The way we commute may have changed over time, but not the way traffic is managed.  The INRIX Global Traffic Scorecard reports that in 2018, the 20 most congested cities in the world saw a reduction of 164 to 210 hours per capita in congestion.  The The exponential growth of vehicles in cities is a central cause of traffic congestion.

Improving public transport is the solution, but at the same time, we also need to consider how to improve the efficiency of traffic management to improve the scene.  Traffic authorities have attempted to transform passive management into active traffic management, but have been limited by network speeds and processing power at the edge.  5G and artificial intelligence offer huge opportunities for traffic management.

When 5G hits the road

With denser and more complex road networks, newer technology and greater data, 5G will provide greater visibility and control over traffic.  This, in turn, will help unclog the transport network faster, further reducing congestion, eliminating knock-on effects and making roads safer for all users.  With 5G, edge devices will become more powerful at transmitting and processing large amounts of data through AI analytics servers, which will only benefit traffic management.

It is 70 times faster than 4G, and it will provide comprehensive visibility into the movements of all road users - people and traffic - for better overall planning.  With a host of sensors, cameras and even drones, 5G will transform the road network into a set of tiny clouds, each of which can communicate with each other, including autonomous vehicles.  The vast amounts of data generated by sensors in autonomous or autonomous vehicles can be accommodated effortlessly by 5G, enabling vehicle-to-vehicle and sensor-to-sensor communication.

Sensors in these vehicles will gather critical information based on recorded observations to make decisions and change routes.  The self-driving car Martti from the VTT Technical Research Center in Finland has been tested to detect icy road conditions in advance and transmit 3D views between vehicles.

AI Solutions and Big Data

The power of artificial intelligence (AI) and big data combined with the advantages of 5G technology will provide a powerful solution that combines high reliability and ubiquitous network access.  The low latency provided by 5G is key here, with AI models using real-time network information and historical data to detect the likelihood of an event and instantly design an optimized response plan for high-speed delivery.  Using a combination of traditional and edge-based AI systems, traffic metadata from the entire road network can be captured in real-time .  This combination of 5g and artificial intelligence will be the answer to transforming traffic management over the next decade.  It could also mark a much-needed boost for autonomous vehicles in a collaborative, connected system. Let's look at two specific AI-based solutions and their impact on vehicle activity.

Artificial Intelligence and Smart Traffic Lights

AI-based traffic light control will have a significant impact on vehicle activity, significantly reducing vehicle conflicts and increasing road network capacity.  An integrated setup for effective traffic management will involve adaptive traffic light systems, edge systems and backend monitoring systems.  Video captured using IP cameras is relayed to an edge-based AI system that analyzes the data before sending it to back-end monitoring.  Pre-trained deep learning models send processed information back to adaptive traffic lights in real time to create traffic flow.

With traffic lights adapting to changing traffic in real time, movement on the road can be controlled by traffic light timing, which can adjust itself.  Changing traffic scenarios and the timing of intersections can be shared through interoperable communications so that all intersections are ready to optimize approaching traffic flow.  The pilot system deployed in Pittsburgh, Pennsylvania, reportedly reduced travel time by 26 percent, idle time by 41 percent and emissions by 21 percent.  Interestingly, the adaptive traffic light system also reduced total accidents and fatal accidents by 13-36 %.

traffic accident artificial intelligence

Since events are unexpected and sometimes catastrophic, incorporating AI into a comprehensive sustainable traffic incident management system with smart traffic lights can transform traffic monitoring.  This is where the Hybrid Technology Alliance comes in.  Big data from IP cameras, GPS, cell phone tracking, detection vehicles, and loop detectors are combined to draw more precise inferences than when studying large amounts of information independently.  AI algorithms then continuously and instantaneously analyze the data, merging to detect potential events.

Traffic simulators can study archived and real-time data on when and where incidents occurred to analyze impacts.  AI models that predict accident duration can also indicate specific points that require attention and the overall impact on road sub-networks.  Additionally, deep learning models can explore correlations between intensity and overall impact, helping to prioritize incidents and their responses.  The integration of data analytics facilitates the testing of various traffic scenarios from which an effective, real-time, automated traffic incident response plan can be derived.

In Delhi, sensors from more than 7,500 CCTV cameras, programmed traffic lights and 1,000 LED signs collect real-time data, which AI processes into instant insights that authorities use to improve traffic management.  Data collected from smart cameras installed across the city in Milton Keynes , UK, was run on a deep learning model to predict traffic conditions 15 minutes ahead with 89% accuracy.

Simplify traffic management

To deliver on the promise of 5G, road and transport network management systems will also need to evolve over time.  Data from different sources is bound to be more complex.  The process by which all systems work together to respond universally and immediately requires precise implementation.  In technological adaptation, it is important that intelligent network decisions are autonomous and understandable.  This will provide room for human decision-making and intervention, as well as technology, when needed.  While we may have been a century since the world's first highway was built , it's only now that the world is starting to speed up the process.