During the coming year, vague references to the Internet of Things -- and related big data analytics applications -- will start to evolve into very meaningful use-case scenarios. The metamorphosis of road transportation management is one example of this trend.
Traditional smart transportation approaches to address traffic congestion, safety, pollution, and other urbanization challenges are expected to encounter scalability and efficiency obstacles by the end of this decade.
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. This will threaten to stall economic growth, especially in developing regions.
Clearly, there's no shortage of investment in this market. According to the latest market study by ABI Research, global yearly spend on traffic management systems alone will exceed $10 billion by 2020.
But both current and future investment needs to be focused more on the apparent forward-looking perspective.
"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," said Dominique Bonte, VP and practice director at ABI Research.
ABI believes that low latency, peer-to-peer and meshed-network type connectivity based on DSRC-enabled V2V, 4G, and 5G, will be critical enablers of this transformation during the next decade.
Intelligent Transportation System (ITS) virtualization will heavily rely on big data with car OEMs such as Toyota, Volvo, and PSA already exploring generating hyper-local weather and/or traffic services from car probe data, to be shared with both other nearby vehicles and -- in aggregated form -- governments and road operators.
However, according to the ABI assessment, 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.
This latest ABI study covers big ITS data, physical roadside transportation infrastructure virtualization technologies, and a systems approach to transportation management. Relevant connectivity, analytics, cloud platform, security, and identity technologies are also described.