Artificial Intelligence in Transportation Market Size to Worth Around US$ 14.79 Billion by 2030

[150+ Pages Report] As per the latest Research and survey report issued by Precedence Research, the global artificial intelligence in transportation market was valued at around USD 2.3 billion in 2021 and is expected to register revenues worth USD 14.79 billion by the end of 2030, growing at an exceptional CAGR of approximately 22.97% between 2022 and 2030.

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Artificial Intelligence in Transportation Market Report Scope

A recent study by Precedence Research on the artificial intelligence in transportation market offers a forecast for 2022 and 2030. The study analyzes crucial trends that are currently determining the growth of the artificial intelligence in transportation market. This report explicates on vital dynamics such as the drivers, restraints, and opportunities for key market players, along with key stakeholders as well as emerging players associated with the manufacturing of artificial intelligence in transportation. The study also provides the dynamics that are responsible for influencing the future status of the artificial intelligence in transportation market over the forecast period.

A detailed assessment of the artificial intelligence in transportation market value chain analysis, business execution, and supply chain analysis across regional markets has been covered in the report. A list of prominent companies operating in the artificial intelligence in transportation market along with their product portfolio enhances the reliability of this comprehensive research study.

Competition Landscape

The report has engulfed a chapter on the global artificial intelligence in transportation market’s competitive landscape, which provides detailed analysis and insights on companies offering artificial intelligence in transportation. Profiles of key companies, along with a strategic overview of their M&A and expansion plans across geographies, have been delivered in this chapter. This chapter is priceless for report readers, as its enables them in gauging their growth potential in the market and implement key strategies for extending their market reach.

This chapter offers key recommendations for both new and existing market participants, enabling them to emerge sustainably and profitably. Intelligence on the market players has been delivered on the basis of their product overview, SWOT analysis, key developments, key financials and company overview. Occupancy of these market participants has been tracked by the report and portrayed via an intensity map.

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Some of the Prominent Players in the Artificial Intelligence in Transportation Market Include:

• 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

Artificial Intelligence in Transportation Market Segmentation

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

Regional Segmentation

• Asia-Pacific [China, Southeast Asia, India, Japan, Korea, Western Asia]
• Europe [Germany, UK, France, Italy, Russia, Spain, Netherlands, Turkey, Switzerland]
• North America [United States, Canada, Mexico]
• South America [Brazil, Argentina, Columbia, Chile, Peru]
• Middle East & Africa [GCC, North Africa, South Africa]

Regional Analysis

The research report includes a detailed study of regions of North America, Europe, China, Japan and Rest of the World. The report has been curated after observing and studying various factors that determine regional growth such as economic, environmental, social, technological, and political status of the particular region. Analysts have studied the data of revenue and manufacturers of each region. This section analyses region-wise revenue and volume for the forecast period of 2022 to 2030. These analyses will help the reader to understand the potential worth of investment in a particular region.

The report provides in-depth segment analysis of the global artificial intelligence in transportation market, thereby providing valuable insights at macro as well as micro levels. Analysis of major countries, which hold growth opportunities or account for significant share has also been included as part of geographic analysis of the artificial intelligence in transportation market.

The report includes country-wise and region-wise market size for the period 2022-2030. It also includes market size and forecast by segments in terms of production capacity, price and revenue for the period 2022-2030.

Why should you invest in this report?

If you are aiming to enter the global artificial intelligence in transportation market, this report is a comprehensive guide that provides crystal clear insights into this niche market. All the major application areas for artificial intelligence in transportation are covered in this report and information is given on the important regions of the world where this market is likely to boom during the forecast period of 2022-2030 so that you can plan your strategies to enter this market accordingly.

Besides, through this report, you can have a complete grasp of the level of competition you will be facing in this hugely competitive market and if you are an established player in this market already, this report will help you gauge the strategies that your competitors have adopted to stay as market leaders in this market. For new entrants to this market, the voluminous data provided in this report is invaluable.

Table of Contents

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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