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Last Updated: Apr 25, 2025 | Study Period: 2024-2030
By analysing real-time data on ticket sales, gateline usage, station parking usage, station store sales, reports of train and station crowding, and other indicators, artificial intelligence may learn about the travel preferences and habits of rail passengers.
This information is used by the AI computer to understand the intricate patterns of ticket sales and forecast train usage based on the weather, regional economic performance, sporting events, and a wide range of other variables that it would have found to be related to train utilisation.
As a result, the potential of AI's most potent tool to learn from enormous amounts of data may be used to comprehend how users wish to use the rail network like never before. As the information it monitors grew over time, more data would be available for analysis, generating the perfect environment for an AI computer to constantly refine and improve itself
The Indonesia Railway AI Market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
To assist operators in monitoring social distance and lowering passenger density in stations and on trains, Thales has created an eco-friendly Artificial Intelligence (AI)-based system.
Thales supports the safest and most enjoyable transportation of passengers, monitors operations with a thorough understanding of the situation, and maximises the effectiveness of transportation services.
Thales creates ground-breaking solutions like digital signalling, train autonomy, mobile ticketing, passenger flow analytics, data driven operation control, and smart maintenance using digital technologies like IoT, 5G, cloud and web IT, data analytics, and AI. These solutions will have a significant impact on how we all travel.
Using a modular and robust equipment platform approach, Thales' newest metro signalling system SelTrac G8's digital architecture enables software functionalities to be continuously upgraded without causing traffic disruptions.
Sl no | Topic |
1 | Market Segmentation |
2 | Scope of the report |
3 | Abbreviations |
4 | Research Methodology |
5 | Executive Summary |
6 | Introduction |
7 | Insights from Industry stakeholders |
8 | Cost breakdown of Product by sub-components and average profit margin |
9 | Disruptive innovation in the Industry |
10 | Technology trends in the Industry |
11 | Consumer trends in the industry |
12 | Recent Production Milestones |
13 | Component Manufacturing in US, EU and China |
14 | COVID-19 impact on overall market |
15 | COVID-19 impact on Production of components |
16 | COVID-19 impact on Point of sale |
17 | Market Segmentation, Dynamics and Forecast by Geography, 2022-2030 |
18 | Market Segmentation, Dynamics and Forecast by Product Type, 2022-2030 |
19 | Market Segmentation, Dynamics and Forecast by Application, 2022-2030 |
20 | Market Segmentation, Dynamics and Forecast by End use, 2022-2030 |
21 | Product installation rate by OEM, 2022 |
22 | Incline/Decline in Average B-2-B selling price in past 5 years |
23 | Competition from substitute products |
24 | Gross margin and average profitability of suppliers |
25 | New product development in past 12 months |
26 | M&A in past 12 months |
27 | Growth strategy of leading players |
28 | Market share of vendors, 2022 |
29 | Company Profiles |
30 | Unmet needs and opportunity for new suppliers |
31 | Conclusion |
32 | Appendix |