Algorithmic and AI-based traffic management: The future of traffic management

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  Quantumrun Foresight
• January 27, 2022

Insight summary

As the global population and vehicle fleet expand, the potential for severe urban traffic congestion looms, prompting the exploration of solutions, such as AI-based traffic management systems. These systems use AI to monitor traffic conditions using various inputs like traffic cameras, road sensors, and mobile phone data, adjusting traffic lights in response to congestion and preventing accidents. The broader implications of such systems include improved national productivity through reduced traffic time, new job opportunities in tech sectors, enhanced data privacy, and the development of smarter cities.

Algorithmic and AI-based traffic management context

As the world’s vehicle fleet grows alongside the world’s ballooning population, new solutions are being pursued to head off a possible future of extreme urban traffic congestion. In 2015, the estimated number of motor vehicles in the world was 1.3 billion. The number is expected to increase to more than 2 billion in 2040, largely due to the growing affluence in developing economies.

This estimated increase in the number of motor vehicles may increase real-time road traffic across the developed and developing world. A potential solution to reduce or prevent real-time traffic congestion is algorithmic and artificial intelligence (AI)-based traffic management systems. Algorithmic and AI-based traffic management can help identify traffic violations such as running red lights, crossing lanes, reverse driving and lane-marking infractions.

AI-based traffic management is a system that uses artificial intelligence (AI) to monitor traffic conditions, predominantly with the use of traffic cameras and road sensors but increasingly, also the use of people’s mobile phones and in-vehicle connectivity. These traffic inputs send traffic information to a central control center where algorithms are used to estimate road traffic density. In response to the received information, the system alters the given municipality’s traffic lights to relieve real-time road congestions and avoid road accidents.

Disruptive impact

AI could lead to better traffic management through the implementation of traffic management systems (TMS). Several technologies can make TMS increasingly commonplace. For example, mobile network connection technology such as the 4G and upcoming 5G technologies may be seen as the linchpin to a successful implementation of these TMS. Another technology is edge computing, which can solve the latency challenges of limited volume transmission through cable-free connections. 

Future smart city solutions such as self-driving cars and buses, and product delivery through drones can change traffic conditions in cities. These smart city services can potentially help improve the traffic conditions experienced by city-dwellers since these transport innovations would deeply integrate with TMS’ and strictly adhere to traffic laws. 

With the application of AI solutions for traffic monitoring, road traffic conditions will improve and significant police resources can be freed up and deployed elsewhere. Further, AI-based traffic management systems could significantly enhance not just traffic conditions, but provide business and ecological advantages.

Several cities have started deploying intelligent traffic management systems (ITMS) to reduce traffic. For example, Pittsburgh in the US has deployed the intelligent traffic signal control system at 50 intersections, which has helped reduce travel times by 26 percent and intersection waiting times by 41 percent. Another example is the deployment of intelligent traffic management systems in Hangzhou, China, which dropped the city from the fifth most congested Chinese city to 57th on the national ranking.

Implications of algorithmic and AI-based traffic management

Wider implications of algorithmic and AI-based traffic management may include:

• Reduced total amount of time country populations spend in traffic, increasing national productivity metrics.


• Monitoring and managing human crowds in high-capacity entertainment and tourist locations.


• New job opportunities in data analysis, software development, and AI technology, while potentially displacing traditional jobs in traffic management and control.


• Increased collection and use of personal data, prompting governments to enact stricter data privacy laws to protect individuals' rights, which could result in a more secure digital environment and increased public trust.


• A more equitable distribution of transportation resources, potentially reducing socio-economic disparities by improving access to transportation in underserved areas.


• Significant cost savings for cities and municipalities through improved efficiency, which could be redirected towards other public services.


• A decrease in traffic congestion and accidents, improving public health outcomes by reducing pollution-related illnesses and injuries from accidents.


• The integration of AI-based traffic management systems can lead to the development of smarter, more connected cities, driving technological advancements in various sectors, such as telecommunications, transportation, and urban planning.

Questions to consider

• Do you think intelligent traffic management systems can curb vehicle emissions?


• What are the potential dangers of deploying intelligent traffic management systems?