Big Data, virtualization to dominate smart transportation era

Traditional smart transportation approaches to address traffic congestion, safety, pollution, and other urbanization challenges are expected to hit scalability and efficiency obstacles by the end of this decade.

Photo credit: www.thebigdatainsightgroup.com

Photo credit: www.thebigdatainsightgroup.com

Traveler information systems such as variable-message signs, intelligent traffic lights, camera-enforced urban tolling, and traffic monitoring centers will ultimately prove ineffective and prohibitively expensive, threatening to stall economic growth, especially in developing regions.

According to ABI Research, global yearly spend on traffic management systems alone will exceed $10 billion by 2020.

“What will really be required is a step change towards virtualizing smart transportation solutions via in-vehicle technology, and cloud-based control systems whereby information is sent directly to and from the car, bypassing physical roadside infrastructure all together,” commented ABI Research VP and practice director Dominique Bonte.

“Low latency, peer-to-peer, and meshed-network type connectivity based on DSRC-enabled V2V, 4G, and — in the next decade — 5G, will be critical enablers of this transformation.”

ITS virtualization will heavily rely on big data with car OEMs such as Toyota, Volvo, and PSA already exploring generating hyperlocal weather and/or traffic services from car probe data, to be shared with both other nearby vehicles and — in aggregated from — governments and road operators. Other examples include Continental’s partnership with HERE and IBM on its dynamic eHorizon solution.

However, a closed-loop systems approach will ultimately become the key paradigm, allowing artificial intelligence-powered self-steering and learning demand-response solutions influencing traffic levels through dynamic speed limits and variable road use and toll charges.
Autonomous vehicles, in an ironic twist, will be managed collectively and controlled centrally, remotely and dynamically adjusting routing and other parameters.

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