AI in Traffic Management Market Size, Share and Trends Analysis, By Component (Hardware, Software, and Service), By Deployment (On-Premise and Cloud), By Technology, By Application, By End-Use (Logistics & Transportation Providers, Government Authorities, and Highway Operators), and By Region, Forecast 2025-2032

AI in Traffic Management Market Overview and Key Insights:

The AI in traffic management market size reached USD 20.65 billion in 2024 and is expected to register a revenue CAGR of 32.8% during the forecast period. AI has been instrumental in reinventing traffic control by allowing for real-time monitoring, dynamic signal modifications, and predictive analytics.

Intelligent Traffic Management Systems (ITMS) use AI technologies including machine learning, computer vision, and IoT devices to improve traffic flow, reduce congestion, and increase safety.

Market Drivers:

Increasing urbanization & traffic congestion is a key driver of revenue growth in the AI in traffic management market. According to the World Bank, 4.4 billion people, or 56% of the world’s population, reside in cities. By 2050, the urban population is predicted to have more than doubled from its current level, with approximately seven out of ten people living in cities. Urbanization, which accounts for more than 80% of global GDP, has the potential to contribute to long-term growth through higher productivity and innovation if properly managed.

Urban land consumption is outpacing population increase by up to 50%, resulting in the addition of 1.2 million km² of new built-up area globally by 2030. Urban land consumption is outpacing population increase by up to 50%, resulting in the addition of 1.2 million km² of new built-up area globally by 2030.

Advancements in AI & IoT is another key driver of revenue growth in the AI in traffic management market. On November 2024, Broadcom unveiled VeloRAIN, an industry first for robust AI networking beyond the data center. VeloRAIN will add new intelligence capabilities for identifying encrypted application traffic that was previously unparsable by network optimization technologies. 

Market Opportunity:

Rising adoption of autonomous vehicles acts as an opportunity for the AI in traffic management market. Autonomous vehicles (AVs) use technology to partially or completely replace the human driver in driving a vehicle, avoiding road hazards, and responding to traffic. One study discovered that when eco-driving, platooning, intersection connection, and quicker highway speeds are considered direct consequences of AVs, energy consumption and GHG emissions can be decreased by 9%.

Furthermore, the United Nations Economic Commission for Europe has a rule on automated lane-keeping systems that governs the implementation of L3 AD at speeds of up to 60 km/h. In addition, the UN’s World Forum for Harmonization of Vehicle Regulations (WP.29) is developing further regulations for using AD capabilities at higher speeds. This group intends to expand the usage of advanced autonomous systems to speeds of up to 130 kilometers per hour, with the rule going into effect in 2023.

Recent Trends:

Emerging trends include real-time traffic monitoring, collision detection and avoidance systems, predictive maintenance, and big data analytics.

As technology evolves, it addresses difficulties across multiple markets, resulting in a positive influence. AI is transforming traffic management by making roadways smarter, safer, and more efficient. The use of AI and machine learning algorithms to evaluate vast amounts of data from vision systems and sensors has made modern data processing systems like edge and cloud computing critical for intelligent traffic management. To reduce traffic congestion, AI allows signal-to-vehicle interaction and dynamic city planning, resulting in smooth traffic flow at signals.

On April 2024, Umovity and Derq, an industry-leading AI Intelligent Transportation System (ITS) supplier, announced their cooperation and first joint solution at the ITS America Conference & Expo in Phoenix, Arizona. By combining Derq’s INSIGHT, the company’s automated safety performance monitoring application, with Econolite’s industry-leading cloud-based Advanced Transportation Management System (ATMS), Centracs Mobility, the two companies provide a robust traffic management solution that employs Artificial Intelligence (AI) to improve safety for all drivers.

Restraints & Challenges:

High implementation & maintenance costs and data privacy & cybersecurity concerns are key limiting factors for growth of the market. Implementing AI in transportation necessitates a considerable initial investment in advanced sensors, software, and hardware. Additionally, corporations must engage experienced individuals to manage these systems, which increases operational costs. These hefty expenditures can be prohibitive for small businesses and communities. Furthermore, AI systems collect massive volumes of data, which includes sensitive personal and operational information. This raises serious issues about data privacy and security, as these systems are susceptible to hackers. A data breach could compromise passenger safety, disrupt transportation networks, and lead to loss of public trust.

Component Segment Insights and Analysis:

Based on the component, the AI in traffic management market is segmented into hardware, software, and service.

Hardware segment contributed the largest market revenue share in 2024. AI in transportation is heavily reliant on data gathered from a variety of sources, including connected vehicles, traffic cameras, and smartphone apps. Adaptive Traffic Control Systems (ATCS) use AI algorithms and real-time traffic data to dynamically change traffic light timings. Cameras and sensors monitor traffic flow, and AI uses this information to improve signal sequences, lowering congestion and wait times at crossings.

Parking scarcity is a major factor in traffic congestion. Artificial intelligence-powered parking management systems use sensors and cameras to detect available parking spaces in real-time. Drivers can then utilize mobile apps to find available parking, saving time and reducing gridlock generated by circling for places. On August 2024, NoTraffic, the developer of the world’s premier mobility platform, has announced a cooperation with SWARCO McCain, a long-time industry pioneer in breakthrough Intelligent Transportation Systems (ITS). 

Software segment contributed the highest market growth rate in 2024. AI can assist transportation businesses in monitoring the health of their cars, trains, and aircraft by analyzing sensor data and detecting anomalies, much as it does in smart factories. Predictive maintenance enables operators to detect possible issues before they occur, minimizing downtime and increasing safety. Similarly, predictive maintenance is being used for road maintenance. 

On November 2024, Transoft Solutions, a Canadian firm, has acquired a Brisbane-based software startup that uses artificial intelligence to improve road safety and traffic management. UniQuest, UQ’s commercialisation arm, founded the Advanced Mobility Analytics Group (AMAG) in 2020 to commercialize intellectual property from UQ, the University of British Columbia, and Queensland University of Technology. 

Technology Segment Insights and Analysis:

Based on the technology, the AI in traffic management market is segmented into Machine Learning (ML), computer vision, Internet of Things (IoT), Natural Language Processing (NLP), and Others.

Machine Learning (ML) segment contributed the largest market revenue share in 2024. Modern smart cities use machine learning and deep learning to improve traffic management by streamlining daily tasks and increasing productivity. To reduce traffic congestion, a multi-agent reinforcement learning-based urban traffic route guidance method that consists of two algorithms—modular Q-learning (MQ) and trajectory A Rejection—that has a high adaptive learning capability in different traffic scenarios can be employed.

On January 2025, researchers at the National Institute of Technology (NIT) in Rourkela have created an AI-based Multi-Class Vehicle Detection (MCVD) model and a Light Fusion Bi-Directional Feature Pyramid Network (LFBFPN) tool to improve traffic management in developing countries. The researchers created the new MCVD model, which employs a Video Deinterlacing Network (VDnet) to efficiently extract critical elements from traffic photos, even when cars fluctuate in size and form.

Application Segment Insights and Analysis:

Based on the application, the AI in traffic management market is segmented into traffic signal control systems, incident detection & management, route optimization, public transport management, and predictive analytics.

Traffic signal control systems segment contributed the largest market share in 2024. The AI-based traffic light control system is an intelligent embedded system that uses computer vision to estimate the density of cars in each lane of a traffic crossroads to create adaptive durations for green and red traffic lights in each lane. Predictive analytics powered by artificial intelligence improve road safety by assessing historical and current data while taking weather conditions into account. Adaptive traffic signals anticipate risks, change timings, and improve responsiveness to ensure safer urban traffic management.

On September 2023, Peachtree Corners, one of the nation’s first smart cities in Israel powered by real-world connected infrastructure and 5G, has announced a partnership with ITC Intelligent Traffic Control Ltd (ITC), which will provide an agnostic software solution that can be applied to existing traffic infrastructure to measure traffic and predict and mitigate congestion before it occurs, resulting in a more safe, sustainable, and efficient travel experience. 

Predictive analytics segment is expected to have the highest growth rate throughout the forecasted period. Traffic prediction is vital in reducing traffic congestion, which is a major issue worldwide, with negative implications such as lost hours of travel time and higher fuel usage. Integrating developing technologies into transportation systems greatly improves traffic forecast and generates new research concerns.

Cities such as Singapore, London, and Barcelona have successfully deployed real-time traffic management systems, resulting in better traffic flow and lower emissions. Autonomous vehicles and smart infrastructure will drive further breakthroughs in traffic management, relying primarily on real-time data and predictive technology to ensure efficient urban mobility and sustainable city development.

Geographical Outlook:

AI in traffic management market is strategically segmented by geography to provide a comprehensive understanding of regional market dynamics. Discover demand analysis, emerging trends, and growth opportunities shaping market performance across different regions and countries.

Asia Pacific AI in Traffic Management Market:

Asia Pacific is registered to have highest market share in AI in traffic management market in 2024. Asia’s urbanization development occurs at a time when natural resources are becoming increasingly scarce and consumption outpaces production. The current unsustainable use of resources comes at a cost, with resource extraction and processing accounting for more than 90% of global biodiversity loss and water stress. Other attempts include increasing the use of remote sensors to monitor pollution and implementing a waste-charging plan that encourages Hong Kong residents to recycle and reuse rather than relying on landfills.

On February 2024, Medianets Lab in Hungary announced that it is working on a traffic congestion forecasting and control system that will employ artificial intelligence (AI) to regulate traffic signals. The project, known as Multi-Input Deep Learning for Congestion Prediction and Traffic Light Control – Tralico, is being developed in collaboration with Istanbul IT and Smart City Technologies in Turkey and the Nara Institute of Science and Technology in Japan. 

North America AI in Traffic Management Market:

North America is expected to register the fastest growth rate during the forecasted period. IoT is converting traffic management on smart roads into intelligent, adaptive systems that increase road safety, reduce congestion, and lessen environmental impact. Smart highways may use real-time data from sensors, cameras, and connected vehicles to make informed judgments about traffic flow, foresee problems, and respond to changing conditions with unparalleled efficiency. 

On January 2025, Ouster, Inc., a leading provider of high-performance lidar sensors for the automotive, industrial, robotics, and smart infrastructure industries, announced that it has been awarded a two-million-dollar contract to implement its Ouster BlueCity traffic management solution in Chattanooga, Tennessee, to improve roadway safety and reduce congestion. The initiative will use digital lidar sensors and edge AI at each intersection to regulate traffic flow, detect and analyze safety events, and detect vehicle-to-everything (V2X) communications.

Europe AI in Traffic Management Market:

Europe is to register a considerable market share during the forecasted period. The UN predicts that urban populations in Europe will reach 80% by 2050, up from roughly 70% currently. Traffic congestion is a daily issue for many individuals in the EU, highlighting the importance of efficient mobility management in urban areas. Research indicates that efficient traffic flow promotes urban economic growth. It also indicates that reducing commute times by 10% can increase productivity by 2.9%. In congested areas, free-flowing traffic can result in up to 30% productivity benefits.

Artificial intelligence (AI) is developing as a critical technology for the future of traffic safety and administration. While fully autonomous vehicles may be a long way off, they are already being used in other domains. Hull City Council in the United Kingdom launched a pilot initiative in March 2024 to employ artificial intelligence to manage traffic lights with the goal of alleviating traffic congestion during peak hours. The trial was successful, with findings indicating that journey times were decreased by 16.9% during peak morning hours and 8% during peak evening hours.

Competition Analysis:

The AI in traffic management market is characterized by a fragmented structure, with several players competing across various segments and regions. List of major players included in the AI in traffic management market report are:

• SmartCow
• Talentelgia Technologies Pvt Limited
• Videonetics
• Derq Inc.
• INRIX
• Advantech Co., Ltd.
• Vehant Technologies
• IBM
• FLOW LABS
• Neural Labs
• Glen Traffic
• Intelligent Traffic Control Ltd (ITC)
• Rekor Systems, Inc.
• Lanner Electronics Inc
• NOTRAFFIC

Strategic Developments in AI in Traffic Management Market:

• In October 2024, Nayan Tech stated that company has raised USD 2 million in a Pre-Series A fundraising round. BEENEXT led the investment, with participation from We Founder Circle, Venture Catalysts, Lets Venture, and FAAD Capital. This funding boost would help Nayan Tech grow its operations and offer AI and IoT-powered solutions in India and around the world. Nayan Tech collects large amounts of data from vehicles, public transportation, and delivery fleets using dashcams, drones, and smart mobile apps.
• In July 2024, ISS (Intelligent Security Systems), a global leader in video intelligence and data awareness systems, has introduced Intellisection, a new turnkey solution for automated intersection surveillance. Intellisection uses artificial intelligence and powerful neural network training technology to improve road safety and efficiency, allowing federal, state, and municipal authorities to collaborate on a comprehensive traffic management system.
• In March 2024, L&T Technology Services Limited, a leading worldwide digital engineering, and R&D services provider, has announced a collaboration with Intel Corporation to develop and provide scalable edge-AI solutions for a variety of use cases, including Cellular Vehicle-to-Everything (CV2X) applications. 

Key Advantages for Stakeholders:

Navistrat Analytics’ industry report provides an in-depth quantitative analysis of various market segments, historical and current trends, market forecasts, and dynamics within the global market. The historical years covered in this report are 2022 to 2023, with 2024 serving as the base year for market size calculations. The forecast period extends from 2025 to 2032.

The report includes an executive summary and a comprehensive overview of market drivers, restraints, opportunities, and challenges (DROC), along with insights into regulatory standards. It features detailed analyses such as PORTER’s Five Forces, SWOT, and PESTLE, as well as assessments of technological trends and the competitive landscape.

PORTER’s Five Forces analysis helps stakeholders evaluate the impact of new entrants, competitive rivalry, supplier power, buyer power, and substitution threats, enabling them to assess the level of competition and the attractiveness of the global market. The competitive landscape provides stakeholders with a clear understanding of the current market positions of key players, offering valuable insights into their competitive environment.

Scope And Key Highlights Of The AI in Traffic Management Market Report:

The AI in Traffic Management market report offers a detailed analysis of market size, including historical revenue (in USD Billion) data for 2022-2023 and revenue forecasts for 2025-2032 across the following segments:

• Component Outlook (Revenue, USD Billion; 2022-2032)
  • Hardware
  • Software
  • Service

• Deployment Outlook (Revenue, USD Billion; 2022-2032)
  • On Premise
  • Cloud-based

• Technology Outlook (Revenue, USD Billion; 2022-2032)
  • Machine Learning (ML)
  • Computer Vision
  • Internet of Things (IoT)
  • Natural Language Processing (NLP)
  • Others

• Application Outlook (Revenue, USD Billion; 2022-2032)
  • Traffic Signal Control Systems
  • Incident Detection & Management
  • Route Optimization
  • Public Transport Management
  • Predictive Analytics

• End-Use Outlook (Revenue, USD Billion; 2022-2032)
  • Logistics & Transportation Providers
  • Government Authorities
  • Highway Operators’

• Regional Outlook (Revenue, USD Billion; 2022-2032)
  • North America
    • U.S.
    • Canada
    • Mexico
  • Europe
    • Germany
    • France
    • U.K.
    • Italy
    • Spain
    • Benelux
    • Nordic Countries
    • Rest of Europe
  • Asia Pacific
    • China
    • India
    • Japan
    • South Korea
    • Oceania
    • ASEAN Countries
    • Rest of APAC
  • Latin America
    • Brazil
    • Rest of LATAM
  • Middle East & Africa
    • GCC Countries
    • South Africa
    • Israel
    • Turkey
    • Rest of MEA

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