a style of parsing intersections.

It seems likely that cyclists wearing helmets are more generally safety conscious than than those that do not wear helmets.

Similarly ..... studies indicate female cyclists are more safety conscious than male cyclists .... female mode share increasing with improved infrastructure and less perceived risk .... canaries in the coal mine.

While getting information on triathlete cycling risk would be desirable .....

....... it would seem more to the point to determine what countermeasures influenced risk reduction the most ..... so we could adopt the safest style of riding ..... regardless of risk because we are probably going to ride anyway ....


I doubt many triathletes will adapt to a changed style of riding (indoor vs. outdoor) very quickly based on a study of calculated risk assessment .... as opposed to changing their outdoor style of riding based using counter measures that increase safety and reduce perceived risk.

The recent use of high powered lights to improve cyclist visibility is a good example of a type of countermeasure to increase cyclist safety and reduce perceived risk.

https://pvcycling.wordpress.com/...28/lighten-up-dummy/

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Cheers, Neal

+1 mph Faster

That's your cumulative odds for a 40 year period, but you make the assumption that there is correlation year over year to run a cumulative probability scenario....but there is no year over year correlation thus there is no cumulative probability to be calculated.

In a given year it's still 1/40000. If you went out last year and did not die, your odds at the start of this year at 1/40000. Did not die after 39 years? Well starting this year, your odds are 1/40000. Why? Because the guy who started riding this year and you riding 39 years without dying experience the same odds/shit out on the road TODAY. These odds have nothing to do with what happened over the last 39 years. Do you see the difference.

Let's put it another way. How does the texting driver about to hit a rider know if it is me, riding in my 35th year without dying, slowman riding his 40th year without dying or my newbie tri friend that I just put on a bike for the first time this year? If anything, I bet the 40 year rider has better odds than the newbie, in the sense that his spider senses are battlefield sharpened, but the average odds are the same.....they have nothing to do with what happened last year or what happened to the other guy down the street or the other state. It's a national level yearly view.

I am assuming that the odds are 1/40,000 each year. Given that, what are the odds over a 40 year period? That's what we want to know.

Who cares about the probability over 40 years? It is meaningless at this moment in time THIS YEAR. It does not keep going up as time moves on. As I said, slowman needs to know the risk on THIS ride this year year. Its the same for him and the newbie going out on his first ride today. What slowman did for the last 39 does not increase his risk today more than the newbie.

What are the odds that a single fair coin flip turns up heads?
What are the odds that 40 fair coin flips in a row turn up all heads?

What are the odds of not dying while riding a bike in a single year?
What are the odds of not dying while riding a bike 40 years in a row?

Bike riding in a given year, like a single fair coin flip, is an independent event, but one can still calculate the probability of a sequence of independent events. That is different than changing the odds from one year to the next based on each year's outcome.

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----------------------------------
"Go yell at an M&M"

Wut?! I want to know the odds that cycling will be the thing that kills me during my potential lifespan. The odds of me dying this year are a hell of a lot less than the odds of me dying during a 40 year period. Actually 40 times less. It seems like you are saying that the two are the same. Which is nuts.

Is there a difference in risk and odds?

Say I have a 1/400 risk of being bitten by a snark every time I swim in the ocean. It only makes sense that my risk on a particular day are only 1/400. But if I never get in the water my odds are zero. But, if I go every day my odds will go up. However, the risk is always the same?

Just trying to understand.

How did your odds go up if you go every single day? They are 1/400 on any given day. You just expose yourself more times like playing Russian roulette which is 1/6 but you could spin the revolver and shoot at your head 10,000 times and actually live.

You could go one day and on that day it is 1/400....skip 398 days and it is 1/400, go the 398 days in between and it was 1/400 each day. You could go on of a million days and you did not increase the risk. It's 1/400 on the millionth day and even though you went out many multiples of 400, you magically did not get bitten. Some other poor suckers got bitten, but you did not, but one in 400 people are getting bitten on that trip to the ocean....you just got off the hook every time and every time you go to the ocean things get reset to zero so today it is 1/400 AGAIN, not 1/399, then 1/398, down to 1/5, 1/4,1/3, 1/2 to the last one where you number is up....that's the beauty....your number is never up. It gets reset on every outing.

You can take the risk to zero for sure. Never do the activity. But once you do it, it's 1/400 (or whatever that denominator is). If that was not the case, you absolutely would not want Gold card fliers on your airplane. Those guys flying 100K-250K miles per year, their presence MUST increase the chance of your plane crashing....and we know that is not the case!

let's take Russian roulette or rolling a die. Let say we want to roll a 1 with a 6 sided die. Roll one is 1/6, roll two is 1/6, roll three is 1/6 but as I continue to roll the die the chance that I will roll a 1 goes up. I'd bet a lot of money that you couldn't roll a die 100 times and not roll a 1. Is it possible sure. But, if you rolled 99 times and no one had appeared the chance of a one on 100th roll is still 1/6. I get that. The chance of rolling a specific number in a number of rolls is =1-(5/6)^n where n=number of rolls.
N=1 16%
N=2 30%
N=3 40%
N=4 50%
N=5 59%
N=6 65%
N=1000 99.99999999999999999999999999999999999999999999999999999999999999999999999 %(72 places)
N=10,000 100%

But, you seem to be saying that your exposure doesn't mean anything. That exposure has to mean something. I'm not saying it drastically effects the odds/chance/probability of something happening but it has to change it in some way.

I think micro data can be particularly misleading. macro data is more meaningful I think.

I haven't seen for a while, but here in NZ the government publishes some data on cycling. As with any data collection, there will be errors but they collect data about cycling in the same-ish way they collect data about cars. I paraphrase below.

Number of registered cars on the road, distance travelled per year (note that in NZ we get a warrant of fitness check every year (used to be 6 months) to ensure our cars are safe. Odometer reading is one of the things that's checked, particularly for diesel vehicles that pay road user charges based on distance travelled). They get reasonably accurate number of cars on the road and a believable average distance per vehicle per year. They then use the reported deaths and injuries per year and hey presto, there's a number that represents your chance of death (or injury) per km travelled.

For bikes, they use bike sales per year, estimated bikes owned to get how many people have bikes. They have people with clipboards at various places around the city and country, counting bikes. Combined with online surveys re bike use, they get a reasonable idea about cycle use per year. Again, they look at reports of deaths and injuries each year and again, a risk per km is determined.

From memory, last I heard was about 8-10 years ago. While cycling has increased a lot in NZ since then, I suspect the rate of injury and death is something like the same. The last stats I saw showed that the accident and death rate was very close to identical for cars and bikes. Of course this is completely opposite to peoples perceptions (particularly the nervous mothers of precious children).

I remember showing the article to a friend. She had never ridden a bike as a child (she was legally blind) but thanks to an operation, saw well enough to take up cycling in her mid 40's. She eventually got good enough to ride the "Round Taupo" century ride with 12,000 other mad keen cyclists. She eventually gave up cycling because of her perception of the dangers. While she read the government statistics and agreed on their accuracy, she nodded and agreed right up to the time she said, "But it's still too dangerous to ride a bike". Even with clear evidence that cycling here was no more dangerous than driving her car, she still perceived the danger more on a bike. Insane in my book.

I think Dan, relying on anecdotal reporting, even from a fairly large group like ST, wouldn't be particularly valid.

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TriDork

"Happiness is a myth. All you can hope for is to get laid once in a while, drunk once in a while and to eat chocolate every day"

"I think Dan, relying on anecdotal reporting, even from a fairly large group like ST, wouldn't be particularly valid."

i agree. and that's why we're not going to be doing that. and we never were going to be doing that. it was never contemplated.

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Dan Empfield
aka Slowman

This is what you guys are missing. We're not trying to calculate the mythical odds over a 40, 50 or 60 year cycling career of a cyclist. We're trying to figure out the risk today. In the vein, it's like rolling the dice today. It does not matter how many rolls happened over say the last 40 years. All we care about is if Dan does out to ride tomorrow, what are his odds tomorrow. There the same as your odds, my odds, the newbies odds, assuming we all did the same ride in the same place at the same time. The past history has zero impact on the current odds for tomorrow's ride. We're trying to get to the point of helping people decide, "Is my ride tomorrow or this weekend to train for USAT nationals or Kona safe, or do I take it to the trainer". We're not trying to help them decide the cumulative risk over 40 years. Just a single year. If you want to distill it down to the daily probability and then apply cumulative daily exposures, you'll actually find your odds get BETTER than the annual average since you are not riding every day. It's not going to get higher than the yearly average over an entire year, because by definition, almost everyone is riding less day in a year than there are days in a year.

In that case Dan, getting hold of data that already exists, from each State, would be the first avenue to pursue. DMV?

Doing any new data collection is out of the question. The trick would be normalizing the information so valid comparisons from state to state could be made. I'd also suggest that having a baseline to compare with, would be good. Comparing death and injury rates with cars, would be the obvious choice Also, adding in seat belt and non-seatbelt information in cars would be good as well (if it exists).

Is cycling safer than driving in a car or more dangerous? (the higher speed of motoring may make driving more dangerous). Is cycling more dangerous that wearing a seatbelt but less dangerous that not wearing a seatbelt.

I remember years ago, when undergoing asbestos training, that the stats on the screen looked pretty scary and we were all mortified that anyone would go anywhere near asbestos based on the cancer rates. Then the instructor pointed out that even unprotected asbestos workers got cancer at lower rates than smokers, did we all realize it was actually pretty safe to deal with asbestos. Similar to comments I made in a previous response, perception is not a very good predictor of fact. But I guess that's why you're trying to collect the cycling data in the first place.

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TriDork

"Happiness is a myth. All you can hope for is to get laid once in a while, drunk once in a while and to eat chocolate every day"

agreed that each day is a new day and the odds/chance are not affected by the past or the future.

But, how can you not agree that the more you increase your exposure to risk the more likely it will happen.

Just as you said someone could pull the trigger 10,000 times in a game of life & death and never once die. Each of those 10,000 pulls of the trigger have the same 1/6 chance. But, when you look at it over time the chances of hitting the bullet approach 100%

The more you expose yourself to the odds the better the probability of them happening. Whether it's airplanes, Sharks, lottery, or dice. That is a fact you cannot refute.

How this data is compiled and used has no bearing on that.

Can confirm.
Source: work at insurance company

- collect statewide crash data and mine for cycling related accidents. Some states do not file a report if no injury occurred, so these may trend toward more sever accidents. Also assume that all bike/car accidents are under reported.
- collect census data of population by block or block group or place (some scale that is small enough to discern between high and low population density).
- many municipalities collect transportation mode share data via survey. You can use this to estimate the number of cycling trips made per year as well as the # of those that are recreational v. commute trips.
- Strava Metro data may be of use here as well since their data collection trends towards recreational trips. This can be used to gain a sense of miles travelled per year by location.
- aggregate the data by zip code, or some geographic reference, so that crash data can be compared against local population density.
- cross tab the data to figure out what the likelihood of an accident is based on location, population, bike mode share, accidents per bike trips.

There are probably a bunch of other steps that I am omitting. And as others have posted, this data will give you results that are intensely local in nature.

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Jon Ryder
RPM Coaching