Using insights from recent article about improving bike infrastructure in Paris about paris-bikes HOT 3 CLOSED

To build an attributed network we merged the street network derived from OSM, after removing e.g. footpaths, motorways, with the attribute information contained in the Apur dataset using OSM IDs. [...] The resulting dense cycling network consists of 25’197 edges (street segments) and 17’073 nodes. A new street segment is defined at every intersection or where cycling infrastructure changes and contains information on street characteristics such as speed limit (derived from OSM attributes) and the type of infrastructure provided (based on Apur data).

We could replicate this method if we wanted to try out what @Liyubov proposes in #18

One final limitation is important. Our work describes the state of infrastructure in a city in the form of suitability, and then uses this suitability to model bikeability across a large urban centre. The approach is designed to give an overview at the city and neighbourhood level, based on a reproducible method which models both suitability of cycling infrastructure, and its relationship with a meaningful accessibility model. It does not however replace local knowledge which is an essential component of detailed planning of cycling infrastructure.

I like this comment, very much in line with the previous studies that the City of Paris purchased for picking exact bike parking locations :)

from paris-bikes.

Yes there is more research on this for other cities, which @ryanrakusin and others spotted.
The article is good catch, maybe to test with new data, simplify their results and show them to City hall could be a good idea.

from paris-bikes.

Closed because we already presented the highlights from the article in a meeting with the city

from paris-bikes.