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Global Automated Fare Collection Market to reach USD 25.68 billion by 2027.

Global Automated Fare Collection Market Size study, by Component (Hardware, Software) by Technology (Smart Card, Magnetic Stripe, Near Field Communication (NFC), Optical Character, Recognition (OCR)) by System (Ticket Vending Machine (TVM), Ticket Office Machine (TOM), Fare Gates, IC Card) by End Use (Railways & Transportation, Parking, Entertainment, Others) and Regional Forecasts 2021-2027

Product Code: ALTST-27399502
Publish Date: 7-12-2021
Page: 200

Global Automated Fare Collection Market is valued approximately USD 10.52 billion in 2020 and is anticipated to grow with a healthy growth rate of more than 13.6 % over the forecast period 2021-2027. Automated Fare Collection (AFC) refers to a ticketing system in public transport where the fare is no longer paid directly but via through an automated system. The Fare is directly debited from bank account of the driver or the owner. Automated Fare collection market is gaining popularity due to reduced payment time and enhanced payment security. Growing infrastructure development sector and rapid penetration of digital transection gateways are key drivers for the growth of the global Automated fare collection Market. According to Statista, total digital transaction value is expected to show an annual growth rate (CAGR 2021-2025) of 12.24% resulting in a estimated total amount of USD 10,71.3 billion by 2025. Also, increasing government expenditure on technological infrastructure development, the adoption & demand for Automated Fare Collection is likely to increase the market growth during the forecast period. However, high manufacturing and installation costs impedes the growth of the market over the forecast period.

The key regions considered for the global Automated Fare Collection market study includes Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is the leading/significant region across the world in terms of market share owing quick adoption of emerging technologies in this region and availability of required infrastructure. Whereas Asia-Pacific is also anticipated to exhibit highest growth rate / CAGR over the forecast period 2021-2027. Rising investments from governments of emerging economies aimed at the development of technological infrastructure would create lucrative growth prospects for the Automated Fare Collection market across Asia-Pacific region.

Major market player included in this report are:
Cubic Corporation (US)
Indra Sistemas (Spain)
Thales (France)
GMV (Spain)
ST Electronics (Singapore)
ATOS (France)
Longbow Technologies S/B (Malaysia)
Samsung SDS (Korea)
Genfare (US)
Advanced Card Systems Ltd. (Hong Kong)
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 Component:
Hardware
Software
By Technology:
Smart Card
Magnetic Stripe
Near Field Communication (NFC)
Optical Character Recognition (OCR)
By System:
Ticket Vending Machine (TVM)
Ticket Office Machine (TOM)
Fare Gates
IC Card
By End Use:
Railways & Transportation
Parking
Entertainment
Others
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 Automated Fare Collection 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. Automated Fare Collection Market, by Region, 2019-2027 (USD Billion)
1.2.2. Automated Fare Collection Market, by Component, 2019-2027 (USD Billion)
1.2.3. Automated Fare Collection Market, by Technology, 2019-2027 (USD Billion)
1.2.4. Automated Fare Collection Market, by System, 2019-2027 (USD Billion)
1.2.5. Automated Fare Collection Market, by End Use, 2019-2027 (USD Billion)
1.3. Key Trends
1.4. Estimation Methodology
1.5. Research Assumption
Chapter 2. Global Automated Fare Collection 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 Automated Fare Collection Market Dynamics
3.1. Automated Fare Collection Market Impact Analysis (2019-2027)
3.1.1. Market Drivers
3.1.1.1. Increasing Penetration of digital Transections gateways
3.1.1.2. Growing Infrastructure development sector
3.1.2. Market Challenges
3.1.2.1. High installation and maintenance Cost.
3.1.3. Market Opportunities
3.1.3.1. Increasing government expenditure on technology.
Chapter 4. Global Automated Fare Collection 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. Economical
4.2.3. Social
4.2.4. Technological
4.3. Investment Adoption Model
4.4. Analyst Recommendation & Conclusion
4.5. Top investment opportunity
4.6. Top winning strategies
Chapter 5. Risk Assessment: COVID-19 Impact
5.1.1. Assessment of the overall impact of COVID-19 on the industry
5.1.2. Pre COVID-19 and post COVID-19 market scenario
Chapter 6. Global Automated Fare Collection Market, by Component
6.1. Market Snapshot
6.2. Global Automated Fare Collection Market by Component, Performance – Potential Analysis
6.3. Global Automated Fare Collection Market Estimates & Forecasts by Component 2018-2027 (USD Billion)
6.4. Automated Fare Collection Market, Sub Segment Analysis
6.4.1. Hardware
6.4.2. Software

Chapter 7. Global Automated Fare Collection Market, by Technology
7.1. Market Snapshot
7.2. Global Automated Fare Collection Market by Technology, Performance – Potential Analysis
7.3. Global Automated Fare Collection Market Estimates & Forecasts by Technology 2018-2027 (USD Billion)
7.4. Automated Fare Collection Market, Sub Segment Analysis
7.4.1. Smart Card
7.4.2. Magnetic Stripe
7.4.3. Near Field Communication (NFC)
7.4.4. Optical Character Recognition (OCR)
Chapter 8. Global Automated Fare Collection Market, by System
8.1. Market Snapshot
8.2. Global Automated Fare Collection Market by System, Performance – Potential Analysis
8.3. Global Automated Fare Collection Market Estimates & Forecasts by System 2018-2027 (USD Billion)
8.4. Automated Fare Collection Market, Sub Segment Analysis
8.4.1. Ticket Vending Machine (TVM)
8.4.2. Ticket Office Machine (TOM)
8.4.3. Fare Gates
8.4.4. IC Card
8.4.5. Global Automated Fare Collection Market, by End Use
8.4.6. Market Snapshot
8.4.7. Global Automated Fare Collection Market by End Use, Performance – Potential Analysis
8.4.8. Global Automated Fare Collection Market Estimates & Forecasts by End Use 2018-2027 (USD Billion)
8.4.9. Automated Fare Collection Market, Sub Segment Analysis
8.4.10. Railways & Transportation
8.4.11. Parking
8.4.12. Entertainment
8.4.13. Others

Chapter 9. Global Automated Fare Collection Market, Regional Analysis
9.1. Automated Fare Collection Market, Regional Market Snapshot
9.2. North America Automated Fare Collection Market
9.2.1. U.S. Automated Fare Collection Market
9.2.1.1. Component breakdown estimates & forecasts, 2018-2027
9.2.1.2. Technology breakdown estimates & forecasts, 2018-2027
9.2.1.3. System breakdown estimates & forecasts, 2018-2027
9.2.1.4. End Use breakdown estimates & forecasts, 2018-2027
9.2.2. Canada Automated Fare Collection Market
9.3. Europe Automated Fare Collection Market Snapshot
9.3.1. U.K. Automated Fare Collection Market
9.3.2. Germany Automated Fare Collection Market
9.3.3. France Automated Fare Collection Market
9.3.4. Spain Automated Fare Collection Market
9.3.5. Italy Automated Fare Collection Market
9.3.6. Rest of Europe Automated Fare Collection Market
9.4. Asia-Pacific Automated Fare Collection Market Snapshot
9.4.1. China Automated Fare Collection Market
9.4.2. India Automated Fare Collection Market
9.4.3. Japan Automated Fare Collection Market
9.4.4. Australia Automated Fare Collection Market
9.4.5. South Korea Automated Fare Collection Market
9.4.6. Rest of Asia Pacific Automated Fare Collection Market
9.5. Latin America Automated Fare Collection Market Snapshot
9.5.1. Brazil Automated Fare Collection Market
9.5.2. Mexico Automated Fare Collection Market
9.6. Rest of The World Automated Fare Collection Market

Chapter 10. Competitive Intelligence
10.1. Top Market Strategies
10.2. Company Profiles
10.2.1. Cubic Corporation (US)
10.2.1.1. Key Information
10.2.1.2. Overview
10.2.1.3. Financial (Subject to Data Availability)
10.2.1.4. Product Summary
10.2.1.5. Recent Developments
10.2.2. Indra Sistemas (Spain)
10.2.3. Thales (France)
10.2.4. GMV (Spain)
10.2.5. ST Electronics (Singapore)
10.2.6. ATOS (France)
10.2.7. Longbow Technologies S/B (Malaysia)
10.2.8. Samsung SDS (Korea)
10.2.9. Genfare (US)
10.2.10. Advanced Card Systems Ltd. (Hong Kong)
Chapter 11. Research Process
11.1. Research Process
11.1.1. Data Mining
11.1.2. Analysis
11.1.3. Market Estimation
11.1.4. Validation
11.1.5. Publishing
11.2. Research Attributes
11.3. Research Assumption

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