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    AI in Transportation Market

    ID: MRFR/ICT/5210-CR
    128 Pages
    Ankit Gupta
    January 2024

    AI in Transportation Market Research Report Information by Offering (Hardware {CPU, GPU, Sensors, Others}, Services, Software {AI Platforms, AI Solutions}), By IoT Communication Technology (Cellular, LPWAN, LoRaWAN, Z-Wave, Zigbee, NFC, Bluetooth, Others), By Application (Autonomous Truck, Semi-autonomous Truck, Truck Platooning, Human-Machine Interface (HMI), Predictive Maintenance, Precision & Mapping, Traffic Detection, Computer Vision-Powered Parking Management, Road Condition Monitoring, Automatic Traffic Incident Detection, Driver ...

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    AI in Transportation Market Summary

    As per MRFR analysis, the AI in Transportation Market Size was estimated at 2745.6 USD Million in 2024. The AI in Transportation industry is projected to grow from 3035.04 USD Million in 2025 to 8268.73 USD Million by 2035, exhibiting a compound annual growth rate (CAGR) of 10.54 during the forecast period 2025 - 2035.

    Key Market Trends & Highlights

    The AI in Transportation Market is experiencing robust growth driven by technological advancements and increasing demand for safety and efficiency.

    • The adoption of autonomous vehicles is witnessing a notable increase, particularly in North America, which remains the largest market.
    • Enhanced traffic management systems are being implemented to optimize urban mobility, especially in the rapidly growing Asia-Pacific region.
    • The software segment continues to dominate the market, while the hardware segment is emerging as the fastest-growing area due to rising technological innovations.
    • Key market drivers include the growing demand for safety and efficiency, alongside the integration of smart infrastructure, which is propelling advancements in AI applications.

    Market Size & Forecast

    2024 Market Size 2745.6 (USD Million)
    2035 Market Size 8268.73 (USD Million)
    CAGR (2025 - 2035) 10.54%

    Major Players

    Waymo (US), Tesla (US), Cruise (US), Aurora (US), Mobileye (IL), Baidu (CN), Nuro (US), Zoox (US), Pony.ai (CN)

    AI in Transportation Market Trends

    The AI in Transportation Market is currently experiencing a transformative phase, driven by advancements in machine learning, data analytics, and automation technologies. This sector appears to be evolving rapidly, with various stakeholders, including automotive manufacturers, logistics companies, and public transportation agencies, increasingly adopting AI solutions to enhance operational efficiency and improve safety. The integration of AI technologies into transportation systems seems to facilitate smarter traffic management, predictive maintenance, and autonomous vehicle development, thereby reshaping the landscape of mobility. Furthermore, the growing emphasis on sustainability and reducing carbon footprints may further propel the adoption of AI-driven solutions, as organizations seek to optimize routes and minimize energy consumption. In addition, the AI in Transportation Market is likely to witness a surge in investment as governments and private entities recognize the potential benefits of these technologies. Collaborations between tech firms and traditional transportation providers appear to be on the rise, fostering innovation and accelerating the deployment of AI applications. As the market continues to mature, it may also face challenges related to regulatory frameworks and public acceptance, which could influence the pace of adoption. Overall, the future of the AI in Transportation Market seems promising, with numerous opportunities for growth and development across various segments.

    Increased Adoption of Autonomous Vehicles

    The trend towards autonomous vehicles is gaining momentum, as manufacturers and tech companies invest heavily in research and development. This shift is likely to enhance road safety and reduce traffic congestion, as AI systems can optimize driving patterns and improve decision-making in real-time.

    Enhanced Traffic Management Systems

    AI technologies are being integrated into traffic management systems to analyze data from various sources. This integration may lead to improved traffic flow, reduced travel times, and better resource allocation, ultimately enhancing the overall efficiency of urban transportation networks.

    Predictive Maintenance and Fleet Management

    The use of AI for predictive maintenance is becoming increasingly prevalent in fleet management. By analyzing data from vehicles, companies can anticipate maintenance needs, reduce downtime, and extend the lifespan of their assets, thereby optimizing operational efficiency.

    The integration of artificial intelligence in transportation systems is poised to enhance operational efficiency and safety, while simultaneously addressing the growing demand for sustainable mobility solutions.

    U.S. Department of Transportation

    AI in Transportation Market Drivers

    Integration of Smart Infrastructure

    The AI in Transportation Market is witnessing a significant trend towards the integration of smart infrastructure. Cities and municipalities are increasingly adopting AI technologies to optimize traffic flow, reduce congestion, and enhance public transportation systems. Smart traffic signals, equipped with AI algorithms, can adapt in real-time to changing traffic conditions, thereby improving overall efficiency. Data indicates that cities implementing smart infrastructure can experience a reduction in traffic delays by approximately 20 percent. This integration not only enhances the user experience but also contributes to environmental sustainability by reducing emissions. As urbanization continues to rise, the demand for smart infrastructure solutions is expected to propel the AI in Transportation Market.

    Growing Demand for Safety and Efficiency

    The AI in Transportation Market is experiencing a notable surge in demand for enhanced safety and operational efficiency. As transportation systems become increasingly complex, stakeholders are prioritizing technologies that can mitigate risks and improve safety outcomes. AI-driven solutions, such as advanced driver-assistance systems (ADAS), are being integrated into vehicles to reduce accidents and enhance overall safety. According to recent data, the implementation of AI technologies in transportation has the potential to decrease road fatalities by up to 30 percent. This growing emphasis on safety is likely to drive investments in AI solutions, as companies seek to comply with regulatory standards and meet consumer expectations for safer travel.

    Advancements in Machine Learning Algorithms

    The AI in Transportation Market is significantly influenced by advancements in machine learning algorithms. These algorithms are becoming increasingly sophisticated, enabling more accurate predictions and decision-making processes in transportation systems. For instance, machine learning can analyze vast amounts of data from various sources, such as traffic patterns and weather conditions, to optimize routing and scheduling for logistics companies. This capability is particularly crucial as the logistics sector is projected to grow by 4.5 percent annually. The ability to leverage machine learning for predictive analytics is likely to enhance operational efficiency and reduce costs, thereby driving further adoption of AI technologies in transportation.

    Emergence of Electric and Autonomous Vehicles

    The AI in Transportation Market is significantly shaped by the emergence of electric and autonomous vehicles. As the automotive industry shifts towards electrification and automation, AI technologies play a crucial role in enabling these advancements. Electric vehicles (EVs) equipped with AI systems can optimize energy consumption and enhance driving performance. Furthermore, the development of fully autonomous vehicles relies heavily on AI for navigation, obstacle detection, and decision-making. The market for electric and autonomous vehicles is projected to grow at a compound annual growth rate (CAGR) of 20 percent over the next decade. This trend indicates a robust future for AI in transportation, as these vehicles require sophisticated AI systems to operate safely and efficiently.

    Rising Investment in Smart Mobility Solutions

    The AI in Transportation Market is benefiting from a rising investment in smart mobility solutions. Governments and private entities are increasingly allocating funds towards the development of innovative transportation systems that leverage AI technologies. This investment is aimed at creating more efficient, sustainable, and user-friendly transportation options. For example, the market for smart mobility solutions is expected to reach USD 200 billion by 2026, driven by the need for improved urban mobility. As cities grapple with congestion and pollution, the push for smart mobility solutions is likely to accelerate the adoption of AI in transportation, fostering a more integrated and efficient transportation ecosystem.

    Market Segment Insights

    By Offering: Software (Largest) vs. Hardware (Fastest-Growing)

    In the AI in Transportation Market, the offering segment is primarily composed of software, hardware, and services, with software occupying the largest market share. Software solutions are integrated extensively into transportation systems for autonomous driving, route optimization, and predictive maintenance. Hardware, while currently smaller in market share, is rapidly gaining traction due to advancements in sensor technologies and AI-enabled devices. Services, which encompass consulting and managed services, play a crucial role in the integration and deployment of AI systems in transportation.

    Software (Dominant) vs. Hardware (Emerging)

    Software solutions in the AI in Transportation Market dominate by providing critical functionalities necessary for modern transportation systems. These include real-time data analytics, decision-making processes, and machine learning algorithms tailored for transportation needs. On the other hand, hardware is emerging as a vital component, driven largely by the increasing demand for innovative technologies such as advanced sensors and IoT devices. This synergy between hardware and software is essential for transformative changes in the industry, enabling higher efficiency, safety, and intelligence in transportation systems.

    By IoT Communication Technology: Cellular (Largest) vs. LPWAN (Fastest-Growing)

    In the AI in Transportation Market, IoT communication technologies are pivotal for real-time data transmission and connectivity. Cellular communication leads the segment, leveraging established infrastructure to provide extensive coverage and reliability for transportation applications. LPWAN, while smaller in current market share, has emerged as a significant contributor, particularly for low-power, long-range applications such as asset tracking and fleet management. The growth of IoT communication in transportation is driven by the increasing demand for connected vehicles and smart logistics solutions. Factors such as advancements in 5G technology enhance cellular capabilities, while the expanding adoption of LPWAN solutions addresses the need for cost-effective, energy-efficient communication for large-scale IoT deployments. The combination of these technologies is expected to reshape transportation networks fundamentally.

    Communication Technology: Cellular (Dominant) vs. LPWAN (Emerging)

    Cellular communication technology stands out in the AI in Transportation Market due to its high reliability, strong bandwidth capabilities, and widespread adoption. This technology is dominant, allowing for seamless connectivity in various transportation applications, including vehicle telematics and smart traffic management systems. In contrast, LPWAN represents an emerging solution focused on long-range, low-power communications ideal for devices that require infrequent data transmission. Its ability to operate on minimal energy makes it suitable for applications like remote sensor networks and logistics tracking. While cellular technology is currently more prevalent, LPWAN's unique attributes position it as a compelling alternative for specific use cases, leading to its rapid adoption in the coming years.

    By Application: Autonomous Truck (Largest) vs. Predictive Maintenance (Fastest-Growing)

    Within the AI in Transportation Market, the application segment is notably led by Autonomous Trucks, which represent the largest share. This section of the market showcases substantial investment and development, cementing its key role in transforming logistics and freight operations worldwide. Following closely is Predictive Maintenance, which emerges as the fastest-growing value in this segment as companies increasingly understand the value of using AI to anticipate maintenance needs and reduce downtime.

    Autonomous Trucks (Dominant) vs. Predictive Maintenance (Emerging)

    Autonomous Trucks dominate the AI in Transportation application segment, representing a breakthrough in supply chain efficiency and safety. These self-driving vehicles incorporate sophisticated AI algorithms to navigate roadways, significantly reducing human error and improving fuel efficiency. On the other hand, Predictive Maintenance is rapidly emerging as a crucial component of transportation operations, leveraging AI's capacity to analyze data and foresee equipment failures. This proactive approach not only cuts operational costs but also enhances vehicle longevity and overall safety. As technology advances, these two applications are expected to shape the future landscape of the transportation market.

    By Machine Learning Technology: Deep Learning (Largest) vs. Computer Vision (Fastest-Growing)

    In the AI in Transportation Market, the machine learning technology segment is predominantly driven by Deep Learning, which holds the largest market share among the various technologies. Deep Learning is extensively applied in various transportation applications such as autonomous vehicles, traffic management systems, and smart transportation networks. Computer Vision, while not as large as Deep Learning, is recognized for its rapid growth, employing image and video analysis to enhance navigation, monitoring, and safety initiatives in transportation. The growth trends in this segment are propelled by advancements in hardware capabilities, increasing amounts of transport-related data, and the rising demand for automation and smart technologies in the transportation sector. The emphasis on safety, efficiency, and real-time data processing among transportation entities is paving the way for Machine Learning technologies, with Natural Language Processing and Context Awareness also contributing to broaden this evolving landscape as they enable more intuitive interactions and decision-making processes in transportation systems.

    Technology: Deep Learning (Dominant) vs. Computer Vision (Emerging)

    Deep Learning stands as the dominant technology in the AI in Transportation Market, characterized by its ability to process massive datasets and improve decision-making in autonomous systems and predictive analytics. It provides an edge in complicated environments, successfully managing sensory data for real-time application. Computer Vision, recognized as an emerging technology in this sector, focuses on the interpretation of visual data from the vehicles and surroundings. Its rapid development leads to innovations in safety systems, traffic monitoring, and logistical operations, distinguishing it as a vital component in advancing smart transport solutions. Both technologies are interlinked; however, the depth and versatility of Deep Learning afford it a leading position, while Computer Vision lays the groundwork for more integrated visual analytics.

    Get more detailed insights about AI in Transportation Market

    Regional Insights

    North America : Innovation and Leadership Hub

    North America is the largest market for AI in transportation, holding approximately 45% of the global share. The region benefits from robust technological infrastructure, significant investments in R&D, and a favorable regulatory environment. The demand for autonomous vehicles and smart transportation solutions is driven by urbanization and the need for efficient logistics. Regulatory support, including safety standards and funding for AI initiatives, further catalyzes growth. The United States leads the market, with key players like Waymo, Tesla, and Cruise spearheading innovation. Canada also plays a significant role, focusing on AI research and development. The competitive landscape is characterized by a mix of established automotive giants and emerging startups, fostering a dynamic ecosystem that encourages collaboration and technological advancements.

    Europe : Regulatory Framework and Innovation

    Europe is the second-largest market for AI in transportation, accounting for about 30% of the global share. The region's growth is propelled by stringent regulations aimed at enhancing road safety and reducing emissions. Initiatives like the European Green Deal and investments in smart city projects are driving demand for AI solutions in transportation. Countries are increasingly adopting policies that support the integration of AI technologies into public transport systems. Germany, France, and the UK are at the forefront of this market, with significant investments in autonomous vehicle technology and smart infrastructure. The competitive landscape includes major automotive manufacturers and tech companies, such as BMW and Volkswagen, collaborating with startups to innovate. The presence of regulatory bodies ensures that safety and ethical considerations are prioritized in AI deployment.

    Asia-Pacific : Emerging Powerhouse in AI

    Asia-Pacific is witnessing rapid growth in the AI in transportation market, holding approximately 20% of the global share. The region's expansion is driven by increasing urbanization, rising disposable incomes, and government initiatives promoting smart transportation solutions. Countries like China and Japan are investing heavily in AI technologies to enhance public transport efficiency and reduce traffic congestion. Regulatory frameworks are evolving to support the safe deployment of autonomous vehicles. China is a key player, with companies like Baidu and Pony.ai leading the charge in autonomous driving technology. Japan also contributes significantly, focusing on integrating AI into existing transport systems. The competitive landscape is marked by a mix of local startups and established firms, fostering innovation and collaboration across the region. The growing demand for smart logistics solutions further fuels market growth.

    Middle East and Africa : Resource-Rich Frontier for AI

    The Middle East and Africa region is emerging as a frontier for AI in transportation, holding about 5% of the global market share. The growth is driven by increasing investments in infrastructure and smart city initiatives, particularly in countries like the UAE and South Africa. Governments are recognizing the potential of AI to enhance transportation efficiency and safety, leading to supportive regulatory frameworks. The demand for innovative transportation solutions is rising as urban populations grow. The UAE is a leader in adopting AI technologies, with initiatives aimed at integrating autonomous vehicles into public transport. South Africa is also making strides, focusing on improving logistics and public transport systems. The competitive landscape includes both local and international players, with a focus on collaboration to drive technological advancements in the region.

    Key Players and Competitive Insights

    The AI in Transportation Market is currently characterized by a dynamic competitive landscape, driven by rapid technological advancements and increasing demand for autonomous solutions. Major players such as Waymo (US), Tesla (US), and Mobileye (IL) are at the forefront, each adopting distinct strategies to enhance their market positioning. Waymo (US) focuses on extensive testing and partnerships with ride-hailing services, while Tesla (US) emphasizes its proprietary Full Self-Driving (FSD) technology, aiming for widespread consumer adoption. Mobileye (IL), on the other hand, leverages its expertise in computer vision to provide advanced driver-assistance systems (ADAS) to various automotive manufacturers, thereby diversifying its operational focus. Collectively, these strategies contribute to a competitive environment that is increasingly centered around innovation and technological integration.

    In terms of business tactics, companies are increasingly localizing manufacturing and optimizing supply chains to enhance efficiency and reduce costs. The market structure appears moderately fragmented, with several key players vying for dominance. This fragmentation allows for a variety of approaches to emerge, as companies seek to carve out niches within the broader landscape of AI in transportation. The collective influence of these key players is significant, as they not only drive technological advancements but also set industry standards that others may follow.

    In August 2025, Waymo (US) announced a strategic partnership with a major logistics company to integrate its autonomous vehicles into last-mile delivery services. This move is indicative of Waymo's intent to diversify its applications beyond passenger transport, potentially capturing a lucrative segment of the logistics market. The partnership may enhance operational efficiencies and reduce delivery times, thereby positioning Waymo as a leader in the autonomous delivery space.

    In September 2025, Tesla (US) unveiled its latest iteration of the Full Self-Driving software, which includes enhanced safety features and improved navigation capabilities. This update is crucial as it not only reinforces Tesla's commitment to innovation but also addresses regulatory concerns regarding safety in autonomous driving. By continuously improving its technology, Tesla aims to maintain its competitive edge and foster consumer trust in its autonomous solutions.

    In July 2025, Mobileye (IL) expanded its collaboration with several automotive manufacturers to integrate its advanced driver-assistance systems into new vehicle models. This strategic expansion is significant as it allows Mobileye to solidify its market presence and leverage its technology across a broader range of vehicles. By aligning with multiple manufacturers, Mobileye enhances its influence in the market and positions itself as a key player in the transition towards fully autonomous vehicles.

    As of October 2025, current competitive trends in the AI in Transportation Market are heavily influenced by digitalization, sustainability, and the integration of AI technologies. Strategic alliances are increasingly shaping the landscape, as companies recognize the value of collaboration in driving innovation. Looking ahead, it appears that competitive differentiation will evolve, with a shift from traditional price-based competition towards a focus on technological innovation, reliability in supply chains, and the ability to adapt to changing consumer demands. This evolution suggests that companies that prioritize innovation and strategic partnerships will likely emerge as leaders in the AI in Transportation Market.

    Key Companies in the AI in Transportation Market market include

    Industry Developments

    • Q2 2024: Waymo expands autonomous trucking partnership with J.B. Hunt Waymo announced an expanded partnership with J.B. Hunt Transport Services to deploy autonomous trucks for freight hauling in Texas, marking a significant step in commercializing AI-driven logistics solutions.
    • Q2 2024: Aurora Innovation secures $820 million in new funding to accelerate self-driving truck rollout Aurora Innovation raised $820 million in a new funding round to support the commercial launch of its autonomous trucking platform, with participation from major institutional investors.
    • Q3 2024: Volvo Group and Nvidia launch next-generation AI-powered autonomous truck platform Volvo Group and Nvidia unveiled a new AI-based platform for autonomous trucks, aiming to enhance safety and efficiency in long-haul freight operations through advanced machine learning and sensor fusion.
    • Q2 2024: Cruise wins California approval to expand autonomous vehicle testing to commercial delivery Cruise received regulatory approval from California authorities to begin using its autonomous vehicles for commercial delivery services, expanding its AI-driven transportation offerings beyond passenger rides.
    • Q3 2024: Tesla appoints new VP of AI for Autopilot and Full Self-Driving division Tesla announced the appointment of a new Vice President of AI to lead its Autopilot and Full Self-Driving teams, signaling a renewed focus on advancing AI capabilities in its transportation products.
    • Q2 2024: Daimler Truck opens new AI research center in Berlin Daimler Truck AG inaugurated a dedicated AI research facility in Berlin to accelerate the development of intelligent transportation systems and autonomous vehicle technologies.
    • Q3 2024: Mobileye and Volkswagen announce partnership for AI-powered urban mobility pilot in Hamburg Mobileye and Volkswagen launched a pilot program in Hamburg to deploy AI-powered autonomous shuttles for public transportation, aiming to gather data and refine urban mobility solutions.
    • Q2 2024: Amazon acquires AI logistics startup Vimaan to boost warehouse automation Amazon completed the acquisition of Vimaan, a startup specializing in AI-driven logistics and warehouse automation, to enhance its supply chain efficiency and transportation network.
    • Q3 2024: Hyundai Motor launches AI-based smart traffic management system in Seoul Hyundai Motor introduced a city-wide AI-powered traffic management system in Seoul, designed to optimize traffic flow and reduce congestion using real-time data analytics.
    • Q2 2024: Nuro secures $600 million contract to deploy autonomous delivery vehicles for major US retailer Nuro announced a $600 million contract with a leading US retailer to deploy its AI-powered autonomous delivery vehicles across multiple states, expanding its footprint in last-mile transportation.
    • Q3 2024: Einride opens first US remote operations center for electric, autonomous freight Einride opened its inaugural US remote operations center to support the deployment and monitoring of its AI-driven electric and autonomous freight vehicles.
    • Q2 2024: TuSimple receives USDOT approval for fully driverless truck operations on public roads TuSimple announced it has received approval from the US Department of Transportation to operate fully driverless trucks on public highways, marking a regulatory milestone for AI in transportation.

    Future Outlook

    AI in Transportation Market Future Outlook

    The AI in Transportation Market is projected to grow at a 10.54% CAGR from 2024 to 2035, driven by advancements in autonomous vehicles, smart logistics, and enhanced traffic management systems.

    New opportunities lie in:

    • Development of AI-driven predictive maintenance solutions for fleet management.
    • Integration of AI in real-time traffic optimization platforms.
    • Creation of personalized AI-based travel planning applications for consumers.

    By 2035, the AI in Transportation Market is expected to be robust, driven by innovation and strategic investments.

    Market Segmentation

    AI in Transportation Market Offering Outlook

    • Hardware
    • Services
    • Software

    AI in Transportation Market Application Outlook

    • Autonomous Truck
    • Semi-autonomous Truck
    • Truck Platooning
    • Human-Machine Interface (HMI)
    • Predictive Maintenance
    • Precision & Mapping
    • Traffic Detection
    • Computer Vision-Powered Parking Management
    • Road Condition Monitoring
    • Automatic Traffic Incident Detection
    • Driver Monitoring
    • Others

    AI in Transportation Market Machine Learning Technology Outlook

    • Deep Learning
    • Computer Vision
    • Natural Language Processing
    • Context Awareness

    AI in Transportation Market IoT Communication Technology Outlook

    • Cellular
    • LPWAN
    • LoRaWAN
    • Z-Wave
    • Zigbee
    • NFC
    • Bluetooth
    • Others

    Report Scope

    MARKET SIZE 20242745.6(USD Million)
    MARKET SIZE 20253035.04(USD Million)
    MARKET SIZE 20358268.73(USD Million)
    COMPOUND ANNUAL GROWTH RATE (CAGR)10.54% (2024 - 2035)
    REPORT COVERAGERevenue Forecast, Competitive Landscape, Growth Factors, and Trends
    BASE YEAR2024
    Market Forecast Period2025 - 2035
    Historical Data2019 - 2024
    Market Forecast UnitsUSD Million
    Key Companies ProfiledMarket analysis in progress
    Segments CoveredMarket segmentation analysis in progress
    Key Market OpportunitiesIntegration of autonomous vehicle technology with smart city infrastructure presents a transformative opportunity in the AI in Transportation Market.
    Key Market DynamicsRapid advancements in artificial intelligence are reshaping transportation efficiency and safety standards across various sectors.
    Countries CoveredNorth America, Europe, APAC, South America, MEA

    Market Highlights

    Author
    Ankit Gupta
    Senior Research Analyst

    Ankit Gupta is an analyst in market research industry in ICT and SEMI industry. With post-graduation in "Telecom and Marketing Management" and graduation in "Electronics and Telecommunication" vertical he is well versed with recent development in ICT industry as a whole. Having worked on more than 150+ reports including consultation for fortune 500 companies such as Microsoft and Rio Tinto in identifying solutions with respect to business problems his opinions are inclined towards mixture of technical and managerial aspects.

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    FAQs

    What is the current valuation of the AI in Transportation Market?

    The AI in Transportation Market was valued at 2745.6 USD Million in 2024.

    What is the projected market valuation for the AI in Transportation Market by 2035?

    The market is projected to reach 8268.73 USD Million by 2035.

    What is the expected CAGR for the AI in Transportation Market during the forecast period?

    The expected CAGR for the AI in Transportation Market from 2025 to 2035 is 10.54%.

    Which companies are considered key players in the AI in Transportation Market?

    Key players include Waymo, Tesla, Cruise, Aurora, Mobileye, Baidu, Nuro, Zoox, and Pony.ai.

    What are the main segments of the AI in Transportation Market?

    The main segments include Offering, IoT Communication Technology, Application, and Machine Learning Technology.

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