AI in Mobility Market Size, Share, Growth, Trends 2024-2033

Artificial Intelligence (AI) in Mobility Market Size and Growth 2024 to 2033

The global artificial intelligence (AI) in mobility market size was valued at USD 8.83 billion in 2024 and is expected to hit around USD 337.27 billion by 2033. It is growing at a compound annual growth rate (CAGR) of 49.89% from 2024 to 2033.

The scope of artificial intelligence (AI) in mobility can be defined as the AI technologies which can be applied to transportations systems and vehicles in order to increase their effectiveness, safety and usability. These are the use of AI in operation such as auto-mobiles where they provide the AI for the route determining, decision making and real time object recognition. AI also facilitates better traffic prediction optimization, better ride-sharing services using dynamic planning, and richer V2X communication for better infrastructure. With the help of AI, the mobility sector has the following vision: intelligent, resource-responsive, and sustainable transportation.

• According to the INRIX global traffic scorecard in 2022, most urban areas and the country overall saw increasing travel demand in 2022. Across the globe, 58% of urban areas analysed saw increased traffic delays over last year, while 38% saw delay decreases. Wherein, London remained the most congested area analysed at 156 hours of delay per driver, up 5% over last year. Big movers include second ranked Chicago, IL (155 hours, up 49%), Boston, MA (134 hours, up 72%) and Toronto, ON (118 hours, up 59%). Traffic in many North American cities came roaring back in 2022 from 2021, a bit behind Europe.
• AI revolution in the automotive industry is shifting into high gear! With a forecast that 98% of new vehicles will be equipped with AI technology by 2025 and the global market set to rev up to $10.92 billion, the impact of artificial intelligence on cars is not just a fender bender—it’s a full-on transformation.
• AI and Deep Learning-based automotive apps can provide a wealth of useful car insights. Cameras and infrared sensors can precisely monitor the driver’s activity and send warning messages to help prevent accidents. Some of the primary areas of attention for driver behavior analytics include the identification of - IoT sensors can gather data on motorist speeds, fast bends, and rapid braking, among other things. This data may be continually evaluated to generate an impression of the driver’s conduct on the road.
• AI is being used to improve the overall driving experience for users. For example, automakers are developing AI-powered systems that can recognize and respond to driver preferences, adjusting things like seat position, temperature, and music based on previous behavior and data analysis.

Artificial Intelligence (AI) in Mobility Market Growth Factors

• Technological Advancements: Innovations in the AI technologies are becoming faster, thus changing the mobility sector by improving automation on vehicles, flow of traffic and user interface. Technological advancements in algorithm, structure and sight identification and in possession of data is enhancing the transportation systems. Such developments are instrumental in driving market expansion through adding value to the efficacy and durability of AI-enhanced mobility platforms while at the same time lowering the overall cost of operations.
• Growing Demand for Smart Mobility Solutions: The increasing requirement for ITS as well as smart structure and development is creating demand for the AI in mobility. Businesses and cities are looking for AI in such domains as self-driving cars, traffic management and predictive maintenance. This need is increasing the market as the stakeholders utilize enhanced AI solutions to enhance transport and city design.
• Emphasis on Automation and Efficiency: The trend to automate mobility services is pushing AI technologies’ uptake profoundly. Automated controls in vehicle and traffic systems, fleet and routes management, and even self-driving solutions are now actively replacing people. Such concentration on the automation of some essential tasks and operations creates a strong market trend that adapts to the organizational goal to improve transportation.
• Investment in Research and Development: Significant investments in AI research and development are driving innovation in the mobility sector. Companies and research institutions are focusing on developing new AI algorithms, enhancing system capabilities, and exploring novel applications. This emphasis on R&D is advancing AI technologies in mobility and expanding their range of applications, contributing to market growth.
• Strategic Partnerships and Collaborations: Collaborations between technology providers, automotive manufacturers, and governmental agencies are accelerating the integration of AI into mobility solutions. These strategic partnerships are fostering innovation and facilitating the development of advanced AI technologies tailored to specific mobility needs. The growing trend of joint ventures and alliances is enhancing the capabilities and deployment of AI in the mobility sector.

Artificial Intelligence (AI) in Mobility Market Trends

• Adoption of Advanced Algorithms: The integration of sophisticated algorithms, including neural networks and computer vision, is improving AI’s capabilities in mobility applications. These advanced algorithms enhance vehicle perception, navigation, and decision-making processes.
• Rise of Autonomous Vehicles: The development and deployment of autonomous vehicles are a significant trend in the AI in mobility market. These vehicles leverage AI to operate independently, improve safety, and enhance transportation efficiency.
• Increased Focus on Smart Traffic Management: AI technologies are being increasingly used to optimize traffic flow and reduce congestion. AI-driven solutions are enhancing the ability to manage traffic in real-time, improving overall urban mobility.
• Integration with Big Data Analytics: The synergy between AI and big data analytics is improving the ability to process and analyze transportation data. This integration enables more informed decision-making and enhances the efficiency of mobility systems.
• Expansion of AI-Powered Mobility Services: AI-powered services, such as ride-sharing and predictive maintenance, are becoming more prevalent. These services use AI to enhance user experiences, optimize operations, and provide actionable insights.

Report Scope

Artificial Intelligence (AI) in Mobility Market Dynamics

Drivers

The increasing need for smart transportation solutions and automation in mobility, along with significant investments in AI research and development, are key drivers of market growth. The demand for enhanced operational efficiency and user experiences is further propelling the adoption of AI technologies.

Restraints

High development costs and the complexity of integrating AI systems into existing mobility infrastructure pose challenges. Additionally, regulatory and ethical concerns surrounding AI in transportation can impact market growth.

Opportunities

Emerging markets and ongoing advancements in AI technology offer substantial growth opportunities. The development of innovative AI solutions and strategic partnerships within the mobility sector provide avenues for expanding market presence.

Challenges

Navigating regulatory frameworks and ensuring the seamless integration of AI technologies into existing systems are significant challenges. Maintaining high standards of accuracy, safety, and reliability while scaling up AI capabilities remains a critical concern for the industry.

Artificial Intelligence (AI) in Mobility Market Segmental Analysis

Technology Analysis

Machine Learning: Machine learning (ML) encompasses algorithms and statistical models that enable systems to learn from data and enhance their performance over time without explicit programming. In the context of AI in mobility, machine learning is pivotal for applications such as predictive analytics, where it processes historical data to forecast future trends and behaviors. For example, ML algorithms can analyze traffic patterns to optimize routing for vehicles, improve fuel efficiency, and enhance the overall driving experience. The continuous learning capability of ML also allows systems to adapt to changing environments, making them increasingly effective in real-time decision-making.

Natural Language Processing (NLP): Natural language processing is a branch of AI focused on enabling machines to understand, interpret, and respond to human language. In mobility applications, NLP enhances user interactions through voice recognition and conversational interfaces, allowing users to engage with navigation systems, ride-sharing platforms, and customer service chatbots more intuitively. By enabling vehicles to respond to voice commands, NLP improves the user experience, making it safer and more convenient for drivers to access information while on the road. The ongoing advancements in NLP are leading to more sophisticated and user-friendly mobility solutions.

Computer Vision: Computer vision is a field of AI that focuses on enabling machines to interpret and understand visual information from the world. In mobility, computer vision plays a crucial role in applications such as autonomous vehicles, where it processes data from cameras and sensors to detect obstacles, recognize traffic signs, and understand road conditions. This technology is essential for ensuring safety and reliability in self-driving systems, as it enables vehicles to make informed decisions based on their surroundings. Furthermore, computer vision is also utilized in driver assistance systems, providing real-time feedback to enhance driver awareness and reduce accidents.

Robotics: Robotics refers to the application of AI in designing and operating machines that can perform tasks autonomously or semi-autonomously. In the mobility sector, AI-driven robotics are increasingly used in applications such as autonomous delivery vehicles and drones for transporting goods. These systems leverage AI to navigate complex environments, avoid obstacles, and make real-time decisions. The integration of robotics in mobility enhances operational efficiency, reduces delivery times, and opens up new possibilities for last-mile logistics. As technology advances, the capabilities of robotic systems in mobility are expected to expand, further revolutionizing transportation and delivery services.

Sensor Fusion: Sensor fusion involves the integration of data from multiple sensors to improve the accuracy and reliability of information processing. In mobility applications, sensor fusion is critical for enhancing the performance of autonomous vehicles and smart transportation systems. By combining data from various sources, such as cameras, LiDAR, radar, and GPS, sensor fusion enables a comprehensive understanding of the vehicle's environment. This technology enhances object detection, obstacle avoidance, and navigation accuracy, leading to safer and more efficient mobility solutions. As sensor technology evolves, the effectiveness of sensor fusion will continue to play a vital role in the advancement of AI in mobility.

Application Analysis

Autonomous Vehicles: The application of AI technologies in autonomous vehicles focuses on developing and operating self-driving cars and trucks. AI systems process vast amounts of data from various sensors and cameras to navigate safely, make real-time decisions, and respond to changing road conditions. Key components include machine learning algorithms for perception and decision-making, computer vision for understanding the environment, and sensor fusion for integrating information from multiple sources. Autonomous vehicles aim to improve safety, reduce traffic congestion, and enhance mobility accessibility. As technology advances, the market for autonomous vehicles continues to expand, with significant investments from automotive manufacturers and tech companies.

Smart Transportation Systems: Smart transportation systems leverage AI applications to optimize traffic management, route planning, and public transportation efficiency. These systems analyse real-time data from various sources, including traffic cameras, sensors, and user inputs, to make informed decisions that enhance mobility. For example, AI can dynamically adjust traffic signals based on current traffic flow, reducing congestion and improving travel times. Additionally, smart transportation systems can provide real-time updates to users regarding public transit schedules and delays, improving the overall travel experience. The adoption of smart transportation systems contributes to sustainable urban mobility and supports the development of smart cities.

Fleet Management: AI solutions for fleet management focus on optimizing the operations of commercial vehicle fleets, including maintenance scheduling, route planning, and fuel efficiency analysis. By analysing data from vehicle telematics, AI can identify patterns related to driver behaviour, vehicle performance, and maintenance needs, enabling fleet operators to make informed decisions. Predictive analytics can forecast when vehicles require maintenance, reducing downtime and operational costs. Additionally, AI-powered systems can optimize routes to minimize fuel consumption and enhance delivery efficiency. As businesses increasingly recognize the benefits of AI in fleet management, this application continues to grow in importance.

Mobility-as-a-Service (MaaS): Mobility-as-a-Service (MaaS) refers to AI-driven platforms that integrate various transportation services into a single, accessible on-demand solution. MaaS platforms allow users to plan, book, and pay for multiple transportation modes, including public transit, ride-sharing, bike-sharing, and car rentals, through a single application. AI enhances MaaS by analysing user preferences and real-time data to provide personalized recommendations and optimize travel routes. By offering seamless and convenient transportation options, MaaS encourages the use of public and shared transport, contributing to reduced traffic congestion and environmental impact.

Predictive Maintenance: Predictive maintenance involves the use of AI applications to forecast vehicle maintenance needs, minimizing downtime and costs associated with unexpected repairs. By analysing data from sensors and telematics systems, AI can identify patterns that indicate potential failures or maintenance requirements. This proactive approach allows fleet operators and vehicle manufacturers to schedule maintenance at optimal times, improving operational efficiency and extending the lifespan of vehicles. Predictive maintenance is becoming increasingly important in the mobility sector, as businesses seek to reduce maintenance costs and enhance service reliability.

Deployment Mode Analysis

Cloud-based Solutions: Cloud-based solutions refer to AI applications hosted on cloud platforms, offering scalability and flexibility for businesses in the mobility sector. These solutions allow organizations to store and process vast amounts of data, enabling advanced analytics and machine learning capabilities. Cloud-based platforms facilitate real-time data sharing and collaboration among various stakeholders, including automotive manufacturers, public transportation providers, and logistics companies.

On-Premises Solutions: On-premises solutions involve AI systems deployed within an organization’s infrastructure, providing enhanced control and security over data and operations. This deployment mode is often preferred by businesses that require stringent data privacy and compliance measures, such as automotive manufacturers and logistics companies. On-premises solutions allow organizations to customize their AI applications to fit specific operational needs and maintain direct oversight of data management.

Artificial Intelligence (AI) in mobility Market Regional Analysis

The AI in mobility market is divided into key regions: North America, Europe, Asia-Pacific, and LAMEA (Latin America, Middle East, and Africa). Here’s a detailed overview of each region:

Why is North America leading in the AI in mobility market?

The AI in Mobility market in North America is highly advanced, led by the United States and Canada. The U.S. is a leader in deploying AI for autonomous vehicles, traffic management, and smart infrastructure. Canada complements this with innovations in smart mobility solutions and enhanced transportation systems. The region benefits from significant investments in AI technology, a strong tech ecosystem, and a focus on integrating AI into various mobility applications.

Europe play crucial role in AI in mobility market

Europe is a key player in the AI in Mobility market, with countries like Germany, France, and the UK leading in automotive innovation and smart city initiatives. The European market emphasizes regulatory compliance and sustainability, driving advancements in AI technologies for transportation and infrastructure. The region’s focus on high standards and strategic development supports the deployment of cutting-edge AI solutions in mobility.

Why Asia-Pacific experiencing rapid growth?

The Asia-Pacific region is experiencing rapid growth, driven by major players such as China, Japan, and India. This growth is fuelled by expanding infrastructure, increased adoption of AI technologies, and investments in autonomous vehicles and smart transportation systems. The region’s advancements in AI technology are aimed at improving traffic management, enhancing vehicle automation, and supporting smart city projects.

LAMEA AI in mobility market is in developing phase

LAMEA is developing, with growing interest in smart transportation solutions and infrastructure improvements. Brazil and South Africa are leading with investments in mobility technologies, while the Middle East is expanding its focus on AI-driven transportation systems despite economic and infrastructure challenges. The region shows considerable potential for growth, supported by rising investments and ongoing advancements in AI mobility solutions.

Artificial Intelligence (AI) in Mobility Market Top Companies

• Waymo
• Tesla, Inc.
• NVIDIA Corporation
• IBM Corporation
• Baidu, Inc.
• Intel Corporation
• Alphabet Inc.
• Microsoft Corporation
• Apple Inc.
• Aurora Innovation, Inc.
• Apple Inc.
• Uber Technologies, Inc.
• Qualcomm Technologies, Inc.
• Bosch Mobility Solutions
• Daimler AG

The artificial intelligence (AI) in mobility market is characterized by a diverse landscape of key players, ranging from established automotive manufacturers to innovative tech companies and startups. Major automotive manufacturers, such as Tesla, Toyota, Ford, and Volkswagen, are heavily investing in AI technologies to enhance their autonomous driving capabilities, develop advanced driver-assistance systems (ADAS), and improve overall vehicle performance. These companies leverage AI for applications like predictive maintenance, route optimization, and enhanced safety features.

CEO Statements

Elon Musk, CEO of Tesla: “AI is crucial for Tesla's mission to develop self-driving technology. Our AI systems continuously learn and adapt to improve the safety and performance of our autonomous vehicles."

John Krafcik, Former CEO of Waymo: “Our goal with Waymo is to make self-driving cars a reality and to ensure that they are safer and more efficient than human-driven vehicles. AI is at the heart of this mission, driving our innovation and development.”

Jensen Huang, CEO of NVIDIA: " AI and deep learning are transforming the automotive industry, from advanced driver assistance systems to fully autonomous vehicles. NVIDIA’s AI platforms are designed to accelerate this transformation.”

Artificial Intelligence (AI) in mobility Market Recent Developments

Strategic partnerships and Launches highlight the rapid advancements and collaborative efforts in the AI in mobility industry. Industry players are involved in various aspects of AI in mobility, including production, technologies, and innovation, and play a significant role in advancing the market. Some notable examples of key developments in the AI in mobility market include:

• In June 2023, Spare announced a collaboration with Uber, integrating Spare's cloud-based on-demand transit technology with Uber's large driver network. This agreement enables transit authorities to improve microtransit and paratransit services by leveraging Uber’s network. Large-scale deployments have already been successfully implemented in Pinellas Suncoast Transit Authority (PSTA) and Dallas Area Rapid Transit (DART), demonstrating the effectiveness of this collaboration. For PSTA, the partnership has enabled the launch of the PSTA Mobility on Demand (MOD) service on the Spare platform which allows ADA-eligible riders to book on-demand transportation whenever and wherever they need it.
• In June 2023, Geotab Inc. announced that it introduced generative AI models into its platform by setting a new machine learning model in the connected transportation industry. By increasing the standard for connected transportation by giving participating consumers easy access to on-demand information about the efficiency, sustainability, and performance of their vehicles.
• In May 2023, Valeo and DiDi Autonomous Driving announced a new strategic partnership and investment agreement. Valeo aims to make an investment in DiDi Autonomous Driving, and the two companies will collaborate to create intelligent safety solutions for L4 robotaxis. Valeo and DiDi Autonomous Driving will combine their expertise to provide passengers safe, dependable, comfortable, and cost-effective autonomous driving services. By investing in DiDi Autonomous Driving, Valeo hopes to expand on this strategic alliance and support the long-term development of a vital actor in the future of autonomous driving.

Market Segmentation

By Technology

• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Robotics
• Sensor Fusion

By Application

• Autonomous Vehicles
• Smart Transportation Systems
• Fleet Management
• Mobility-as-a-Service (MaaS)
• Predictive Maintenance

By Deployment Mode

• Cloud-based
• On-Premises

By Region

• North America
• APAC
• Europe
• LAMEA