Advanced Traveller Information System Market by Type (Route Guidance Systems, Transit Information Systems), by Travel Mode (Uni-modal ATIS, Multi-modal ATIS), by Application (Operator specific ATIS, Region-specific ATIS, Independent ATIS), by North America (U.S., Canada, Mexico), by Europe (UK, Germany, France, Italy, Spain, Russia, Netherlands, Switzerland, Poland, Sweden, Belgium), by Asia Pacific (China, India, Japan, South Korea, Australia, Singapore, Malaysia, Indonesia, Thailand, Philippines, New Zealand), by Latin America (Brazil, Mexico, Argentina, Chile, Colombia, Peru), by MEA (UAE, Saudi Arabia, South Africa, Egypt, Turkey, Israel, Nigeria, Kenya) Forecast 2024-2032
The Advanced Traveller Information System Market size was valued at USD 22.78 USD Billion in 2023 and is projected to reach USD 59.61 USD Billion by 2032, exhibiting a CAGR of 14.73 % during the forecast period. Advanced Traveller Information System (ATIS) is a technology platform that provides real-time information to travelers about traffic conditions, public transportation schedules, route options, weather updates, and other relevant travel-related data. ATIS leverages a variety of data sources, including GPS, traffic sensors, social media, and transportation network providers, to offer users accurate and timely information, thereby enhancing their travel experience and enabling more efficient journey planning. ATIS includes the integration of artificial intelligence (AI) and machine learning (ML) to provide predictive analytics and personalized travel recommendations. The use of big data analytics to process vast amounts of travel data for more accurate information is also on the rise. Additionally, there is a trend towards multi-modal travel information systems that combine data from various modes of transportation, such as cars, buses, trains, and bicycles, to offer comprehensive travel solutions. The development of user-friendly mobile applications and the incorporation of voice-activated assistants are also gaining popularity. AI and ML integration, big data analytics, multi-modal information systems, and user-friendly applications as it offer significant opportunities, particularly in emerging urban areas and through collaboration with various stakeholders in the transportation ecosystem.
Type:
Travel Mode:
Application:
Aspects | Details |
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Study Period | 2018-2032 |
Base Year | 2023 |
Estimated Year | 2024 |
Forecast Period | 2024-2032 |
Historical Period | 2018-2023 |
Growth Rate | CAGR of 14.73% from 2018-2032 |
Segmentation |
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Aspects | Details |
---|---|
Study Period | 2018-2032 |
Base Year | 2023 |
Estimated Year | 2024 |
Forecast Period | 2024-2032 |
Historical Period | 2018-2023 |
Growth Rate | CAGR of 14.73% from 2018-2032 |
Segmentation |
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Note* : In applicable scenarios
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