Explore expert-written blogs on IT solutions, telecom trends, network infrastructure, ATS booth innovations, payroll management, and more — empowering businesses with knowledge and strategy.
Major streets of most popular cities around the world share one sight in common: the long tail of vehicles locked in traffic jams. The traditional traffic lights often fail to curb this problem which has necessitated research into new traffic management systems. Thanks to AI, some cities are beginning to overcome this problem. But how does an AI traffic management system work?
AI traffic management systems analyze historical and real-time traffic data to dynamically adjust the traffic signals. Unlike in traditional settings where the time it takes traffic lights to change is fixed, AI can adjust the timer to decongest a clogged section of the road. Advanced AI-powered traffic management systems can redirect cars to less jammed routes.
AI-powered traffic management systems are dynamic and will not only adapt to the changing traffic volume but can also identify potential crowding hotspots, detect road crashes that may cause traffic jams, and optimize traffic flow by tweaking traffic signals. Some of the key functionalities of AI in traffic management include:
While real-time routing has been extensively explored in traffic management systems, their applications extend beyond the roads. Several other companies have incorporated these systems to improve the efficiency of their operations. Here are a few industries that leverage real-time routing and optimization:
Ride-hailing and sharing services: ride-hailing apps like Lyft and Uber use real-time routing to match drivers to closest customers while also considering live traffic data. This helps to optimize pick-up and drop-off and lower waiting times.
Delivery logistics: companies that deal with delivering packages to customers including Amazon and UPS make use of real-time routing to adjust their delivery routes based on traffic patterns and deadlines.
Field service management: when field technicians receive service calls, they use optimized routes to reach the customer’s location. This can greatly improve service times and boost customer satisfaction.
Only a few cities across the globe have successfully implemented AI traffic management systems and it is not a surprise. The reason this system is not so popular is due to the complex nature of data required to train the system as well as the specialized infrastructural design that will allow it to operate effortlessly. There are two angles to the effective use of this system; the road and user angle. Here is a breakdown of the different parts required to set up and run this system.
The road component consists of installations that must be placed on the road to collect data and control traffic.
AI traffic management systems need both historical and real-time data to function efficiently. AI-powered traffic management systems are not different from other systems with AI at their core. The data quality used in its training will determine its accuracy in managing traffic or predicting overcrowding.
Vehicle and road condition detection is an important component of this system. Others are traffic cameras at major roads and intersections, weather data, GPS data from connected vehicles, and pedestrian counters.
All the different data coming from the cameras, sensors, and GPS of connected vehicles will go through real-time data processing pipelines. These pipelines include a mechanism for cleaning and harmonizing the data from the different sources.
The processed data is fed to trained machine learning (ML) algorithms like neural networks or regression for further analysis to identify traffic patterns. The result of this analysis is used to control the traffic lights to ensure the continuous flow of vehicles.
The machine learning algorithm sends back the message containing traffic patterns it found in the analysis to the traffic controls. This pattern is used to adjust the timing of the adaptive traffic signal in real-time.
Leveraging the data from the ML algorithm, adaptive traffic signals can coordinate the signals across different intersections. The dynamic signal adjustment can be based on real-time data or predicted congestion based on data from predictive models.
With the right historical data, ML algorithms for road traffic management can also be predictive. They can identify patterns in historical data and compare them with real-time data and conditions to predict congestion hotspots and respond to unforeseen incidents.
The driver component is the part of the AI traffic management system that communicates with the driver. For the system to work, the feedback mechanism must be designed in a way that drivers will easily understand them and see their benefits.
The AI based traffic management systems must be integrated with public transport systems (and where possible, private transport through navigation apps) to communicate with drivers. The system would provide the drivers with real-time route guidance.
This communication allows the system to tell the drivers when there is traffic congestion and suggest alternative routes. For this system to work, the drivers need to cooperate.
The system must have visualization tools where drivers and traffic managers can monitor the traffic condition in real-time. In addition to that, traffic managers need interactive dashboards to manually alter the reading of the dynamic traffic lights when necessary.
Another necessary feedback mechanism is the use of public displays on roadsides to inform drivers who may not be connected to the automated communications systems about the possibility of traffic jams and suggest alternative routes.
The benefits of using AI for traffic management are enormous. It is a win-win for traffic managers, road users, and the environment. Below are some of the ways AI proves its worth in traffic management.
Improved travel flow: by optimizing traffic flow and minimizing congestion, commuters reach their destinations faster, and productivity hours will not be lost sitting in traffic.
Enhance fuel efficiency: drivers often burn several liters of diesel sitting idly on the road or at intersections. Better traffic flow will lead to reduced fuel consumption.
Lower pollution: the longer diesel cars sit on the road, the more they will eject carbon monoxide and soot into the atmosphere. Pollution can be cut if cars get to their destinations faster.
Boost safety: through early detection of potential hazards, the system can notify appropriate authorities and make adjustments to traffic lights to improve safety.
Data quality and availability, infrastructure requirements, and complex urban environments remain some of the roadblocks on the path of expanding the reach of AI traffic management systems.
However, with the surge in the number of connected and autonomous vehicles on the road and the rise of smart cities, innovators can deepen AI communication between vehicles and the environment in real-time.
This communication between cars and traffic systems will help authorities to more efficiently monitor the road for potential hazards using the camera feed from other vehicles. This will also reduce the cost associated with mounting cameras on the streets.