Data and transportation: How does data help the transportation sector?

Data has many applications in transportation. Let's take a look at some of the applications where it can help this sector.

Data and public transport

For public transportation, here are two examples of applications.

The first one concerns something that seems simple but is fraught with difficulties. It is about passenger information: announcing that a train is late and with what delay is important. So we can use machine learning to predict delays. Passengers will at least have reliable information, which will reduce their dissatisfaction. Another application around this passenger information is real time. Via applications, we are able to know precisely the level of occupation of the roads. And then, with a driving respecting the limitations, to know very correctly the arrival time, called ETA (estimated time of arrival) by the applications.

The second application is the optimization of the transportation network. For this, we will understand the passenger flows to be able to find the best schedules, the best frequencies and thus improve the efficiency of train, bus and metro networks. For buses, we can also choose to optimize the location of stops, which is necessarily more complex for an already built rail network. It is also possible to create carpooling areas at the best locations.

Data and the future of transport

The two fields of application with data for which there are the most efforts in transportation are the following: 

  • Predictive maintenance
  • Autonomous vehicle

All types of vehicles are affected by these two cases. For the first one, we will mainly start with trucks and trains. It consists in predicting the right time to change and maintain parts. Rather than waiting for a particular part to break down - which can be dangerous if it's a brake, for example - we're going to set up sensors and collect data to anticipate breakdowns and maintenance needs. Prevention is better than cure! Repairing parts before they break will save money, in addition to reducing the time the equipment is in maintenance and is not used.

The other case, which is the best known and on which a large part of the investments are concentrated, is the autonomous vehicle, with Tesla the most publicized. It is a question of being able to make sure that a vehicle no longer has a driver and can therefore move forward on its own, while respecting the rules related to this type of vehicle. 

Autonomous vehicles already exist! But they are not the ones you think. In fact, there are driverless subways. In Paris, for example, lines 1 and 14 are equipped with autonomous vehicles. These subways do not have a driver. The next type of vehicle will potentially be the car, where some tasks - such as driving straight through traffic - are already well mastered. But this automation will follow for other types of vehicles: trains, then boats and even airplanes will be made autonomous. 

In particular, autonomous vehicles will be better able to optimize energy consumption and thus reduce CO2 emissions. This should also help to reduce traffic congestion in dense areas.

Open Data is very present in the transportation sector

You want to work with transport data? The website allows you to use a lot of public data to develop new use cases related to data in transport! SNCF, Air France and RATP also offer data sets. The NOTRe law - New Territorial Organization of the Republic - obliges local authorities with more than 3,500 inhabitants to make available data produced or received as part of a public service mission, such as transport.

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