I’ve often been curious about transit levels in the United States, especially cycling. Commuting by bicycle has been dropping since 2014 in the U.S. There are lots of theories about why that is.
One question I’ve asked fellow cycling advocates in Carrboro: what is the highest level of cycling ridership we could possibly hope for in the next, say, 10 years? Assume we could implement our entire bike plan overnight. How would you even go about answering this question?
It’s hard for people to even venture a guess. Here’s how I would do it. Find the best currently available data on ridership in the U.S. and look for cities with the highest cycling numbers. Narrow that list to ones that have similar characteristics with an emphasis on qualities that affect ridership: number of workers, cycling infrastructure, climate (average number of rainy days, average temperature), “hilliness,” proximity to local areas of employment, college towns, average income – there are many factors. Use the cities at the top of this list as a guide – how much higher is the ridership there? How much of the difference can we attribute to each factor? Is it too hilly in your town to ever really get much cycling ridership?
I was surprised that there was not a convenient way to do this already, so I created my own. I realize data from the American Community Survey isn’t perfect, but it’s way better than anything else we have and it’s what the League of American Bicyclists uses to determine usage (in fact, it’s the only factor they use to determine actual “usage” on their application for Bike Friendly Community rankings.)
Some things I’d like to add:
A hilliness factor – how many hills does each town have?
Climate related number – average temp, rainfall, snow, etc.
Average commute times
Average incomes
Average age
If you’d like to contribute to the project in some way or have ideas on adding hilliness or climate numbers, I’d love to hear from you. Contact me on Twitter.
Here’s a good write up of Trade-Ideas SCoRe or Stock Composite Rating. It blends our technical, fundamental, and non-structured data into a nice metric that can be used to assess a stock’s overall strength. Here’s the full description from Andy Lindloff who spearheaded the effort to create the metric.
The Trade Ideas’ SCoRe (Stock Composite Rating) is a proprietary formula that measures a stock’s strength along technical, fundamental, and non-structured dimensions. We employ over 17 such metrics to measure each SCoRe. A stock can have a SCoRe of 30 to 100, with 100 being the best score. Unlike the other rating systems, the SCoRe is dynamic, and will change in real-time during live market hours. This allows our clients to act swiftly to quickly changing price action. ScoRe incorporates both Technical and Fundamental metrics, with an emphasis on moving averages and the position in range on multiple time frames.
The Top-List above only looks for stocks with a SCoRe greater than 90. It has identified a stock that may be on the verge of breaking out to an all-time high. iRobot Corp. (IRBT) has a SCore of 98. They recently had earnings and the stock exploded to within cents of it’s all-time high set back in August of last year.
With a huge short float (42%), a new all-time high above $118.75 could really get this thing going as many shorts will be scrambling to cover. The stock price has made a big move since earnings, so a pullback would not be a surprise and some may wish to use that weakness to enter a position at or near the rising 10 day SMA, should it get there.
One of the most frustrating things that can occur as a trader is when you miss a profitable trade. You’ve gone through countless hours of diligent work and research to create and refine a trading strategy, you’ve paper traded it, you’ve double checked that you crossed your Ts and dotted your Is, and maybe even weathered a drawdown. Finally another trade setup comes along and — dang! — you miss it by either being a moment too late or, even worse, you have your entry order in place and you don’t get filled and the price blows by.
The price is now well into what would have been profitable territory had you gotten in the trade, but now you’re left sitting on the sidelines wondering what might have been.
So you missed the original trade, but now the price has pulled back offering you another shot. Do you take it? Here’s a hypothetical but very realistic example in SYY from 2/4/2019.
SSY on 2/4/2019
There are two conflicting ideas in this scenario.
You are being offered a second chance to take the trade at the original or perhaps even a better price.
However, if you take this trade you are guaranteed to take 100% of the trades that end up hitting your stop as this is exactly the behavior of trades that stop out.
These two ways of thinking about this scenario are of course in stark contrast to each other. What is the best way to think about this scenario so that you have a plan when this all too common situation arises? I see two answers to this question, one very simple and one more complicated but complete answer.
The Simple and Perfectly Valid Answer
Your default answer should be this: if you hadn’t missed the original entry, is there any reason you wouldn’t still be in this trade? If not, then you should take the second entry — after all, this is literally a second chance to execute your original plan for this trade.
This is a perfectly valid answer that gives you a stress free way to respond to the situation. But…
The Difficult but More Rewarding Answer
The real crux of the situation is this: even if you establish that you would still be in the trade if you took the original entry, you now have more information than you had prior. Has the stock exhibited any behavior that makes it more likely to end up a profitable or losing trade? If so, that should play heavily into your decision about whether to take this second entry.
This leads us to a discussion of my favorite and commonly neglected trading topic: post entry trade analysis. Every trader spends a lot of time thinking about pre-entry analysis, but very few spend the time required for the more rewarding topic: what happens after you enter your trades.
Post Trade Analysis — Where to Begin
To start making a plan for our situation in SYY, we need to figure out where to focus. The first thing to do is review the charts for the trades you’ve taken. Focus on the trades that went in your direction initially like SYY. Separate these charts into profitable trades and losing trades. Is there some behavior that some of the winning trades exhibited that some of the losing trades didn’t? If so, try to formulate a rule and keep score using a spreadsheet for as many previous trades as you can. As with any sort of analysis it’s the aggregate that counts — any one particular trade in an of itself is meaningless.
This can be especially difficult since these are trades that you’ve taken and probably have some memory of (hindsight bias). Try to leave this emotional baggage at the door. As much as possible take on the role of a skeptical, neutral observer.
If you find a characteristic that shows promise it can have a dramatic effect on your results. We’re only discussing this missed trade scenario, but think about what this implies. If you can determine a statistically sound rule to use for this scenario, you can also apply it to different ones. For example, if you’re already in a trade that exhibits profitable behavior and then pulls back as SYY did, you can use this information to scale in and add to your position. Conversely if it shows negative behavior you could exit your existing position at breakeven and avoid a loss entirely. This is hugely powerful information that can substantially raise your system’s profitability — and not by adding more trades, but by simply altering the trades you’re already making! As I alluded to in my post on scaling into trades, this is very difficult by has the potential to be highly rewarding.
Do you use some feature in your trading platform that seems so intuitively and obviously useful to you but hardly anyone knows it exists? I feel this way about what I refer to as “time stops” (a.k.a. Good After Time orders) in Interactive Brokers’ trading platform called TWS.
It’s a very simple property called the Good After Time or Start Time that you can set on any order. When set, the order is held and not activated until the current time is past the Start Time.
Why is this Useful?
Any trade you make should have a “lifetime.” That is, what is the plan if you enter a trade and your stop price and your target price (if you have one) don’t get reached? Do you leave the position open forever? No — at some point another profitable opportunity will come along that you’ll need capital to take. This part of the trading plan is important and often overlooked. If you don’t have a predetermined plan for this trade lifetime then you’re in danger of turning “trades” into “investments” and falling victim to the endowment effect — the psychological fallacy common to all humans but especially traders where you value something more than you should simply because you own it.
By placing a Start Time on a market order to exit your existing position you do two things:
Fully execute your trading plan by committing to a timed exit.
Automate your trading plan knowing you don’t have to remember to exit at a certain point in time.
Be Prepared for the Rare but Potentially Expensive Scenario
Number two is important — have you ever had a power outage or network outage (or computer glitch) at the exact moment when you’ve needed to place a trade? If you haven’t yet it’s just a matter of time before you do. This order allows you to rest easy knowing that your order will go live according to your plan whether you happen to be online or not.
This is in fact the best part about this order type. If you manually exit your trades at the end of the day, some part of your brain is wasting energy remembering to take this rote action. A time stop frees up that mental energy to do something else.
I’m always surprised how few traders know about and use this order type when it has become such an important part of my routine for almost a decade. I could not trade without it.
I came across the tweet from Mike Bellafiore the other day describing a typically very frustrating trading experience that I’m sure will resonate with a lot of readers.
So you make a trade with all your risk parameters set and you get stopped out, only to see the stock move strongly in your direction. When the dust settles, you stare at a beautiful chart — a quintessential move that is right up your alley — and you found a way to take a loss on the trade. This situation is not that uncommon if you’ve been trading for any length of time and, man it can be frustrating. Here’s a good example from Friday in KBH:
The article describes the situation as if the trader was fundamentally unprepared for this particular trading situation and how he should have done a little more homework to handle the trade differently once he was stopped out. I agree you should absolutely do as much as you can to prepare for the trading day well before the opening bell rings.
Being Unprepared Isn’t The Reason
The important thing to remember though is that you can do all the preparation in the world and this situation is guaranteed to still happen. It would be statistically surprising if this DIDN’T happen pretty routinely.
The proper takeaway I’ve learned over the years is that a well functioning trading plan will necessarily have these trades occur with regularity. In fact, it’s actually a positive thing that they occur — your general thesis is sound and your stop was placed almost perfectly.
Any Single Trade Is Irrelevant
Remember: any single trade is unimportant to the success of your trading system. If one trade can cause this type of meltdown, then you either need to trade with smaller size or redesign your trading system.
Don’t let this situation frustrate you. Handle it like a pro — recognize it and make a mental note. If it happens a lot, go back and backtest using a looser stop and see if there’s improvement. Only make changes if you can prove to yourself that an alteration will be more optimal over a large number of trades.
I’ve always thought that we could do a lot better displaying XC race team results. The cold, dry text format that we’re all used to is nice for a quick score, but pretty bad for most anything else. Just how did our fifth runner stack up against everyone else’s? Sure, you can scan the column and compare a couple teams at a time, but there should be a better way.
Another thing that should be easily visualized is the differences in the scores. Let’s say we were behind a team by 10 points. Is that a lot? In a dual meet it’s not really that close but in the state meet or a big invitational that’s a razor thin margin!
Here’s my attempt at making cross country team results way more fun to look at. These are the results from the North Carolina XC State meets from November 3rd, 2018. Each column represents a team. Each colored section in the column represents the points for one of the five scoring runners. The total column height is the team score for the team. Hover your mouse over the columns to see a popup that shows the runner’s name, place, and time.
Take a look and let me know what you think. How would you improve it? Any reason Milesplit shouldn’t do this? If you like this, follow me on Twitter.
Since we just got done with the state meet yesterday, I wanted to post some details about the app I created a few years ago and the companion web site. I don’t have any plans on making this available to others, but maybe it will give someone some ideas to do some better things with a timing app.
The Problem I’m Trying to Solve
My wife Joan is the head coach of Chapel Hill High (I’m the assistant). Although I’m the “data guy,” Joan is simply amazing at remembering all the PRs and times of the entirety of both the boy’s and girl’s teams. As soon as the runners finish a race she instantly knows if they ran well or not with high accuracy. I quickly realized that there was no way I could remember the PRs for 80 kids each season. I knew I needed a technological solution.
Here’s a list of things that I wanted to be able to do:
Be able to record the finishing times of each runner on the team as they cross the line without having to write anything down on a clipboard.
Easily keep track of mile splits during a XC race for each runner on the team.
Tell me at a glance how well the runner ran compared to their personal best and their season best.
Keep track of the big, notable workouts we do each year to be able to compare runners’ progress across years against themselves and against runners that graduated seasons ago.
My Approach
The Web Site
I created a web site using PHP and a database that can store times, splits, and rosters from races and workouts. You can do all sorts of queries and reports to compare 5k results across the program’s history or see a particular workout report that shows athlete’s improvements over time.
This was quite an effort but not because of the coding as much as collecting as much race history as we could from Mile Split and our archives. Of course this is mostly a manual effort and Mile Split doesn’t make it easy since they only allow you to show one race at a time instead of being able to download your team’s entire history. I made this request to them but they were not interested in allowing this.
My Android Timing App
The web site is pretty nice, but by itself it’s not particularly unique. In my mind the real convenience is the Android app that I created to integrate with the web site for efficiently timing the actual races. I call it Coaching Timer (such an amazing name I realize!) and here’s some screenshots to show how it works.
Timing the Race
Here’s a screenshot of the main timer view. You start the timer with either volume up or down button. The 04:45 is the current elapsed race time.
The main timer view for a race
As they come through the mile split, I press the button for that particular runner as they cross the mile mark. The runners are in descending order by their PR (more on how this happens later – it’s easy!) When I press a runner’s button, that button goes to the bottom of the list of buttons. See this screenshot that shows Ben Hawley and Owen Rogers having dropped to the bottom of the list. Why is this important? It minimizes the hunting I have to do to find the name as someone crosses the mile mark. It also allows me to quickly determine if every team member has crossed the mile mark. Also, if I end up having to click a button out of order that’s a mild indication that someone might be having a good day or a bad day.
As I click each button the timestamp is recorded along with the name in the left hand column.
Ben and Owen just passed the mile mark
As They Finish
I record the 2 mile splits similarly, but then for the finish the app sees that it’s within a normal range of a 5k finish and it adds some important details to the timestamp.
PR and SB comparison added to timestamp
In the above screenshot, Amelia Maughan, Lilly Crook, and Sydney Runkle just crossed the finish line and I clicked their buttons (which moved to the bottom of the list, remember.) In this case, though, their PR and SB (season best) data was added to the timestamp. I can quickly see that Amelia tied her PR, Lilly was 20 seconds off a PR and 10 seconds off a season best, and Sydney beat her PR by 6 seconds.
Send the Results
In the 3 dot menu on the top right of the app, there are several options, two of which allow you to send results.
The Email Times and Text Times menu items let’s you send the results to another app for sending. Email Times brings up the following email message ready to send. Text Times let’s you send them via text message.
Sending results via email
So How Does The App Know the PRs?
Here’s where some magic happens that I really like. You create groups in the app for each race that’s happening – for example, Boys Varsity, Boys JV, Boys JJV, etc. When you initially start a timer you choose the group to use for the race.
I initially created the groups by hand – typing in each runner’s name individually. I realized quickly how much better it could be, so I added an Import function for creating the groups.
The import function pulls a list of runners from the web site. It stores three pieces of data for each runner: their name, their PR, and their season best. The runners are listed in descending order by PR which lines them up just right for selecting varsity, JV, and JJV.
Managing groups and the Import function
Just check the box for each runner to include in this group.
List of runners in descending order by PR
Then to start a timer for a race, just choose the appropriate group and click Start Timer:
Additional Nice Features that I’ve Added
Phone Sleep Prevention: As I’ve used the app over the years I’ve added some features to improve it. When waiting for the start, sometimes the phone would time out and I’d have to unlock it and, dang, I just missed the start of the race. So now there’s a feature implemented that prevents the phone from going to sleep with the Coaching Timer app is active.
Volume Buttons Start Timer: Also I realized it was hard to tap a button and look at the starter’s pistol in the distance across a field since there’s no tactile feedback from a virtual button on the screen. I added a way to start the timer via either volume control – problem solved and it’s much, much easier.
Disabled Phone Rotation: At first I thought it would be important to be able to rotate the phone and have the app switch from portrait to landscape since some names can be long and starting wrapping in portrait mode. This mostly ended up being disorienting when trying to get splits quickly and I disabled it.
Multiple Simultaneous Timers: Most races end up completing before subsequent race begin, but occasionally that’s not the case and they overlap. This is why it’s important to be able to keep multiple timers running simultaneously.
Adjust Start Time for Running Timer: Another critical feature that I added that has saved my behind multiple times is the ability to adjust the start time of an already started timer on the fly. Super useful! Let’s say you missed the start of the race. No more quick math to adjust each time after the fact – as long as anyone got the start of the race on their stopwatch, this function let’s you set the timer to any running time. This has been probably the most useful feature I added – it happens a lot and this is an awesome backup.
Adjustment Factor for Certain Race Venues: There’s an optional “adjustment factor” that you can apply on the web site for certain race venues/meets. Let’s say there’s a course one day that’s cold and muddy and the times are very slow compared to a “normal” course on a good day, or perhaps a course is short. You can apply an adjustment factor that provides an adjusted time for the purposes of the PR calculation. This prevents the PRs being skewed if we run a short course where true PRs shouldn’t really be valid.
The State of the App
The app is pretty stable right now and I’ve thought about releasing it to other coaches. But in order to do that I would have to put a good amount of work into generalizing it to work with other teams. Also, it’s highly dependent on the web site which is another layer of complication for someone to set up. To be honest I’ve always thought this would be a good thing for MileSplit to do since they have a good bit of the data required to make something like this work for any coach.
If you have any comments or suggestions I’d love to hear some feedback. Comment on this post and also follow me on Twitter while you’re at it.
I’ve been trading gaps since 2005 with a statistical, data driven approach. I’ve put in a lot of quality time outside of trading hours backtesting, looking at charts, and developing software to carry out my trading plan. When you take trading seriously and instill a positive, improvement-focused attitude in yourself, once the market opens you should be executing a plan that you’re totally prepared for. You should essentially be following a script.
My Trading Script
I trade several strategies every day. My main set of strategies, though, follows this routine:
Look for pre-determined setups in stocks in my curated watchlist.
Execute trades according to the strategy’s plan.
Watch my trades like a hawk, obsessing over each and every tick.
Ha! I’m joking about #5 — once my trades are in I literally don’t watch them for the rest of the day — no kidding.
The vast majority of market days this works well. But what happens when every stock in the market looks “tradeable?”
On Big Market Days, Everything is Moving
On < 5% of market days, most every stock looks unusual!
These are the hardest types of days to deal with — there’s a lot of opportunity but you can’t realistically take every trade that looks unusual. It’s easy to get overwhelmed and become paralyzed on days like this. The problem is there is a lot of money to be made on these days so it’s good to prepare for them ahead of time. Here’s what I do.
Today there were 64 stocks on my top list for this strategy when there’s normally ~10–15. The first thing I do is look at this top list that looks at a single stock: SPY. (Here’s the cloud link.)
Screenshot of my SPY Today toplist at 9:32am 10/26/2018
I see that the SPY which represents the S&P 500 is trading at 13.8 relative volume. If SPY was trading “normally” it would have a relative volume of 1.0, so this means the SPY is doing 13.8 times it’s normal volume at this time of day — that’s a lot! Here’s what my unfiltered watch list looked like at the time (notice the vertical scroll bar!)
Original top list
This top list also includes the relative volume column. If stocks have unusual activity but on lower relative volume than the overall market, then they’re not really that unusual even it a stock is doing 4 times its normal volume.
Here’s what the top list looks like after I’ve applied some custom coloring to the Relative Volume column:
Top list with custom coloring applied to Relative Volume
The column comes alive showing you what is really unusual today compared to the overall market. Solid red shows me that it’s well below the threshold I’ve set. Darker blue tells me that the relative volume is unusual even on this market day. Lighter blue to white means it’s above the threshold but not by a ton.
This let’s me quickly see what is REALLY unusual given what the overall market is doing. I can very quickly and confidently narrow my list using data that I can rely on.
I’ve been trading gaps for over a decade. Over the years I’ve refined my trading routine using statistics and machine learning to figure out what works for me and what doesn’t, determining which gapping stocks I should trade and which ones I should ignore, how to best trade up gaps and down gaps, and automating as much of my trading process as possible.
Not all Gaps are Equal
Gaps
up, gaps down, large gaps, small gaps, gaps with high volume, gaps with
low volume — there are all sorts of different types of gapping stocks
each day. How do you decide:
Which gaps are tradeable?
How to trade them?
Of course these are not easy questions and it can take years of experience to get closer to the answers. What you’re primarily trading to answer is: will the stock have movement after the opening gap? If it is likely to move, then it’s tradeable.
My Daily Trading List
Each day I post my curated list of stocks that are on my gap trading radar for the day. This list mostly comes from my Trade-Ideas scan which I’ve honed over years of trading to efficiently generate a list of gaps which are worthy of possibly trading that day. My Trade-Ideas top list looks like this:
Example daily trading list for gappers
How I Use My Trade-Ideas Watchlist
This one view of my watchlist in Trade-Ideas gives me a succinct view into the data points I need to make trading decisions. It’s sorted by the Gap% column so I can visually determine which stocks are gapping up or down and by how much. Green background means gapping up, reddish background means gapping down. The darker the green or red means the bigger the gap.
Other Important Columns Color Coded
Other data points that I’ve statistically determined are important for trading gaps are color coded. With just a quick glance I can see some stocks are more tradeable than others and some should be considered a priority gapper today.
Stocks that Will Likely Move After the Opening Gap
The list that I post each day has been curated by me to include only the stocks that have high likelihood to move after the opening gap. I don’t determine the direction for the purposes of this list, but potential for movement = tradeable.
Movement after the opening gap
Only once I determine that the stock has a good chance of movement after the open will it make my daily tweet:
I
use 4 different strategies to trade these gaps: 2 long and 2 short.
Over the years I’ve made the actual order execution as quick and
painless as possible. When I started trading over a decade ago, I
entered the trades manually. I quickly realized that there were two main
ways this was hurting me:
Time consuming which caused me to miss profitable gap trades
Occasional order entry mistakes (position sizing, stop placement, etc)
I
eliminated both of these problems using software. I started developing
my own software to enter the trades. A combination of man and machine
helped me take more profitable trades and eliminate all my order entry
mistakes. I quickly learned that entering trades this way opened up lots
of new opportunities to trade profitably.
I’ve gone through several iterations of the trading software over the years: fine-tuning certain aspects and adding new features that over time I discovered that I needed. That process has culminated in Trade-Ideas Brokerage+ which I developed.
Give Brokerage+ a Look
If
you know anything about developing software, you know it’s harder than
it looks. I’m proud of what my lowly first attempt at order entry
software a decade ago has morphed into.
I got several excellent comments from various people to my post about why bicycle commuting has fallen since 2014. Some comments disputed some of my thoughts while others provided interesting theories of their own. I’ll summarize these responses. Here is the graph again:
I indicated the source as “US Census Data,” but I should have been more specific as one comment suggested that since the “census” is only done every 10 years that these numbers are completely made up. These numbers come from the American Community Survey and they are updated annually.
The Decrease Isn’t Really Real
A small number of responses were of this type. They said that the ACS is a survey and there is insufficient data to reflect a real decline. It’s theoretically possible that the numbers for the last 3 years are erroneous and that the number of bicycle commuters is actually growing, but I doubt it. The ACS is commonly used by cycling advocacy groups to point to successes – see this report by the League of American Bicyclists (LAB) which comes from the ACS data.
In general I think the LAB’s approach is a good one – attempt to measure municipalities’ cycling friendliness score and then use the ACS data to report successes. This can be a powerful argument for cycling advocacy – sprinkle some cycling infrastructure improvements and watch it grow.
Given the success in building cycling infrastructure in lots of areas in the US, are we bumping up against a theoretical ceiling for cycling? I don’t think so but I admit these ACS numbers make me less sure. One comment suggested that looking at bicycle sales numbers as evidence that the decline is an anomaly. A quick search for those numbers doesn’t look optimistic to me.
Commute Times are Increasing
A few comments suggested looking at workers’ commute times which are, in fact, increasing over time. Interestingly I couldn’t find any data on commuting distances. I would imagine that there should be data that analyses congestion over time by looking at commute times and commute distances over time.
By the numbers, all of the new workers over the last several years are choosing to commute by car. They apparently aren’t bothered too much by the longer commutes/congestion or maybe they don’t feel like they have a choice. As commutes get longer, at some point along that continuum the choice to bike to work is no longer a realistic option.
The Places Where Cycling Is Easy Are More Expensive
A few folks responded with this argument including economist Arnold Kling:
Perhaps rents went up in some locations, forcing some would-be bike commuters to move out of range
This is not something I thought of prior to writing the post, but this makes a lot of sense. The areas where cycling infrastructure is excellent or improving are also affected by the housing crisis. Could it be that local zoning laws are indirectly working against cycling? Seems like a reasonable theory worth exploring.
The Connected Generation Can’t be Disconnected Long Enough to Ride a Bike
Also, it is much harder to text on your phone when cycling, versus Uber (or even sneaking in texts at stoplights). For the ‘connected generation’ on-road cycling may be an unacceptable interruption. If they want exercise they can use a stationary cycle.
This seems logical to me although I have to way to verify it or gauge how important its effect is. I’ve always thought of this as an advantage to riding a bike, not a downside. I’m sure not everyone feels the same way.
lenore skenazy and i found some data on the sharp decline in biking to school for kids, i think it happened in the 1990s. 2015 would not be a special year, for adults; it was special on campus. i would not interpret the 3 year decline for adults as anything yet, could be random fluctuation. unless you can get data broken down by age. if you find no change for those over age 30, but yes for under 25, then it is Gen Z.
I looked for data on bicycle commuting and age via the ACS. Oddly enough, you can get raw bicycle commuting numbers normally, but when you group by age they are lumped together in a single group with “Taxicab, motorcycle, bicycle, or other means.” This category is increasing over the timeframe of the bicycle decline which as far as I can tell makes this impossible to isolate bicycle commutes unfortunately. If anyone knows how to use the ACS to isolate bicycle commuters please let me know.
Here are a couple charts that show the difficulty. Even though there is a drop in bicycle commuting, this larger group is increasing over time. Bicycle commuters are only a small part of this group so it’s hard to tease out any effect here.
Randal O’Toole Seems to Doubt the Work from Home Theory
I knew Randal O’Toole was an avid cyclist, so I emailed him to see if he had any ideas. Here’s how he responded:
Those are good ideas. Here’s an additional datapoint: people who work at home earn the most money. Drive alones are second, transit third, and “other” (which includes cycling) well below that. Walking is lowest.
So if work-at-homes captured cyclists, it was the high-end cyclists. I’m not sure how many of those there were.
A more significant phenomenon: the census form asks people how they “usually” get to work. The National Household Travel Survey found that people who usually drive in fact drive 98 percent of the time but people who say they usually bicycle in fact only bicycle 70 percent of the time. Maybe some of those people who bicycled, say, 60 percent of the time in 2014 went to 40 percent in 2017.
Randal said a couple things that surprised me here. One is that cyclists as a group earn less money than the average worker. I tried to confirm this with the ACS survey and it’s true that the group that contains bicycle commuters earn less than average, but I suspect that, like age, this is similarly confounded by grouping “Taxicab, motorcycle, bicycle, or other means” together. A quick search shows that in Australia cycling commuters are wealthier than average. I also discovered from survey data from the NC DOT that cyclists that use greenways strongly skew rich and white relative to the local population.
Randal’s theory about percentage of time commuting seems to jive with the housing costs theory previously mentioned.
General Safety Concerns/Helicopter Generation
A large number of comments suggested that cycling is, in fact, getting more dangerous over time due to drivers texting, more cars, more cycling-intolerant drivers, etc. I agree that it seems there is a general sense among cyclists that of course it’s getting worse on the roads. This has always struck me as somewhat of a moral panic. I’ve had my share of cars purposely buzzing me on my bike and yelling crazy things at me but I don’t get the sense from my own experience that these incidents are increasing over time.
Cycling advocates consistently talk about the large number of people who WOULD bike if they could be made to feel safer on a bicycle. This approach seems to have been successful in getting municipalities to devote resources to large cycling infrastructure projects. The current generation seems to be especially convinced by appeals to safety but there’s a bit of a paradox. If people believe cycling is TOO dangerous they’re not going to do it no matter how good the infrastructure is. (Safety first!) But if people feel that cycling is safe then there isn’t much demand for new infrastructure to be built.
Are the roads getting more dangerous over time? I decided to try to answer that question. It doesn’t feel like it to me but let’s look at the data. The National Highway Traffic Safety Administration has kept statistics on crashes for a long time. They produce an annual report that has all sorts of data on traffic deaths.
First off, I would have intuitively expected to see deaths from distracted drivers increasing over time (texting, obviously), but that doesn’t appear to be the case. (Not sure why we see that sudden decrease from 2009 to 2010 – anyone know?)
OK, well how about deaths from drunk driving? Are those increasing? No – it turns out they’re way down.
What about motorists that exceed the speed limit and cause a crash that kills someone? Getting worse over time? No – those are down too.
Note that the charts above are pessimistic since I didn’t normalize on vehicle miles traveled which has been steadily increasing over time. That is, if we accounted for miles traveled the charts above would look even better than they already do. Here’s a chart of vehicle miles traveled by year. (Note I adjusted the y-axis minimum for better readability.)
Most Unique Response: Fade Out of the Lance Armstrong Effect
From my local cycling friend Brendan:
The post Lance Armstrong cycling era taking effect.
I think the high end cycling world has definitely taken a hit – at least anecdotally from my perspective in a cycling heavy area. I’m reasonably confident (65%) USA Cycling could produce some reports based on road race participation over the last decade and it would show a steady decline. Would this have an effect on bicycle commuting? Probably but most likely the effect would be small.
Where is the Cycling Ceiling in the US?
It ultimately boils down to this for me as I alluded to earlier: how much room is there to grow cycling in the US? Of course I don’t have the answer to this but these numbers make me think that perhaps we’re closer to the ceiling than I would have guessed. And given the headwinds of safety consciousness, housing costs, the price of gas, and the rise of telecommuting, how big could gains from infrastructure improvements be?
I’m starting to think about a way to come up with an answer to this question on my own from some other data sources. Look for a post soon.
Offer to Bet
If you believe the drop in bicycle commuting is temporary, I’m offering a bet at 1:1 odds that the bicycle commuting number won’t exceed the number from 2014 in years 2018, 2019, or 2020. Any takers? Send me a DM on Twitter: @davemabe.
