At least that is what the French are claiming. So what do we do with all of the data from our rides (other than collect it in Excel sheets for end-of-season self gratification)? Well in Lyon they have used it to prove that biking is a more efficient commute than driving in the urban environment. Fast Company highlight the work done by the École Normale Supérieure de Lyon in France on the original bike share program.
They have taken 2 years’ worth of data pulled from the bikes in the cities Velo’V bike share program and analyzed a staggering 11.6 million bike trips, using the start and finish times and the overall trip time. Their conclusions are interesting:
- During rush hour, urban riders set an average speed of 9mph (the usual average speed is 6mph). This was faster than the average car speed during rush hour, and showed that between 7.45am and 8.45am the need to get to work made riders put a little extra effort in, and the bike beats congestion (although the average speed outside of rush hour could also be accounted for by riders making more stops as they weren’t just heading to work).
- Apparently in France there is a tradition that women stay at home on Wednesday mornings to look after their kids, resulting in the Wednesday morning riders pool being mostly made up of men. This logic “appeared” to push up the Wednesday morning average speed. Around here I would get an argument on that one – this is not my opinion, but the study’s assumption (it sounds like most of the women I ride with would kill most French male commuters any morning of the week). There is also the “Hump” theory that Wednesday is the hump of the week, and you are at your peak of activity.
- The fact that these riders were faster than cars over similar distances was all done with ZERO bike lanes. So we suspect that there was a little bit of stop signal breaking, and bus lane riding going on, but imagine what could be done with a bike lane network.
Although I am not really surprised by the last point, it is nice to see all the collected data being used to build the case for urban bike networks, and bike share programs. With the much talked about New York bike share program in the works, it would be nice to plan in advance to use the data collected to build a case for continued growth. Maybe we could get a sponsor to take on a smaller number than the talked about 10,000 bikes, and use that data to bring in more sponsorship opportunities for expansion.