Overview and Scope
AI in transportation refers to the use of artificial intelligence (AI) technologies in various areas of transportation, such as autonomous vehicles, traffic management, and logistics. AI in transportation is used to forecast collisions based on environmental and other parameters to improve passenger safety, decrease accidents and traffic jams, cut carbon emissions, and lower overall costs.
Sizing and Forecast
The ai in transportation market size has grown rapidly in recent years. It will grow from $3.25 billion in 2023 to $3.86 billion in 2024 at a compound annual growth rate (CAGR) of 18.6%. The growth in the historic period can be attributed to traffic management optimization, autonomous vehicles development, demand for intelligent public transportation, predictive maintenance for vehicles, rise in smart infrastructure.
The ai in transportation market size is expected to see rapid growth in the next few years. It will grow to $7.62 billion in 2028 at a compound annual growth rate (CAGR) of 18.5%. The growth in the forecast period can be attributed to expansion of electric and connected vehicles, integration of ai in urban mobility, focus on sustainable and smart cities, enhanced safety features, remote operations and monitoring. Major trends in the forecast period include advancements in ai algorithms and machine learning, government initiatives and regulations, autonomous vehicles advancements, predictive maintenance with ai, multimodal integration.
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The ai in transportation market covered in this report is segmented –
1) By Components: Hardware, Software
2) By Process: Signal Recognition, Object Recognition, Data Mining
3) By Technology Type: Natural Language Processing, Deep Learning, Computer Vision, Context-Awareness
4) By Application: Semi-Autonomous Truck, Truck Platooning, Predictive Maintenance, Precision And Mapping, Autonomous Truck, Machine Human Interface, Other Applications
North America was the largest region in the AI in transportation market in 2023. The regions covered in the ai in transportation market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa
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Major Driver Impacting Market Growth
The growing number of vehicles is expected to propel the growth of the AI in transportation market going forward. The increase in the number of vehicles on the road can be attributed to several factors, including population growth, economic development, and the inadequacy of public transportation infrastructure. AI can be used to optimize traffic flow, reduce congestion, and minimize travel times by analyzing real-time traffic data and adjusting signal timings and lane configurations accordingly. For instance, in March 2023, according to an article published by Hedges & Company, a US-based automotive digital marketing company, the registered cars in the US increased by 0.9% from 286.9 million cars in 2020 to 289.5 million cars in 2021. Therefore, the growing number of vehicles is driving the growth of the AI in transportation market.
Key Industry Players
Major companies operating in the ai in transportation market report are Volvo AB, Mercedes-Benz Group AG, Alphabet Inc., Intel Corporation, NVIDIA Corporation, Valeo SA, ZF Friedrichshafen AG, Continental AG, Magna International Inc., Microsoft Corporation, PACCAR Inc., Robert Bosch GmbH, Scania Group, Xevo Inc., Zonar Systems Inc., Daimler AG, Waymo LLC, Tesla Inc., Uber Technologies Inc. , Mobileye Global Inc., Aptiv plc, TomTom N.V., HERE Technologies, Siemens AG, Cisco Systems Inc., IBM Corporation, Cognata Ltd., Argo AI, Aurora Innovation Inc., Zoox Inc., Embark Technology Inc., TuSimple Holdings Inc., PlusAI Inc.
The ai in transportation market report table of contents includes:
1. Executive Summary
2. AI in Transportation Characteristics
3. AI in Transportation Trends And Strategies
4. AI in Transportation – Macro Economic Scenario
5. Global AI in Transportation Size and Growth
…..
32. Global AI in Transportation Competitive Benchmarking
33. Global AI in Transportation Competitive Dashboard
34. Key Mergers And Acquisitions In The AI in Transportation
35. AI in Transportation Future Outlook and Potential Analysis
36. Appendix
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