Connected cars — example of Big Data transformation

Can you nowadays imagine a city of any size without traffic jams? I mean in the closest 10 years, and not in the far future? In the times when a car is still the most popular means of transport. The answer is no, I suppose. It’s like trying to imagine a world without hurricanes — highly impossible. City traffic is another element, and extreme sometimes, so we should see it as such.

Still, the effort we put to change it is titanic, to improve it — extreme and in the end — pointless. Lots of terrific engineering ideas allows to respond to traffic difficulties. Monitoring systems, adaptive and live traffic lights or regulation systems for inbound and outbound city traffic are just some of the examples we have in range

Those are great for many reasons. They are commonly used in cities, so travelling is possible in spite of the increasing number of vehicles and commuters. However, these solutions are not perfect. We all try to ‘react’ and ‘research’, but we base only on the samples of reality. Thus, we make wrong conclusions of the phenomenon that we can’t actually control. It seems like fighting nature’s force rather to use it’s potential.

Let’s ask what if? What if we tried a different approach? What if we decided to analyse this phenomenon not locally, but using global methods? What if we implemented a big scale research that requires millions of sensors and data collectors, and finally, a system that would be able to analyse gathered data? What if it’s not just wishful thinking but an actual project possible to realise?

Big Data Analytics includes trends analyses and data mining. Big Data assumes gathering all available data from many fields and sources. There are no samples, medians, etc., but factual data about factors that may influence city traffic. We can collect and combine data about various events like: vehicles localisation, driving trajectory, traffic accidents, weather, holidays’ calendar or even department stores’ open hours.

Imagine how many significant correlations we can reveal by such an approach? This way may help us finally understand the traffic and change our point of view of this common city characteristic. Thus, we can make predictions and accurately react on time.

You may think that this is an idle wish, but this technology is real and available at your fingertips. You can find more Big Data Analytics examples in the book above. This book was my inspiration to write about big data, but the second tiger was a real product that it describes — Cloud Your Car. I believe that Cloud Your Car in cooperation with Big Data is a sensational example of the next generation product.

Cloud Your Car allows us to take a closer look to the route traffic as it is. It collects most of the core data that are not available to measure in any previous approach. When we combine together vehicle’s position, driving quality and route characteristics with information mentioned earlier, we can receive a whole picture of how ‘street noise’ looks like. From this point I can say that we are just a step behind predicting the future!

Now, imagine that we are able to collect these data from most of the vehicles on routes. Shortly, the data and analyses will transform into planning only right urban models of how to design and organise transport, routes and city centers. This means cities that actually suit us, not the reverse.

by: Robert Szewczyk

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