How AIoT enables smart traffic solutions

Article By : Ravi Kiran

Major advancements in AI and IoT could be the key to eliminating traffic congestion once and for all.

The early pandemic days of no traffic are officially over, with major cities like San Francisco reporting a near 90% return to 2019 weekend freeway clogs and commuters across the country once again stuck in bumper to bumper traffic. People got comfortable coasting along vacant roads during 2020, and morning commutes went from painful wake-up calls to a quick walk to the computer screen. But now as the world is waking up and seeking in-person connection, the roads are clogging, 6 billion gallons of gasoline are being lost to idling on freeways, and red lights seem to be lingering a little too long.

Traffic congestion is a catalyst for a myriad of problems beyond wait-times. Idle cars mean wasted gas and increased carbon dioxide emissions, impatient drivers, and unsafe road conditions increase the rate of potential injuries and accidents. Inaccurate and untimely data distributed to road operators could be the difference between reliable weather-road forecasting or a build-up on I-5 due to standing water or black ice. Drivers rely on real-time information being successfully provided to these road operators in real-time to help facilitate movement along motorways, but maintaining that type of data transfer at scale is easier said than done. That’s why cities across the globe are seeking efficient, technically-advanced solutions to facilitate constant two-way data transfers to out-smart traffic jams and alert drivers to any hazards on the roads.

Luckily for city dwellers, major advancements in the Internet of Things (IoT) and artificial intelligence (AI), largely driven by an increased emphasis on digital transformation in a post-pandemic world, could be the key to eliminating traffic congestion once and for all. The IoT is growing at an exponential rate thanks to its proven ability to enhance software development, supply chain management, user experiences, and more. With over 83 billion expected connections by 2024, an increase of 130% since 2020, the IoT ecosystem is now robust enough to enable large-scale deployments to solve specific challenges, including a solution to traffic congestion in urban areas.

AI-enabled intelligent traffic management software embedded in connected devices are now advanced enough to analyze traffic patterns, weather conditions, and more, on the scale required to truly combat traffic congestion. So how does this technology work, and why is it better than current traffic management solutions?

It starts with black boxes equipped with sensors that are strategically placed along roadways and connect to roadside servers to relay real-time information to other sensors and operators. These operators then receive signals to direct personnel to alter digital signage, alert traffic control to close roadways, help to manage movement throughout cities with better hot-spot congested area identification, and more. The data is processed in vehicles and transferred to and aggregated in the cloud and sent back with minimal delay to relay localized road safety messages. Vehicles, operators, and traffic control can communicate with one another instantly, providing an effective traffic management solution that drives circles around the preventative measures currently in place.

Some of the proven benefits of AIoT-enabled smart traffic solutions include:

  • Decreased vehicle emissions
  • Accurate traffic counts and real-time recording
  • Reduced waiting times at traffic lights and on freeways
  • Efficient reactions to changing traffic patterns and road conditions
  • Decreases accidents with accounts and training to identify human error
  • Detection of anomalies in real-time, balancing traffic capacity and demand
  • Safer roads and civilians in urban areas

Smart traffic solutions are already being deployed in major cities across the world. These solutions are adaptable, and because they’re powered by AI, they get smarter and more effective the longer they’re deployed by learning more about the patterns of the city they’re serving.  This is one of the biggest promises of AI-powered solutions, and we’ve seen it play out this way time and time again. Take the creation of an artificial intelligent neural network, for example. When creating a simulation of the brain, you need to develop the initial connections to spark internal conversations. After this, more connections are made through running tests on the simulated training model. As the network is expanded, it runs through various situations autonomously, learning and evolving through the creation of new connections. It’s adaptive and self-sufficient without the need for physical implementation of new neural network connections. AIoT traffic management solutions function in a similar way. The more time the sensors are tracking road conditions, weather patterns, and vehicle and driver tendencies, the more connections the IoT sensor cloud network will create on its own. This is more commonly known as the system of rewards and penalties, but now with an AIoT twist.

Smart traffic management systems will adapt and ultimately provide anticipated and real-time traffic alerts to road operators without lag time. Connections are created, freeways opened, gas emissions decreased, and roads are made safer. Leveraging AIoT technology as a solution for traffic congestion in urban areas will give drivers all of the green lights on their morning commute and change the method of movement to promote more environmentally friendly, efficient, and safe road conditions for the future in cities across the globe.

This article was originally published on Embedded.

Ravi Kiran is Founder and CEO of SmartCow and firmly believes in the ability to transform the way we live and work through its bespoke AIoT solutions. His company builds customizable AIoT devices and scalable turnkey software and technologies for computer vision applications specializing in advanced video analytics, applied artificial intelligence and electronics manufacturing. Prior to SmartCow, Ravi worked in computer security engineering for over a decade, before moving into AI engineering. He holds a bachelor’s degree in Information Technology from Nagarjuna University and a graduate certificate in Artificial Intelligence from Stanford.


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