Congestion Maps for Cyclists

I had an idea to create a road map for cyclists, colour-coded by the likelihood of congestion, using GPS data. Here’s some background.  I’ve been plotting a cycle commuting route from the West End to the Kingston area.  On the map, Fulham Road looked like the most direct route: a relatively straight diagonal to the SW. But when I jumped on the bike to try it out, I found that Fulham Road is narrow, and so there is no way to filter past heavy traffic.  The moral of the story is that the most direct route is not always the fastest. Now, I record all my routes via GPS, so I have data files containing time-stamped way-point data. In theory, if I looked back at the extract I would see that some roads were fast and others were slow, by working out the average speed between splits. I could then turn that GPS data into a nice colour-coded map: red roads for slow, and green roads for fast. Anybody who looked at that map would know to avoid Fulham Road at the time I hit it. Now what if thousands of cyclists did this, and the data was aggregated? You’d eventually have a road map of the British Isles, colour-coded by the likelihood of congestion given the time of day. Taking it further, you could ask for a suggestion of the route from A to B which is least likely to be a wall of steel and exhaust fumes. You could get even warnings or suggestions from your smartphone in real time. This does rely on having access to a large number of GPS recordings from cyclists, so there needs to be an incentive to participate. That’s where the route suggestions come in. I don’t know how practical this would be. There are certainly a number of snags with linking average speed to congestion … Time of day, hills, fitness levels, filtering abilities, “traffic light etiquette” and more. I get the impression that Tom Tom works in a similar way - collecting time-stamped GPS data from a large number of users. But Tom Tom is fairly closed, commercial and car-centric.  I want something tweak-able, for cyclists. Time-permitting, I’ll try this with my own data first as a proof-of-concept. EDIT: I’ve cooled down on this idea.  Once you aggregate large data sets, controlling for outside factors (hills, speed bumps, fitness levels and the like) would be far too complex.  The idea could be re-positioned as simply a “how fast do cyclists go” map for general interest.  I doubt this would get enough interest to justify the effort, though.