The artificial intelligence (AI) in transportation market would grow at a CAGR of 22.97% over the predicted time frame. The market is expected to increase in value from US$ 2.83 Bn in 2022 to US$ 14.79 Bn in 2030.
The on artificial intelligence (AI) in transportation Market, which provides a business strategy, research & development activities, concise outline of the market valuation, valuable insights pertaining to market share, size, supply chain analysis, competitive landscape and regional proliferation of this industry.
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Report Scope of the Artificial Intelligence (AI) in Transportation Market
| Report Coverage | Details |
| Market Size in 2022 | USD 2.83 Billion |
| Market Size by 2030 | USD 14.79 Billion |
| Growth Rate from 2022 to 2030 | CAGR of 22.97% |
| Base Year | 2021 |
| Forecast Period | 2022 to 2030 |
| Segments Covered | Offering, Machine Learning Technology, Process, Application, Geography |
A recent report provides crucial insights along with application based and forecast information in the Global Artificial intelligence (AI) in transportation Market. The report provides a comprehensive analysis of key factors that are expected to drive the growth of this market. This study also provides a detailed overview of the opportunities along with the current trends observed in the Artificial intelligence (AI) in transportation market.
A quantitative analysis of the industry is compiled for a period of 10 years in order to assist players to grow in the market. Insights on specific revenue figures generated are also given in the report, along with projected revenue at the end of the forecast period.
Companies and Manufacturers Covered
The study covers key players operating in the market along with prime schemes and strategies implemented by each player to hold high positions in the industry. Such a tough vendor landscape provides a competitive outlook of the industry, consequently existing as a key insight. These insights were thoroughly analysed and prime business strategies and products that offer high revenue generation capacities were identified. Key players of the global Artificial intelligence (AI) in transportation market are included as given below:
Artificial intelligence (AI) in transportation Market Key Players
- Volvo
- Daimler
- Scania
- Paccar
- Peloton
- Valeo
- Xevo
- ZF
- Zonar
- Tier-I Suppliers
- Software Suppliers
- Start-Up’s Bosch
- Intel
- NVIDIA
- Alphabet
- Continental
- Magna
- Man
- Microsoft
- Nauto
- IBM Corporation
Market Segments
By Offering
- Hardware
- Neuromorphic
- Von Neumann
- Software
- Platforms
- Solutions
By Machine Learning Technology
- Deep Learning
- Computer Vision
- Context Awareness
- Natural Language Processing
By Process
- Signal Recognition
- Object Recognition
- Data Mining
By Application
- Semi Autonomous Truck
- Truck platooning
- Predictive maintenance
- Precision and mapping
- Autonomous truck
- Machine human interface
- Others
By Geography
- North America
- U.S.
- Canada
- Europe
- U.K.
- Germany
- France
- Asia-Pacific
- China
- India
- Japan
- South Korea
- Malaysia
- Philippines
- Latin America
- Brazil
- Rest of Latin America
- Middle East & Africa (MEA)
- GCC
- North Africa
- South Africa
- Rest of the Middle East & Africa
Report Objectives
- To define, describe, and forecast the global artificial intelligence (AI) in transportation market based on product, and region
- To provide detailed information regarding the major factors influencing the growth of the market (drivers, opportunities, and industry-specific challenges)
- To strategically analyze micromarkets1 with respect to individual growth trends, future prospects, and contributions to the total market
- To analyze opportunities in the market for stakeholders and provide details of the competitive landscape for market leaders
- To forecast the size of market segments with respect to four main regions—North America, Europe, Asia Pacific and the Rest of the World (RoW)2
- To strategically profile key players and comprehensively analyze their product portfolios, market shares, and core competencies3
- To track and analyze competitive developments such as acquisitions, expansions, new product launches, and partnerships in the artificial intelligence (AI) in transportation market
Table of Content
Chapter 1. Introduction
1.1. Research Objective
1.2. Scope of the Study
1.3. Definition
Chapter 2. Research Methodology
2.1. Research Approach
2.2. Data Sources
2.3. Assumptions & Limitations
Chapter 3. Executive Summary
3.1. Market Snapshot
Chapter 4. Market Variables and Scope
4.1. Introduction
4.2. Market Classification and Scope
4.3. Industry Value Chain Analysis
4.3.1. Raw Material Procurement Analysis
4.3.2. Sales and Distribution Channel Analysis
4.3.3. Downstream Buyer Analysis
Chapter 5. COVID 19 Impact on Artificial Intelligence (AI) in Transportation Market
5.1. COVID-19 Landscape: Artificial Intelligence (AI) in Transportation Industry Impact
5.2. COVID 19 - Impact Assessment for the Industry
5.3. COVID 19 Impact: Global Major Government Policy
5.4. Market Trends and Opportunities in the COVID-19 Landscape
Chapter 6. Market Dynamics Analysis and Trends
6.1. Market Dynamics
6.1.1. Market Drivers
6.1.2. Market Restraints
6.1.3. Market Opportunities
6.2. Porter’s Five Forces Analysis
6.2.1. Bargaining power of suppliers
6.2.2. Bargaining power of buyers
6.2.3. Threat of substitute
6.2.4. Threat of new entrants
6.2.5. Degree of competition
Chapter 7. Competitive Landscape
7.1.1. Company Market Share/Positioning Analysis
7.1.2. Key Strategies Adopted by Players
7.1.3. Vendor Landscape
7.1.3.1. List of Suppliers
7.1.3.2. List of Buyers
Chapter 8. Global Artificial Intelligence (AI) in Transportation Market, By Offering
8.1. Artificial Intelligence (AI) in Transportation Market, by Offering, 2022-2030
8.1.1. Hardware
8.1.1.1. Market Revenue and Forecast (2017-2030)
8.1.2. Software
8.1.2.1. Market Revenue and Forecast (2017-2030)
Chapter 9. Global Artificial Intelligence (AI) in Transportation Market, By Machine Learning Technology
9.1. Artificial Intelligence (AI) in Transportation Market, by Machine Learning Technology e, 2022-2030
9.1.1. Deep Learning
9.1.1.1. Market Revenue and Forecast (2017-2030)
9.1.2. Deep Learning
9.1.2.1. Market Revenue and Forecast (2017-2030)
9.1.3. Context Awareness
9.1.3.1. Market Revenue and Forecast (2017-2030)
9.1.4. Context Awareness
9.1.4.1. Market Revenue and Forecast (2017-2030)
Chapter 10. Global Artificial Intelligence (AI) in Transportation Market, By Process
10.1. Artificial Intelligence (AI) in Transportation Market, by Process, 2022-2030
10.1.1. Signal Recognition
10.1.1.1. Market Revenue and Forecast (2017-2030)
10.1.2. Object Recognition
10.1.2.1. Market Revenue and Forecast (2017-2030)
10.1.3. Data Mining
10.1.3.1. Market Revenue and Forecast (2017-2030)
Chapter 11. Global Artificial Intelligence (AI) in Transportation Market, By Application
11.1. Artificial Intelligence (AI) in Transportation Market, by Application, 2022-2030
11.1.1. Semi Autonomous Truck
11.1.1.1. Market Revenue and Forecast (2017-2030)
11.1.2. Truck platooning
11.1.2.1. Market Revenue and Forecast (2017-2030)
11.1.3. Predictive maintenance
11.1.3.1. Market Revenue and Forecast (2017-2030)
11.1.4. Precision and mapping
11.1.4.1. Market Revenue and Forecast (2017-2030)
11.1.5. Autonomous truck
11.1.5.1. Market Revenue and Forecast (2017-2030)
11.1.6. Machine human interface
11.1.6.1. Market Revenue and Forecast (2017-2030)
11.1.7. Others
11.1.7.1. Market Revenue and Forecast (2017-2030)
Chapter 12. Global Artificial Intelligence (AI) in Transportation Market, Regional Estimates and Trend Forecast
12.1. North America
12.1.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.1.3. Market Revenue and Forecast, by Process (2017-2030)
12.1.4. Market Revenue and Forecast, by Application (2017-2030)
12.1.5. U.S.
12.1.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.1.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.1.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.1.6. Rest of North America
12.1.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.1.6.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.1.6.3. Market Revenue and Forecast, by Process (2017-2030)
12.1.6.4. Market Revenue and Forecast, by Application (2017-2030)
12.2. Europe
12.2.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.2.3. Market Revenue and Forecast, by Process (2017-2030)
12.2.4. Market Revenue and Forecast, by Application (2017-2030)
12.2.5. UK
12.2.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.2.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.2.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.2.6. Germany
12.2.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.6.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.2.6.3. Market Revenue and Forecast, by Process (2017-2030)
12.2.6.4. Market Revenue and Forecast, by Application (2017-2030)
12.2.7. France
12.2.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.7.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.2.7.3. Market Revenue and Forecast, by Process (2017-2030)
12.2.7.4. Market Revenue and Forecast, by Application (2017-2030)
12.2.8. Rest of Europe
12.2.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.2.8.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.2.8.3. Market Revenue and Forecast, by Process (2017-2030)
12.2.8.4. Market Revenue and Forecast, by Application (2017-2030)
12.3. APAC
12.3.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.3.3. Market Revenue and Forecast, by Process (2017-2030)
12.3.4. Market Revenue and Forecast, by Application (2017-2030)
12.3.5. India
12.3.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.3.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.3.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.3.6. China
12.3.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.6.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.3.6.3. Market Revenue and Forecast, by Process (2017-2030)
12.3.6.4. Market Revenue and Forecast, by Application (2017-2030)
12.3.7. Japan
12.3.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.7.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.3.7.3. Market Revenue and Forecast, by Process (2017-2030)
12.3.7.4. Market Revenue and Forecast, by Application (2017-2030)
12.3.8. Rest of APAC
12.3.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.3.8.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.3.8.3. Market Revenue and Forecast, by Process (2017-2030)
12.3.8.4. Market Revenue and Forecast, by Application (2017-2030)
12.4. MEA
12.4.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.4.3. Market Revenue and Forecast, by Process (2017-2030)
12.4.4. Market Revenue and Forecast, by Application (2017-2030)
12.4.5. GCC
12.4.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.4.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.4.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.4.6. North Africa
12.4.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.6.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.4.6.3. Market Revenue and Forecast, by Process (2017-2030)
12.4.6.4. Market Revenue and Forecast, by Application (2017-2030)
12.4.7. South Africa
12.4.7.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.7.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.4.7.3. Market Revenue and Forecast, by Process (2017-2030)
12.4.7.4. Market Revenue and Forecast, by Application (2017-2030)
12.4.8. Rest of MEA
12.4.8.1. Market Revenue and Forecast, by Offering (2017-2030)
12.4.8.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.4.8.3. Market Revenue and Forecast, by Process (2017-2030)
12.4.8.4. Market Revenue and Forecast, by Application (2017-2030)
12.5. Latin America
12.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.5.5. Brazil
12.5.5.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.5.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.5.5.3. Market Revenue and Forecast, by Process (2017-2030)
12.5.5.4. Market Revenue and Forecast, by Application (2017-2030)
12.5.6. Rest of LATAM
12.5.6.1. Market Revenue and Forecast, by Offering (2017-2030)
12.5.6.2. Market Revenue and Forecast, by Machine Learning Technology (2017-2030)
12.5.6.3. Market Revenue and Forecast, by Process (2017-2030)
12.5.6.4. Market Revenue and Forecast, by Application (2017-2030)
Chapter 13. Company Profiles
13.1. Volvo
13.1.1. Company Overview
13.1.2. Product Offerings
13.1.3. Financial Performance
13.1.4. Recent Initiatives
13.2. Daimler
13.2.1. Company Overview
13.2.2. Product Offerings
13.2.3. Financial Performance
13.2.4. Recent Initiatives
13.3. Scania
13.3.1. Company Overview
13.3.2. Product Offerings
13.3.3. Financial Performance
13.3.4. Recent Initiatives
13.4. Paccar
13.4.1. Company Overview
13.4.2. Product Offerings
13.4.3. Financial Performance
13.4.4. Recent Initiatives
13.5. Peloton
13.5.1. Company Overview
13.5.2. Product Offerings
13.5.3. Financial Performance
13.5.4. Recent Initiatives
13.6. Valeo
13.6.1. Company Overview
13.6.2. Product Offerings
13.6.3. Financial Performance
13.6.4. Recent Initiatives
13.7. Xevo
13.7.1. Company Overview
13.7.2. Product Offerings
13.7.3. Financial Performance
13.7.4. Recent Initiatives
13.8. ZF
13.8.1. Company Overview
13.8.2. Product Offerings
13.8.3. Financial Performance
13.8.4. Recent Initiatives
13.9. Zonar
13.9.1. Company Overview
13.9.2. Product Offerings
13.9.3. Financial Performance
13.9.4. Recent Initiatives
13.10. Tier-I Suppliers
13.10.1. Company Overview
13.10.2. Product Offerings
13.10.3. Financial Performance
13.10.4. Recent Initiatives
Chapter 14. Research Methodology
14.1. Primary Research
14.2. Secondary Research
14.3. Assumptions
Chapter 15. Appendix
15.1. About Us
15.2. Glossary of Terms
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