Data and Cycling: Application of data to cycling
While some people are asking questions about the Tour de France that are quite original - I recommend this article by a blogger who wonders how many French fries you would need to eat to have enough calories to finish the Tour de France -data is increasingly present in sports.
Let's study in this article the application to cycling with two applications:
- The Tour de France
- Urban mobility by bicycle
Data in the Tour de France
The Tour de France is one of the 3 most watched events in the world with the Football World Cup and the Olympic Games. So when we talk about cycling, we can easily think about it!
Here's how data is helping the Tour de France run better, for the enjoyment of viewers. All the bikes are now equipped with sensors to know their position in real time. These sensors allow to know precisely the gap between the riders and thus to follow precisely what is happening in the race.
Today, we talk abouteditorializing data. This data is used to improve the spectator's experience, allowing him to follow the race with as much information as possible.
We will be able to see the composition of the peloton, the evolution of each rider. This will help the media as well as the spectators!
The quality of the data is fundamental to managing such precise real time data in complex conditions: terrain - a steep valley for example - and highly variable weather conditions. A helicopter will make the link between the bikes and the datacenter truck that will process the data. The truck is parked at the arrival of each stage. At each stage, 19GB are connected. This allows the data to be updated every second.
In addition, the 3D tracker allows to follow in real time in augmented reality the Tour de France. It allows to better realize the characteristics of the terrain in which the riders evolve. Interesting in the mountain stages!
This data also allows us to make predictions. We can then try to predict the winner of each stage! Companies like Bwin or PMU are interested in such a technology.
Data for runners
We will also be able to measure several data to help the runners. Here are 2 examples:
- Tire pressure measurement. As soon as the pressure drops in a suspicious way, we can act to help the rider
- Temperature measurement. This allows us to check that the runner does not have a problem. If his temperature varies in an unusual way, we can try to make sure that nothing happens to him.
Data and urban mobility by bike
In recent years, bike sharing has developed enormously in cities. Very quickly, a difficulty arose: many stations were totally empty, others totally saturated. For example, a station at high altitude will often be emptier than a station at low altitude. A station near homes will be empty in the early morning when people go to work and too full at the end of the day when people return home.
This phenomenon of empty and full stations can obviously vary depending on the time of day, which has made it impossible in some areas to drop off or pick up a bike. We are therefore going to set up algorithms to limit these phenomena, in particular by making the link with other means of transport. By setting up incentives so that empty stations can be filled and full stations emptied. For example, if you park a bike at altitude, the trip will be cheaper or even free. This is cheaper than paying for a van to bring bikes to empty areas.
Sensors are also set up in the cities to know the frequentation of the bike paths and thus influence the public policies for the development of bike paths.