In Analytics

With the European Football Championships having just come to a close and the Olympics due to start, the Summer of 2016 will have seen two major events that only happen once every four years on the sporting calendar. These are in addition to the regular annual sporting events such as Wimbledon, the British Grand Prix and the Rugby League Challenge Cup Final. With events such as these, a lot of people travel whether it be locally or internationally. Such spikes in travel can have implications on the travel networks and cause problems with people getting around.

Despite the fact that the football championship was in France and the Olympics Brazil, back at home in the UK it is likely that a huge number of people will be watching these events live whether that be in a pub, a sporting establishment such as a club or at home. A huge number of people would have traveled to Wimbledon and also to Silverstone as well as those who made a trip to France and the more adventurous who might descend on Brazil.

Of course in the modern day world where we are able to watch all of our TV on demand it doesn’t really matter whether we miss one of our favorite programs. In the case of live sport however, it is extremely difficult to keep away from social media, news alerts and radio during a live game. So it is likely that a lot of people will watch sport live to stop the end result being spoiled for them.

Take the Olympics for example. Not only will a lot of people travel to Brazil from all over the world, they then need to travel inside the country to see various events. Local Brazilians also need to travel around the country to see the various events plus conduct their usual business. This will cause an increase in people traveling around the country over the period that the Olympics is taking place.

How can analytics help in these cases?

Using data to predict spikes in demand for transportation could be paramount to the success of a large sporting event such as the Olympics. For example, how many tickets have been sold for an event in one of the satellite locations in Brazil could indicate a lot of people traveling from Rio at the same time. Using IoT and data analytics could mean looking forward to one of these events to predict who might be traveling and what effects this could have. By enriching the data further with the city or postal code of ticket purchaser could tell planners where people are traveling from.

Of course it is difficult to predict as a lot of the locations are new and Brazil hasn’t hosted the Olympics before, but by pulling together data from previous transport networks and large events, planners might be able to predict where blockages or problems might occur. Predicting potential problems offers the opportunity of preventing problems from occurring in the first place.

The main aim would be to look at passenger info for the main transport hubs and see where the potential problems might occur normally, then predict what could happen when these places are busier due to huge numbers of people. Brazil wants to make a good impression during the Olympics for people who are visiting but also for people from the country to be proud that it did a good job. By predicting how the transport networks could be affected it will mean that the travelers will be happy and safe whilst visiting the country but also the networks will remain reliable and thus the country will see overall economic benefits from hosting a large sporting event.

(Image courtesy paha_l / 123RF Stock Photo )

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