Digital technologies and BIG DATA are changing the sector playground, deeply transforming vehicles and how we interact with them, and even reshaping the transport infrastructure network. Smart infrastructure technologies, collectively known as Intelligent Transportation Systems (ITS), are being embedded in roads and tunnels as well in car parks and toll-booths, making them increasingly able to communicate with each other and with the vehicles that use them.

These innovations offer the prospect of a transport infrastructure system that suffers less congestion, is safer, and can be maintained predictively. Smart road infrastructure can involve a number of factors, can use technology for better monitoring, and can control traffic flow, update travelers, or even communicate directly with cars to warn of a speed limit change.

It is an essential part of the future of transport for smarter vehicles.


 

Big data is a big thing since his definition.

Among the emerging technologies of the last years, BIG DATA and IOT (Internet of Thing) seem to have passed the critical phases described by Gartner's Hype Cycle, to become fully established technologies, ready for an open diffusion.

The term “BIG DATA” ensembles a large variety of data types, but an even larger list of activities, applications and mobility life sectors.

To be fully understood, the approach to Data issue, must be fully linked to a low cost and easy access sensors display policy, able to generate information that can be collected and analyzed in real time.

That’s the reason for a road network, capable of reading signals from infrastructure installed sensors, and ready to read the signs of the smartphones passing through a road, and able to dialogue with the technologies on board of a vehicle, in order to have an “intelligent structure“ vision.

This view smoothly leads to other concrete applications in autonomous vehicle and predictive traffic management.

All these items are different rings of the same chain, and big data science has a key role in keeping them together firmly.

Sensors and Data are useless if not designed and collected on a specific purpose analysis. A proper data collection comes directly from a clear vision of what to achieve from data in matter of services, instruments and business. A clear business vision is what makes data, big.

Being based on big data, here we have a mix and match of tools, services and sectors to be considered and enabled with a precise view.



 

Traffic Management as a Service

Digital technologies are transforming traffic control centres from rigid giant “hardware and software” buildings, which are difficult to update, into virtual traffic management services that are smarter and easily integrable with multiply information sources.

The new mobility platforms are already integrated with a number of existing systems that hold information on real-time traffic patterns starting from the analysis of GPS in App’s or in the car up to the different signals coming from the engine and the electronics of the vehicles.

Modern traffic control centres find difficult to constantly monitor or include data from these systems. This new systems would integrate local and global information sources and instantly monitor them for anomalies.



 

Future Scenarios


 

Autonomous vehicles

Autonomous cars and freight trucks are living their beta testing maturity and even if it could appear futuristic it’s big data that actually controls self-driving vehicles.

Autonomous vehicles — like Google's (Waymo) self-driving cars that are so often talked about — use a variety of traffic and environmental data to constantly analyze their position in the world. The cars are outfitted with a myriad of sensors to monitor things like their positional awareness, proximity to pedestrians or other drivers, traffic guides and signals and much more. At any given time, they tirelessly analyze their local surroundings, looking for braking or slow down telltales.

These autonomous vehicles drive much better than any human ever could or will. Self-driving vehicles never get tired or exhausted, they never lose focus and always make the right decisions in a split second. Whereas humans, on the other hand, are certainly what makes the roadways less safe.

According to Google, with a record of over 1.8 million miles logged of autonomous and manual driving in their self-driving vehicles, there have only been 13 minor fender-benders recorded. More importantly, all of those accidents were the direct result of human error and not caused by the autonomous vehicles or their advanced driving systems.


 

How Does Big Data Fit into all of this?

Google isn't the only company working on autonomous vehicles. In fact, there are a bunch of different brands working on their own form of the technology such as Tesla, Nissan, GM, Mercedes-Benz, Delphi Automotive, Audi and Bosch. The one thing all these different systems will have in common is big data.

According to INVENT — a company dedicated to “developing inter-vehicular networking, computing, and sensing technologies for next generation smart vehicles” — autonomous vehicles, or “smart” cars of the future, are nothing more than a cog in a much larger data collection system.

"Such vehicles have embedded computers, GPS receivers, short-range wireless network interfaces, and potentially access to in-car sensors and the Internet. Furthermore, they can interact with roadside wireless sensor networks on roads where these networks are deployed”, describes INVENT .

In other words, any vehicles — or the systems controlling them — will sync up with a large network that is constantly feeding data about the local environment and roadways. They will remain aware at all times about congestion or traffic on a current route, accidents and potential dangers, arrival and departure times updated in real time and so much more.

 

The Interconnected Future relation of Vehicles and Data

As these vehicles go mainstream — which many experts predict will happen sometime in the next decade — big data will become increasingly more important. These vehicles will need to tap into a larger network or information database to communicate with one another about the world at large.

While this may sound like some kind of distant future scenario, it’s not. We’re remarkably close to operating these vehicles on roadways. Ford announced it is teaming up with Lyft on self-driving cars and it just goes to show we are on the verge of this technological hurdle. California has already signed a bill into law that allows these vehicles to be used on state roads, and it’s only a matter of time before more locations follow suit.

Still, systems backed by the kind of technology is working on will be necessary to collect and share data with these vehicles. One strategist believes that driverless vehicle systems will create up to 1GB of data per second, which means we’ll need a place for all of it to go.

Big data is already a central focus for a variety of industries, so it comes as no surprise that technology like driverless vehicles will make it even bigger and more necessary.

Education in ML (Machine Learning) and AI (Artificial Intelligence) in transportation and beyond, is what we need to start thinking of data to turn into big data and to be deployed in usable activities.

Investing in BIG DATA approach and in practical applications about AI, is a way to become a world class leader in mobility applications, with a sense.


 

ROLL OF BIGATA AND AI in IoT


 

Credits

UNECE - Working Party on Road Transport (SC.1) http://www.unece.org/trans/main/sc1/sc1.html

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