Plastic bags on bicycle saddles

The new OEK (the members’ magazine of the Amsterdam chapter of cyclists’ organisation Fietsersbond) features another cool investigation by Pete Jordan. This time, he looked into the plastic supermarket bags Amsterdammers use to keep their bicycle saddles dry. He counted them around the railway stations Sloterdijk, CS and Muiderpoort.

Naturally, AH leads (almost 70% of bags), followed by Vomar, Dirk, Jumbo, Lidl and Plus. The high score for Vomar is remarkable given they don’t have many stores in Amsterdam. My guess would be that Jordan counted Vomar bags mainly around Muiderpoort: there used to be a Vomar around the corner.

The OEK further has an analysis of the city’s mobility policy. There will be more bicycle parking space but also 2,000 additional parking places for cars. In streets like Weteringschans and Kinkerstraat, the maximum speed will be lowered to 30 kmph, but the cycle path will be removed: cyclists will have to share the road with cars. Johan Kerstens predicts that cyclists will be cornered: «Would they divert to tram and bus? Or revolt, just like they had to in the 1970s?»

The OEK can be downloaded here (in Dutch), but if you join the Fietsersbond it will be delivered to your home.

Tags: 

Racefietsen vs stadsfietsen: hoe Utrecht zijn fietsers portretteert

In aanloop naar de Tour de France Grand Départ op 4 juli organiseert Utrecht activiteiten om het fietsen te stimuleren en om zich op de kaart te zetten als fietsstad. Zo publiceren ze een serie portretten van fietsers uit Utrecht. Wat voor fietsen hebben ze?

Er zijn tot nog toe 67 fietsen in beeld gebracht en daar zitten maar liefst 26 racefietsen en slechts 13 stadsfietsen bij. Voor beide categorieën geldt dat ongeveer de helft van de fietsen van staal is. De stalen racefietsen zijn niet van die met roestige kettingen die je veel op straat tegenkomt (bijvoorbeeld in Amsterdam), maar zorgvuldig onderhouden klassieke racefietsen (waaronder een mixte damesfiets) en handgemaakte designfietsen.

Verder is er een velomobiel (ik heb niet echt iets met ligfietsen maar deze ziet er cool uit), een Pedersen zweefzadelfiets en een Bough Bike.

Utrecht had ervoor kunnen kiezen om vooral oude stadsfietsen te laten zien, afgewisseld met relatief nieuwe imitatie cargo- en omafietsen met bruine comfortzadels, merk Cortina of Sparta. Dat soort fietsen gebruiken Utrechters immers voor hun dagelijkse ritten (check de video’s van Mark Wagenbuur). Maar de bedoeling van de portretten is natuurlijk niet om de gemiddelde Utrechtse fiets te laten zien. De portretten roepen een beeld op van Utrechtse fietsers als diverse, doorgaans soort van hippe mensen met snelle fietsen.

Methode

Sommige portretten beschrijven of tonen meerdere fietsen. Als het om verschillende types gaat, heb ik ze apart meegeteld. Bij de classificatie van de fietsen moeten soms knopen worden doorgehakt; zo is een enkele keer onduidelijk of een fiets moet worden aangemerkt als toer- of stadsfiets. De analyse is gebaseerd op portretten 100 tot en met 40; de rest moest nog verschijnen op het moment dat de analyse werd uitgevoerd. Download de data hier.

Tags: 

Road bikes vs city bikes: how Utrecht portrays its cyclists

In the build-up to the Tour de France Grand Départ on 4 July, Utrecht is organising activities to promote cycling and to present itself as a cycling city. For example, they’re publishing a series of portraits of Utrecht cyclists. What kind of bicycles do they have?

Out of 67 bicycles portrayed so far, no fewer than 26 are road bikes, and only 13 are city bikes. Among both categories, about half the bicycles are made of steel. The steel road bikes aren’t the ones with rusty chains you see in the streets (for example in Amsterdam), but well-maintained classic road bikes, including a mixte women’s bike, and hand-made designer bikes.

There’s also a velomobile (I’m not really into recumbents but this one looks cool), a Pedersen floating saddle bike and a bough bike.

Utrecht could have chosen to show lots of old city bikes, and a few newish Cortina or Sparta imitation cargo and grandma bikes with brown comfort saddles, which is more or less what Utrechters use for their daily trips (check the videos by Mark Wagenbuur). But of course, the point of the portraits isn’t to show the average Utrecht bicycle. Rather, they paint a picture of Utrecht cyclists as diverse, generally sort of hip people with fast bikes.

Method

Some portraits describe or show several bicycles. If they are of different types, I’ve counted them separately. The classification of bicycles isn’t always straightforward; for example, in a few cases it’s unclear whether a bicycle should be classified as touring or city bike. The analysis is based on portraits 100 through 40; the rest was yet to be published at the time of analysis. Download the data here.

Tags: 

Strava tweets II: after dinner rides and Sunday morning rides

The other day I posted an article about using Strava tweets to analyse road cycling patterns. I plan to do some more analysis on this but first I wanted to take another look at the time at which tweets are posted. Below is a chart that shows the number of Strava tweets per hour of the day.

Two things stand out: on weekdays, there’s an after-dinner peak, and on Sundays, many trips are finished before lunch. The pattern suggests that people tend to tweet pretty quickly after they finish their ride. This in turn seems to suggest that post times may well be a meaningful indicator of the time at which rides take place.

Gender

I used a variant of this script to determine the gender of people who tweeted their Strava rides, based on the first name of their Twitter screen name. According to the results, 9.7% are women. This is more than the 5.5% women in the SWOV survey among Dutch road cyclists, but then again people who use Strava (and tweet about it) are probably more likely to be young and young road cyclists more likely to be women.

For women the median distance of rides is 48km; for men 54km. The difference doesn’t appear very large.

In the chart above, you can select to see data for women instead of all riders (note that the scale changes). The main difference seems to be that for women, there’s much less of an after-dinner peak on weekdays. Perhaps something to do with the fact that women are less likely to have full-time jobs. But the numbers are relatively small so perhaps one shouldn’t read too much into it.

Using strava tweets to analyse cycling patterns

A recent report by traffic research institute SWOV analyses accidents reported by cyclists on racing bikes in the Netherlands. Among other things, the data show an early summer dip in accidents: 53 in May, 38 in June and 51 in August. A bit of googling revealed this is a common phenomenon, although the dip appears to occur earlier than elsewhere (cf this analysis of cycling accidents in Montréal).

Below, I discuss a number of possible explanations for the pattern.

Statistical noise

Given the relatively small number of reported crashes in the SWOV study, the pattern could be due to random variation. Also, respondents were asked in 2014 about crashes they had had in 2013, so memory effects may have had an influence on the reported month in which accidents took place. On the other hand, the fact that similar patterns have been found elsewhere suggests it may well be a real phenomenon.

Holidays

An OECD report says the summer accident dip is specific for countries with «a high level of daily utilitarian cycling» such as Belgium, Denmark and the Netherlands. The report argues the drop is «most likely linked to a lower number of work-cycling trips due to annual holidays».

If you look at the data presented by the OECD, this explanation seems plausible. However, holidays can’t really explain the data reported by SWOV. Summer holidays started between 29 June and 20 July (there’s regional variation), so the dip should have occured in August instead of June.

Further, you’d expect a drop in bicycle commuting during the summer, but surely not in riding racing bikes? I guess the best way to find out would be to analyse Strava data, but unfortunately Strava isn’t as forthcoming with its data as one might wish (in terms of open data, it would rank somewhere between Twitter and Facebook).

A possible way around this is to count tweets of people boasting their Strava achievements. Of course, there are several limitations to this approach (I discuss some in the Method section below). Despite these limitations, I think Strava tweets could serve as a rough indicator of road cycling patterns. An added bonus is that the length of the ride is often included in tweets.

The chart above shows Dutch-language Strava tweets for the period April 2014 - March 2015. Whether you look at the number of rides or the total distance, there’s no early summer drop in cycling. There’s a peak in May, but none in August - September.

Sunset

According to the respondents of the SWOV study, 96% percent of accidents happened in daylight. Of course this doesn’t rule out that some accidents may have happened in the dusk and there may be a seasonal pattern to this.

Many tweets contain the time at which they were tweeted. This is a somewhat problematic indicator of the time at which trips took place, if only because it’s unclear how much time elapsed between the ride and the moment it was tweeted. But let’s take a look at the data anyway.

I think tweets tend to be posted rather early in the day. Also, the effect of switches between summer and winter time is missing in the median post time (perhaps Twitter converts the times to the current local time).

That said, the data suggests that rides take place closer to sunset during the winter, not during the months of May and August which show a rise in accidents. So, while no firm conclusions should be drawn on the basis of this data, there are no indications that daylight patterns can explain accident patterns.

Weather

Perhaps more accidents happen when many people cycle and there’s a lot of rain. In 2013, there was a lot of rain in May; subsequently the amount of rain declined, and there was a peak again in September (pdf). So at first sight, it seems that the weather could explain the accident peak in May, but not the one in August.

Conclusion

None of the explanations for the early summer drop in cycling accidents seem particularly convincing. It’s not so difficult to find possible explanations for the peak in May, but it’s unclear why this is followed by a decline and a second peak in August. This remains a bit of a mystery.

Method

Unfortunately, the Twitter API won’t let you access old tweets, so you have to use the advanced search option (sample url) and then scroll down (or hit CMD and the down arrow) until all tweets have been loaded. This takes some time. I used rit (ride) and strava as search terms; this appears to be a pretty robust way to collect Dutch-language Strava tweets.

It seems that Strava started offering a standard way to tweet rides as of April 2014. Before that date, the number of Strava tweets was much smaller and the wording of the tweets wasn’t uniform. So there’s probably little use in analysing tweets from before April 2014.

I removed tweets containing terms suggesting they are about running (even though I searched for tweets containing the term rit there were still some that were obviously about running) and tweets containing references to mountainbiking. I ended up with 9,950 tweets posted by 2,258 accounts. 1,153 people only tweeted once about a Strava ride. Perhaps the analysis could be improved by removing these.

I had to add 9 hrs to the tweet time, probably because I had been using a VPN when I downloaded the data.

A relevant question is how representative Strava tweets are of the amount of road cycling. According to the SWOV report, about two in three Dutch cyclists on racing bikes almost never use apps like Strava or Runkeeper; the percentage is similar for men and women. The average distance in Strava tweets is 65km; in the SWOV report most respondents report their average ride distance is 60 - 90km.

In any case, not all road cyclists use Strava and not all who use Strava consistently post their rides on Twitter (fortunately, one might add). Perhaps people who tweet their Strava rides are a bit more hardcore and perhaps more impressive rides are more likely to get tweeted.

Edit - the numbers reported above are for tweets containing the time they were posted; this information is missing in about one-third of the tweets.

Here’s the script I used to clean the twitter data.

Bicycle path

Amazing. Apparently, they sweep the bicycle paths at the Veluwezoom.

Tags: 

Fietsonderdelen

In het ledenblad van de jubilerende Fietsersbond staat een mooi artikel over fietsonderdelen die vroeger vanzelfsprekend waren, zoals witte spatborden, stuurblokjes, buiscommandeurs, pompnokjes en banddynamo’s. Leden krijgen het blad in de bus; wie geen lid is kan dat hier in orde maken.

Tags: 

Scooters vaak sneller dan auto’s

Minister Schultz wil Amsterdam de mogelijkheid geven om scooters te verbannen van het fietspad en gebruik te laten maken van de weg, met een helm op. Dit moet het fietspad veiliger maken voor fietsers en zorgen dat ze minder fijnstof inademen. Auto- en scooterlobbyisten vinden echter dat het snelheidsverschil tussen auto’s en scooters te groot is. Met auto’s die 50 km/u rijden, is het voor scooters niet veilig om op de weg te rijden.

Maar halen automobilisten inderdaad 50 km/u in Amsterdam? «Fietsprofessor» Marco te Brömmelstroet heeft een kaart getweet die laat zien dat snelheden tijdens de avondspits vaak ver onder de 50 km/u liggen.

Als onderdeel van een open-datainitiatief heeft Amsterdam ongeveer 5 miljoen snelheidsmetingen op het Hoofdnet Auto tijdens de maand januari 2014 vrijgegeven. De grafiek hierboven laat zien dat, zelfs op het hoofdnet, de snelheid bij de meeste metingen lager dan 50 km/u was, met een mediaan van 31 km/u. Tijdens de avondspits ligt de snelheid nog gemiddeld 5 km/u lager dan ’s nachts.

Uit een onderzoek van de Fietsersbond uit 2011 bleek dat scooters gemiddeld 36,9 km/u rijden op fietspaden in Amsterdam. De kaart laat zien op welke wegen auto’s gemiddeld minstens 36,9 km/u (dunne rode lijnen) of 50 km/u (dikke rode lijnen) rijden. Overigens zou het kunnen dat de Fietsersbond de snelheid van scooters op een andere manier heeft gemeten dan de methode waarmee de snelheid van auto’s is gemeten.

Er zijn grappen gemaakt dat scooterrijders niet op de weg willen rijden omdat ze dan gedwongen zouden zijn om hun snelheid te minderen. De cijfers van de gemeente laten zien dat daar een kern van waarheid in zit.

Scripts voor de gegevensanalyse zijn hier te vinden.

Scooters often faster than cars

Minister Schultz wants to allow Amsterdam to ban scooters from cycle paths and make them use the road, wearing a helmet. This should make cycle paths safer for cyclists and reduce their exposure to air pollution. However, car and scooter lobbyists argue that the speed difference between scooters and cars is too large for scooters to ride safely on the road, with motorists driving 50 kmph.

So do motorists really make 50 kmph in Amsterdam? «Cycling professor» Marco te Brömmelstroet has tweeted a map showing rush hour speeds far below 50 kmph.

As part of its open data initiative, Amsterdam has released some 5 million speed measurements at the «Hoofdnet Auto» (the network of major roads for cars) during the month of January 2014. The histogram above shows that even at these main roads, the majority of measurements recorded a speed below 50 kmph, with a median speed of 31 kmph. Average speeds during afternoon rush hour were about 5 kmph lower than at night.

A 2011 study by cyclists’ organisation Fietsersbond found found an average speed for scooters on Amsterdam’s cycle paths of 36.9 kmph. The map shows roads where motorists drive on average at least 36.9 kmph (thin red line) or 50 kmph (thick red line). Note that the method by which the Fietsersbond measured scooter speed may be different from the method used to measure car speed.

There have been jokes that scooter riders don’t want to use the road because this would force them to reduce their speed. The data of the Amsterdam government show there’s actually some truth to this.

Scripts for processing the data can be found here.

Pages