Artificial Intelligence in Transportation Market- size, Share Growth, Trends, COVID-19 Impact, and Forecasts (2022 - 2030)

“According to the research report, the global artificial iIntelligence in transportation market is expected to reach USD 14.79 Billion by 2030 and the market is projected to grow with a significant CAGR of 22.97% from 2022 to 2030”. 

A new business intelligence report released by Precedence Research with the title Global artificial iIntelligence in transportation market 2022 by Manufacturers, Type and Application, forecast to 2030 is designed with an objective to provide a micro-level analysis of the market. The report offers a comprehensive study of the current state expected at the major drivers, market strategies, and key vendors’ growth. The report presents energetic visions to conclude and study the market size, market hopes, and competitive surroundings. The research also focuses on the important achievements of the market, Research & Development, and regional growth of the leading competitors operating in the market. The current trends of the global artificial iIntelligence in transportation market in conjunction with the geographical landscape of this vertical have also been included in this report.

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The global artificial iIntelligence in transportation market is the professional and accurate study of various business perspectives such as major key players, key geographies, divers, restraints, opportunities, and challenges. This global research report has been aggregated on the basis of various market segments and sub-segments associated with the global market.

·         Note – In order to provide more accurate market forecast, all our reports will be updated before delivery by considering the impact of COVID-19.

MARKET OVERVIEW BY GEOGRAPHY

The report bifurcates the geography into North America, Europe, Asia Pacific and the rest of the world (RoW). This section signifies the performance of the market in each region. The penetration of the market within each region is determined through multiple channels of research and by taking into consideration various factors such as prospective economic or political changes, product/service penetration, and region-wide pricing trends for the product/service, exchange rates, and the information provided by industry experts. Both positive as well as negative changes to the market are taken into consideration for the market estimates. The market size by geography is derived on the basis of the weightages assigned to these markets which are defined by shifts in the economy, ongoing market trends, demographics and competitors.

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COUNTRY FORECAST

The country forecast graph shows the comprehensive analysis on country level market. It also illustrates the market in terms of value generated from sales for the particular year. The drop down permits the year from 2017 to 2030 to compare the values of the countries. The country level forecast dashboard also allows comparing the data between major contributing countries and least participant countries for the each year. This will help client to create strategies to make the most of upcoming growth opportunities globally. Country level growth can accountable for both historical as well as projected market summary, since the development and future opportunities will create market competitors to take their decision according to the previous year’s market evolution. The country forecast data and CAGR will help to understand countries GDP, growing population, growth strategies of each country, and future growth potential on country level. This will help key vendors to identify sustainable growth opportunities in new market.

The study objectives of global market research report:

• To analyze the global artificial iIntelligence in transportation market on the basis of several business verticals such as drivers, restraints, and opportunities
• It offers detailed elaboration on the global competitive landscape
• To get an informative data of various leading key industries functioning across the global regions
• It offers qualitative and quantitative analysis of the global artificial iIntelligence in transportation market
• It offers all-inclusive information of global market along with its features, applications, challenges, threats, and opportunities

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]

The major key questions addressed through this innovative research report:

1. What are the major challenges in front of the global artificial iIntelligence in transportation market?
2. Who are the key vendors of the global artificial iIntelligence in transportation market?
3. What are the leading key industries of the global artificial iIntelligence in transportation market?
4. Which factors are responsible for driving the global artificial iIntelligence in transportation market?
5. What are the key outcomes of SWOT and Porter’s five analysis?
6. What are the major key strategies for enhancing global opportunities?
7. What are the different effective sales patterns?
8. What will be the global market size in the forecast period?

Various players operating in the global artificial iIntelligence in transportation markets are

•  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

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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