Artificial Intelligence in Transportation Market Size will Reach USD 14.79 Billion by 2030

According to Precedence Research, during the forecast period of 2022 to 2030, the global artificial intelligence in transportation market is estimated to develop at a compound annual growth rate (CAGR) of 22.97%. The global artificial intelligence in transportation market was valued at USD 2.3 billion in 2021, and it is predicted to exceed USD 14.79  billion by 2030. The study investigates several elements and their consequences on the growth of the artificial intelligence in transportation market.

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This report focuses on artificial intelligence in transportation market volume and value at the global level, regional level and company level. From a global perspective, this report represents overall artificial intelligence in transportation market size by analyzing historical data and future prospect. Regionally, this report focuses on several key regions: North America, Europe, Middle East & Africa, Latin America, etc.

Report Scope of the Artificial Intelligence (AI) in Transportation Market

The research report includes specific segments by region (country), by company, by all segments. This study provides information about the growth and revenue during the historic and forecasted period of 2017 to 2030. Understanding the segments helps in identifying the importance of different factors that aid the market growth.

In-Depth Analysis on Competitive Landscape

The report sheds light on leading manufacturers of artificial intelligence in transportation, along with their detailed profiles. Essential and up-to-date data related to market performers who are principally engaged in the production of artificial intelligence in transportation has been brought with the help of a detailed dashboard view. Market share analysis and comparison of prominent players provided in the report permits report readers to take preemptive steps in advancing their businesses.

Company profiles have been included in the report, which include essentials such as product portfolio, key strategies, along with all-inclusive SWOT analysis on each player. Company presence is mapped and presented through a matrix for all the prominent players, thus providing readers with actionable insights, which helps in thoughtfully presenting market status and predicting the competition level in the artificial intelligence in transportation market.

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

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Segments Covered in the Report

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]

Some of the important ones are:

• What can be the best investment choices for venturing into new product and service lines?
• What value propositions should businesses aim at while making new research and development funding?
• Which regulations will be most helpful for stakeholders to boost their supply chain network?
• Which regions might see the demand maturing in certain segments in near future?
• What are the some of the best cost optimization strategies with vendors that some well-entrenched players have gained success with?
• Which are the key perspectives that the C-suite are leveraging to move businesses to new growth trajectory?
• Which government regulations might challenge the status of key regional markets?
• How will the emerging political and economic scenario affect opportunities in key growth areas?
• What are some of the value-grab opportunities in various segments?
• What will be the barrier to entry for new players in the market?

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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