This repo contains ideas, code, and example data to estimate ‘cyclability’ on transport networks. At present it is focussed on cycling because there are more readily available datasets for cycling than for walking that we are aware of. However at some point we would like to extend this to walking and other modes, in which case the repo may be renamed (e.g. to activability if nobody comes up with a better name ; ).

What is cyclability?

There are at least three definitions of how conducive to cycling different places, routes and segments of travel networks are:

• Level of Traffic Stress (LTS)
• Bikeability levels, which rates infrastructure based on the level of training needed to feel comfortable:
  • Level 1 teaches basic bike-handling skills in a controlled traffic-free environment.
  • Level 2 teaches trainees to cycle planned routes on minor roads, offering a real cycling experience.
  • Level 3 ensures trainees are able to manage a variety of traffic conditions and is delivered on busier roads with advanced features and layouts
• CycleStreets’s Quietness rating from 1 (very unpleasant for cycling) to 100 (the quietest)
  • The BNA tool which builds on the concept of traffic stress to classify segments as Low Stress or High Stress.

Example data

Data from Leeds, UK

Datasets containing estimates of ‘quietness’ and ‘cyclability’ for Leeds, UK, are available from the a separate repo. These datasets were taken from an area with the following bounding box:

     xmin      ymin      xmax      ymax 
-1.571467 53.797790 -1.541108 53.815759 

This area, representing a 1 km boundary around the University of Leeds (-1.556288, 53.80677) can be seen in OSM at the following URL: https://www.openstreetmap.org/#map=16/53.8068/-1.5563

To generate a GeoJSON version of the input data we used the osmtogeojson command line utility which can be installed on Ubuntu with the following command:

sudo npm install -g osmtogeojson

OSM data

OSM data was downloaded from overpass with the following command which uses wget to query the API for the bounding box:

 #| eval: false
wget -O example-data/leeds.osm "https://overpass-api.de/api/interpreter?data=[out:xml][timeout:25];(way[highway](53.797790,-1.571467,53.815759,-1.541108);node(w););out body;>;out skel qt;"

The output of the command above can be found in the example-data folder of this repo.

osmtogeojson example-data/leeds.osm > example-data/leeds.geojson

The first 10 keys in the OSM dataset that have at least one value are:

 [1] "area"                         "bicycle"                     
 [3] "bicycle:backward:conditional" "bicycle:conditional"         
 [5] "bicycle:forward:conditional"  "bollard"                     
 [7] "cycleway:buffer"              "foot"                        
 [9] "footway"                      "footway:surface"             

The OSM data can be seen in the map below:

The example dataset contains around 3k line segments and 200 keys with at least one value. The number of features by geometry type is shown in the table below.

Quietness

A GeoJSON file with quietness estimates for each road segment in Leeds is available at https://github.com/ITSLeeds/cyclability/raw/main/cyclestreets/leeds_quietness.geojson and is illustrated below:

A simple model was used to find out the relationship between the quietness rating and riding surface, resulting in the following plot:

From this we can see that, as would be expected, living streets and pedestrianised areas are associated with the highest quietness ratings. The model also reveals that, for the sample data in Leeds at least, the segment type alone can explain 67% of the variation in quietness ratings. The dependent variables shown in the plot above are produced by CycleStreets which were themselves derived from OSM data. Let’s try to reproduce the quietness ratings using the OSM data directly. To do that, we first need to join the data from CycleStreets with the OSM data.

The OSM data contains ids such as:

leeds_osm_geojson$id[1:3]

[1] "way/8094922"  "way/22770533" "way/25158101"

The CycleStreets data contains ids such as:

leeds_quietness$id[1:3]

[1] 1709456 1709460 2956857

After removing everything before and including the / character, the ids are the same, with the majority of the ids in the CycleStreets data present in the OSM data:

leeds_osm_geojson$id = gsub(".*\\/", "", leeds_osm_geojson$id)
summary(leeds_quietness$id %in% leeds_osm_geojson$id)

   Mode   FALSE    TRUE 
logical      33    2712 

Warning: There was 1 warning in `dplyr::mutate()`.
ℹ In argument: `id = as.integer(id)`.
Caused by warning:
! NAs introduced by coercion to integer range

Of the 200+ keys in the OSM data, only a few appear frequently enough to be useful for modelling. The keys that most frequently have values are shown in the table below:

Running the same model on the OSM data gives the following results and explains a higher proportion of the variation in quietness ratings: just over 90%.

Live examples

Network Planning Tool

Source: https://nptscot.github.io/#14.75/55.94993/-3.19227

Prior methodological work and implementations

• The Bike Network Analysis (BNA) tool methodology: https://cityratings.peopleforbikes.org/about/methodology
• Discussion of cyclability in A/B Street issue tracker: https://github.com/a-b-street/abstreet/issues/600
• Gist in Python on GitHub calculating cyclability from OSM: https://gist.github.com/aroche/d6fd03e51869c3e554f908bc14b5750b
• Methods described in by CycleStreets: Quietness rating

Thoughts on next steps (draft)

• [ ] Prototype code to generate plausible quietness ratings from OpenStreetMap data
• [ ] Wire up to a web interface
• [ ] Create a frontent to allow people to tweak the parameters affecting cyclability
• [ ] Develop default settings e.g. for different types of users (e.g. novice, experienced, confident) and implementations of different metrics (e.g. Bikeability 1 to 3 or LTS 1 to 4)
• [ ] Encode the settings that lead to these implementations in a human readable and easy-to-edit format, e.g. JSON