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Global Autonomous Train Market to reach USD XX million by 2027.

Global Autonomous Train Market Size study, by Automation Grade (GoA 1, GoA 2, GoA 3 and GoA 4), by Application (Passenger and Freight), by Technology (CBTC, ERTMS, ATC and PTC), and Regional Forecasts 2021-2027

Product Code: ALTST-56098457
Publish Date: 20-01-2022
Page: 200

Global Autonomous Train Market is valued approximately at USD XX Million in 2020 and is anticipated to grow with a healthy growth rate of more than XX% over the forecast period 2021-2027. Autonomous trains, are driverless trains that are monitored from the control station. Growing traffic congestion around the world, growing pollution level and rise in budget allocation for rail transport are fueling demand for autonomous train. According to Centre for Science and Environment, Chikkaballapur in southern Karnataka witnessed 3.9 per cent increase from 2019 pollution level as compared to 2020. Furthermore, growing government investment and research and development activities are expected to create demand in the market. In 2018, Singapore Land Transport Authority (LTA) purchased 66 six-car train-sets from Bombardier for its North-South and East-West lines. Also, in 2021, Siemens Mobility was awarded a contract to design, install, and commission the first Communications-Based Train Control (CBTC) technology for cross-border link trains between Malaysia and Singapore. However, high cost of train automation and high possibilities of hacking are poised to hinder the growth during forecast period.

Asia Pacific is leading the global market in terms of revenue among Asia Pacific, North America, Europe, Latin America, and Rest of the World, and is also expected to witness highest growth rate during forecast period. Growing infrastructural development and rising government funding in transportation sector are creating lucrative demand in Asia Pacific market.
Major market player included in this report are:

Alstom S.A.
ABB Group
Bombardier Transportation
CRRC Transportation
General Electric
Hitachi Ltd.
Kawasaki Heavy Industries
Mitsubishi Heavy Industries
Siemens AG
Thales Group

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Automation Grade:
GoA 1
GoA 2
GoA 3
GoA 4
By Application:
Passenger
Freight
By Technology:
CBTC
ERTMS
ATC
PTC

By Region:
North America
U.S.
Canada
Europe
UK
Germany
France
Spain
Italy
ROE

Asia Pacific
China
India
Japan
Australia
South Korea
RoAPAC
Latin America
Brazil
Mexico
Rest of the World

Furthermore, years considered for the study are as follows:

Historical year – 2018, 2019
Base year – 2020
Forecast period – 2021 to 2027.

Target Audience of the Global Autonomous Train Market in Market Study:

Key Consulting Companies & Advisors
Large, medium-sized, and small enterprises
Venture capitalists
Value-Added Resellers (VARs)
Third-party knowledge providers
Investment bankers
Investors

Chapter 1. Executive Summary
1.1. Market Snapshot
1.2. Global & Segmental Market Estimates & Forecasts, 2019-2027 (USD Billion)
1.2.1. Autonomous Train Market, by region, 2019-2027 (USD Billion)
1.2.2. Autonomous Train Market, by Automation Grade, 2019-2027 (USD Billion)
1.2.3. Autonomous Train Market, by Application, 2019-2027 (USD Billion)
1.2.4. Autonomous Train Market, by Technology, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Autonomous Train Market Definition and Scope
2.1. Objective of the Study
2.2. Market Definition & Scope
2.2.1. Scope of the Study
2.2.2. Industry Evolution
2.3. Years Considered for the Study
2.4. Currency Conversion Rates
Chapter 3. Global Autonomous Train Market Dynamics
3.1. Autonomous Train Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Growing traffic congestion
3.1.1.2. Rising pollution level
3.1.2. Market Restraint
3.1.2.1. Risk of hacking
3.1.2.2. High cost
3.1.3. Market Opportunities
3.1.3.1. Growing government spending
3.1.3.2. Research and development activities
Chapter 4. Global Autonomous Train Market: Industry Analysis
4.1. Porter’s 5 Force Model
4.1.1. Bargaining Power of Suppliers
4.1.2. Bargaining Power of Buyers
4.1.3. Threat of New Entrants
4.1.4. Threat of Substitutes
4.1.5. Competitive Rivalry
4.1.6. Futuristic Approach to Porter’s 5 Force Model (2018-2027)
4.2. PEST Analysis
4.2.1. Political
4.2.2. Economic
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
Chapter 5. Global Autonomous Train Market, by Automation Grade
5.1. Market Snapshot
5.2. Global Autonomous Train Market by Automation Grade, Performance – Potential Analysis
5.3. Global Autonomous Train Market Estimates & Forecasts by Automation Grade 2018-2027 (USD Billion)
5.4. Autonomous Train Market, Sub Segment Analysis
5.4.1. GoA 1
5.4.2. GoA 2
5.4.3. GoA 3
5.4.4. GoA 4
Chapter 6. Global Autonomous Train Market, by Application
a. Market Snapshot
6.1. Global Autonomous Train Market by Application, Performance – Potential Analysis
6.2. Global Autonomous Train Market Estimates & Forecasts by Application 2018-2027 (USD Billion)
6.3. Autonomous Train Market, Sub Segment Analysis
6.3.1. Passenger
6.3.2. Freight
Chapter 7. Global Autonomous Train Market, by Technology
b. Market Snapshot
7.1. Global Autonomous Train Market by Technology, Performance – Potential Analysis
7.2. Global Autonomous Train Market Estimates & Forecasts by Technology 2018-2027 (USD Billion)
7.3. Autonomous Train Market, Sub Segment Analysis
7.3.1. CBTC
7.3.2. ERTMS
7.3.3. ATC
7.3.4. PTC
Chapter 8. Global Autonomous Train Market, Regional Analysis
8.1. Autonomous Train Market, Regional Market Snapshot
8.2. North America Autonomous Train Market
8.2.1. U.S. Autonomous Train Market
8.2.1.1. Automation Grade breakdown estimates & forecasts, 2018-2027
8.2.1.2. Application breakdown estimates & forecasts, 2018-2027
8.2.1.3. Technology breakdown estimates & forecasts, 2018-2027
8.2.2. Canada Autonomous Train Market
8.3. Europe Autonomous Train Market Snapshot
8.3.1. U.K. Autonomous Train Market
8.3.2. Germany Autonomous Train Market
8.3.3. France Autonomous Train Market
8.3.4. Spain Autonomous Train Market
8.3.5. Italy Autonomous Train Market
8.3.6. Rest of Europe Autonomous Train Market
8.4. Asia-Pacific Autonomous Train Market Snapshot
8.4.1. China Autonomous Train Market
8.4.2. India Autonomous Train Market
8.4.3. Japan Autonomous Train Market
8.4.4. Australia Autonomous Train Market
8.4.5. South Korea Autonomous Train Market
8.4.6. Rest of Asia Pacific Autonomous Train Market
8.5. Latin America Autonomous Train Market Snapshot
8.5.1. Brazil Autonomous Train Market
8.5.2. Mexico Autonomous Train Market
8.6. Rest of The World Autonomous Train Market
Chapter 9. Competitive Intelligence
9.1. Top Market Strategies
9.2. Company Profiles
9.2.1. Alstom S.A.
9.2.1.1. Key Information
9.2.1.2. Overview
9.2.1.3. Financial (Subject to Data Availability)
9.2.1.4. Product Summary
9.2.1.5. Recent Developments
9.2.2. ABB Group
9.2.3. Bombardier Transportation
9.2.4. CRRC Transportation
9.2.5. General Electric
9.2.6. Hitachi Ltd.
9.2.7. Kawasaki Heavy Industries
9.2.8. Mitsubishi Heavy Industries
9.2.9. Siemens AG
9.2.10. Thales Group

Chapter 10. Research Process
10.1. Research Process
10.1.1. Data Mining
10.1.2. Analysis
10.1.3. Market Estimation
10.1.4. Validation
10.1.5. Publishing
10.2. Research Attributes
10.3. Research Assumption

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Data Collection:
To determine the appropriate methods of data collection based on the research objectives, we consider both primary and secondary sources. Primary data collection involves gathering information directly from various industry experts in core and related fields, original equipment manufacturers (OEMs), vendors, suppliers, technology developers, alliances, and organizations. These sources encompass all segments of the value chain within the specific industry. Through in-depth interviews, we engage with key industry participants, subject-matter experts, C-level executives of major market players, industry consultants, and other relevant experts. This allows us to obtain and validate critical qualitative and quantitative information while evaluating market prospects. AI and Big Data are instrumental in our primary research, providing us with powerful tools to collect, analyze, and derive insights from data efficiently. These technologies contribute to the advancement of research methodologies, enabling us to make data-driven decisions and uncover valuable findings.
In addition to primary sources, we extensively utilize secondary sources to enhance our research. These include directories, databases, journals focusing on related industries, company newsletters, and information portals such as Bloomberg, D&B Hoovers, and Factiva. These secondary sources enable us to identify and gather valuable information for our comprehensive, technical, market-oriented, and commercial study of the market. Additionally, we utilize AI algorithms to automate the collection of vast amounts of data from various sources such as surveys, social media platforms, online transactions, and web scraping. And employ Big Data technologies for storage and processing of large datasets, ensuring that no valuable information is missed during the data collection process.
Data Analysis:
Our team of experts carefully examine the gathered data using suitable statistical techniques and qualitative analysis methods. For quantitative analysis, we employ descriptive statistics, regression analysis, and other advanced statistical methods, depending on the characteristics of the data. This analysis may also incorporate the utilization of AI tools and big data analysis techniques to extract meaningful insights.
To ensure the accuracy and reliability of our findings, we extensively leverage data science techniques, which help us minimize discrepancies and uncertainties in our analysis. We employ Data Science to clean and preprocess the data, ensuring its quality and reliability. This involves handling missing data, removing outliers, standardizing variables, and transforming data into suitable formats for analysis. The application of data science techniques enhances our accuracy, efficiency, and depth of analysis, enabling us to stay competitive in dynamic market environments.
Market Size Estimation:
Our proprietary data tools play a crucial role in deriving our market estimates and forecasts. Each study involves the creation of a unique and customized model. The model incorporates the gathered information on market dynamics, technology landscape, application development, and pricing trends. AI techniques, such as machine learning and deep learning, aid us to analyze patterns within the data to identify correlations, trends, and relationships. By recognizing patterns in consumer behavior, purchasing habits, or market dynamics, our AI algorithms aid us in more precise estimations of market size. These factors are simultaneously analyzed within the model, allowing for a comprehensive assessment. To quantify their impact over the forecast period, correlation, regression, and time series analysis are employed.
To estimate and validate the market size, we employ both top-down and bottom-up approaches. The preference is given to a bottom-up approach, where key regional markets are analyzed as separate entities. This data is then integrated to obtain global estimates. This approach is crucial as it provides a deep understanding of the industry and helps minimize errors.
In our forecasting process, we consider various parameters such as economic tools, technological analysis, industry experience, and domain expertise. By taking all these factors into account, we strive to produce accurate and reliable market forecasts. When forecasting, we take into consideration several parameters, which include:
Market driving trends and favorable economic conditions
Restraints and challenges that are expected to be encountered during the forecast period.
Anticipated opportunities for growth and development
Technological advancements and projected developments in the market
Consumer spending trends and dynamics
Shifts in consumer preferences and behaviors.
The current state of raw materials and trends in supply versus pricing
Regulatory landscape and expected changes or developments.
The existing capacity in the market and any expected additions or expansions up to the end of the forecast period.
To assess the market impact of these parameters, we assign weights to each one and utilize weighted average analysis. This process allows us to quantify their influence on the market and derive an expected growth rate for the forecasted period. By considering these various factors and applying a weighted analysis approach, we strive to provide accurate and reliable market forecasts.
Insight Generation & Report Presentation:
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