The AI in transportation market is expected to grow at a CAGR of 11.80%, reaching a market size of US$6.196 billion in 2029 from US$3.797 billion in 2024.
AI technology and algorithms are being integrated into as many areas of transportation systems as possible in a bid to increase efficiency, safety, and sustainability. The key to developing and deploying the autonomous car is using AI to traverse safe environments and detect surroundings using computer vision, sensor fusion, machine learning, and deep learning to analyze complicated traffic in real-time.
Other AI application areas in traffic management include sensors, cameras, and other forms of data monitoring and optimizing traffic flow in cities and highways. AI technologies have ensured safety and security within transportation systems by detecting and managing risks such as accidents and other security-related issues. Computer vision systems analyze traffic and airports, and machine learning models compile the analysis for further application. AI-based optimization algorithms further improve traffic flow by decreasing emissions, reducing congestion, and promoting alternative fuels and modes.
Overall, the advent of Mobility-as-a-Service is what boosts AI technologies in the transportation market, opening doors for commuting and travel to more efficient, convenient, and sustainable mobility solutions.
Overall, North America's leadership in AI technology, together with its supporting ecosystem, strong industrial presence, and early adoption of AI in transportation, establishes it as a prominent participant in the worldwide market.
AI technology and algorithms are being integrated into as many areas of transportation systems as possible in a bid to increase efficiency, safety, and sustainability. The key to developing and deploying the autonomous car is using AI to traverse safe environments and detect surroundings using computer vision, sensor fusion, machine learning, and deep learning to analyze complicated traffic in real-time.
Other AI application areas in traffic management include sensors, cameras, and other forms of data monitoring and optimizing traffic flow in cities and highways. AI technologies have ensured safety and security within transportation systems by detecting and managing risks such as accidents and other security-related issues. Computer vision systems analyze traffic and airports, and machine learning models compile the analysis for further application. AI-based optimization algorithms further improve traffic flow by decreasing emissions, reducing congestion, and promoting alternative fuels and modes.
AI in transportation market drivers
Rising Mobility-as-a-Service (MaaS) is contributing to AI in the transportation market growth
MaaS was developed to provide transport services in one platform and a unified solution that can create leverage for AI adoption. In MaaS systems, AI algorithms are applied to optimize routes, predict demand, and thus provide individual travel experiences. Of the various products in the market, the Hitachi Predictive Maintenance for Fleet Operations powered by Google Cloud brings together IoT data, RCM methodologies, and AI technology that optimize fleet maintenance efficiency and asset dependability. This is done through augmented reality, machine learning algorithms, and external data, allowing for real-time inspections and repairs of mission-critical fleet assets.Overall, the advent of Mobility-as-a-Service is what boosts AI technologies in the transportation market, opening doors for commuting and travel to more efficient, convenient, and sustainable mobility solutions.
AI in transportation market geographical outlook
North America is witnessing exponential growth during the forecast period
North American transportation firms, government organizations, and communities were among the first to employ AI technology to improve transportation networks' efficiency, safety, and sustainability. This early adoption has driven the area to the top of AI in the transportation industry.Overall, North America's leadership in AI technology, together with its supporting ecosystem, strong industrial presence, and early adoption of AI in transportation, establishes it as a prominent participant in the worldwide market.
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Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive IntelligenceReport Coverage:
- Historical data & forecasts from 2022 to 2029
- Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, Customer Behaviour, and Trend Analysis
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The AI in transportation market is segmented and analyzed as follows:
By Technology
- Deep Learning
- Natural learning process
- Machine Learning
- Others
By Deployment
- Cloud
- On-Premise
By Application
- Route optimization
- Shipping volume prediction
- Predictive Fleet Maintenance
- Real-time Vehicle tracking
- Others
By Geography
- North America
- USA
- Canada
- Mexico
- South America
- Brazil
- Argentina
- Others
- Europe
- Germany
- France
- UK
- Spain
- Others
- Middle East and Africa
- Saudi Arabia
- UAE
- Israel
- Others
- Asia Pacific
- China
- Japan
- India
- South Korea
- Indonesia
- Taiwan
- Others
Table of Contents
1. INTRODUCTION
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
5. AI IN TRANSPORTATION MARKET BY TECHNOLOGY
6. AI IN TRANSPORTATION MARKET BY DEPLOYMENT
7. AI IN TRANSPORTATION MARKET BY APPLICATION
8. AI IN TRANSPORTATION MARKET BY GEOGRAPHY
9. COMPETITIVE ENVIRONMENT AND ANALYSIS
10. COMPANY PROFILES
Companies Mentioned
- Hitachi
- Wialon (Gurtam)
- AltexSoft
- Planung Transport Verkehr GmbH
- Integrated Roadways
- Maticz
- FlowSpace
- Axestrack
Methodology
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