Video: Why Open Data Matters for Cycling: Visualizing a Cycling City
trufi-association.org
trufi-association.org
Related, there's some free courses on how to do Urban Mobility Visualizations (those cool 3d renders architects and urban planners make) at urbanmobilitycourses.eu, well worth checking out.
It looks like it is a recording of something that was intended as a live seminar¹ so a conscious decision was made to make and present it this way. Though transcript would be useful, that could take time to produce². If you have an abundance of time, perhaps you could volunteer?
[1] one that might have been in a hall somewhere but was pushed into homes by C19 restrictions, going by the backgrounds
[2] the speakers may not have had full scripts, just crib notes, meaning a transcript would be more work than simply collecting their notes together, and I've not seen great results from automatic transcription except when the speaker has a very clear “standard” accent and the sound quality is high.
Without a good measurement, we won't have something to point at to force cities to be better. And making these changes is an urgent must right now for the climate of course and just for the average standard of living, which seems to be going down everywhere due to the sizes and quantities of cars that are arriving in cities lately.
I really support this kind of work and hope it grows. Here is another nice project I found using OSM during my search:
https://pasaentuciudad.com.mx/ranking-cyclability-in-europe-...
I'm the designer and developer of CicloMapa [1], one of the tools presented in the video. The project is a partnership with ITDP, which is an international organization that created and constantly measures a metric called People Near Bike (PNB) [2]. In summary takes into account if the infrastructure is really serving people that live nearby.
Another metric we use, at least for some brazilian cities, is IDECICLO [3], which analyses the quality of the existing infrastructure in terms of access, comfort, safety etc. It's a very holistic metrics, but compared to PnB is way more complex to measure and more prone to subjectivity.
[1] https://ciclomapa.org.br/ [2]https://itdpbrasil.org/pnb/ [3] https://plataformadedados.netlify.app/ideciclo/
I would think it would be a challenge to get useful data from such a source even if resolution were high enough. That nice low traffic road may have been photographed in the weekend, outside traffic hours, on a national holiday, because of a road black a kilometer away, etc.
I guess that could be limited by how good the mapping data is where you live but it works really well where I am.
Back when I used it my commute had tens of segments with 40km/hr medallists, and a sprint segment with weekly new records in excess of 50km/hr. And, you can even catch snipers rolling on to and off a road near me to protect effort for a two kilometre junction-free sprint most weekends.
These days it's all a bit moot as you get people on ebikes and scooters "cheating" by recording their rides as road rides (either deliberately or more often by accident).
On cheating: I used to moan about Strava accepting car and train journey speeds when it felt obvious how to detect some of them, but I can't even begin to imagine how to automate detection in the era of e-bikes. People must spend a lot more time flagging rides to maintain their magic internet points trophy now ;)
I'm a very experienced cyclist and I don't upload my 'transportation' rides. It's not worth it. So all the data from me riding a particular route twice, five times a week - as well as my preferred routes to various activities, shopping, etc - doesn't make it into strava. My fun / training rides do.
Strava ride uploads tend to come from more confident and willing to ride on roads that are more intimidating or require confident riding techniques to be safe (such as riding at the edge of a bike lane, or on a multi-lane road, taking the lane). Cycling in urban settings is much less intimidating if you're fit and able to accelerate quickly and bike at closer to the average speed of traffic (which often really isn't that fast.) Drivers are a lot less prone to "punish passes" and other dangerous behavior if you're fast.
They may ride a particular road that is terrible for cycling but they have no other choice because of where they are coming from or going to, and because they tend to ride a lot, they'll bias the heatmap. One of my favorite routes to ride involved an utter shitshow of 5 minutes worth of riding, and I took that road several times a week.
It should also be known that Strava tries to play up their "we make data available for city urban planners and cycling advocates!" to their userbase...and then turns around and charges an obscene amount of money.
Speaking of money: strava data means you miss the vast majority of people riding bicycles - those on the very lowest rungs of the economic ladder. They don't have GPS activity watches or GPS bike computers, they don't give a damn about recording their ride to/from work/school; they may not even have a smartphone, period. They don't have 12+ hours of leisure time a week to go for rides for fun, etc.
Bike advocacy groups are increasingly trying to account for these folks, because they're largely "invisible" - they don't sign up for newsletters from bike committees and advocacy groups, they often are riding outside "9-5" commute hours because they're working shifts/nights and riding to/from neighorhoods wealthier folks do. Most people think that in any given city the predominant cycling demographic is hipster programmers on track bikes...not realizing how many cyclists are maintenance/cleaning/construction/food service workers are out on the roads while they're asleep.
It might be possible to model traffic with OSMnx to assign weights to roads by expected traffic levels https://github.com/gboeing/osmnx
Depending on where you live you might be able to get traffic data and maybe traffic models from public authorities using Freedom of Information requests. eg London's TFL:
https://datatonic.com/case-studies/traffic-prediction-tfl/
>Designed to gather data from all over London, TFL’s Urban Traffic Control (UTC) system collects car activity records via 14,000 individual road sensors located throughout the city. We were given 3-months worth of these records to create our traffic prediction model — totalling over 120 billion data points.
Once you have road weights you can use something like brouter - https://brouter.de - it can already do road types and elevation.
LA has a lot going for it in terms of biking for transport. Easy to navigate road system, the sprawl means bikes are an attractive alternative to walking, ditto for having pretty flat terrain.
Biking in LA is great if you live in like Playa del Rey, Santa Monica, Burbank, Pasadena, or even Altadena, but in a lot of places it needs a ton of work to be seen as anything but a deathtrap in a lot of people's eyes. To say nothing of the rampant bike theft. I've lost one so far, knocking on wood.