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Global AI in Transportation Market - Forecast to 2030
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Market Overview

AI in Transportation Market is anticipated to register a significant CAGR of 15.3% during the review period. Interest for AI in transportation is being driven by the requirement for increased effectiveness, security, and consumer loyalty, as well as the potential for cost savings and natural advantages.

Man-made consciousness (AI) is playing an increasingly significant job in the advancement of transportation frameworks, especially those determined by the interest for autonomous vehicles. Self-driving vehicles depend on a scope of innovations, including PC vision, machine learning, and regular language processing, to see their current circumstance, simply decide, and speak with different vehicles and infrastructure. AI is especially valuable in transportation is in predicting and managing traffic stream. By analyzing ongoing information from sensors, cameras, and different sources, AI calculations can anticipate blockage and change traffic signals and other infrastructure to enhance traffic stream.

This can assist with reducing travel times, fuel utilization, and discharges, as well as further develop security. AI is making a distinction in the improvement of autonomous vehicle control frameworks. These frameworks utilize various sensors, including lidar, radar, and cameras, to see the climate and arrive at conclusions about how to explore it. Machine learning calculations are utilized to train these frameworks to perceive items and examples in the climate, and to go with choices in view of that information.

Market Segmentation

Based on components type, the AI in Transportation market segmentation hardware and software.

Based on end user type, the AI in Transportation market segmentation includes Truck platooning, Autonomous trucks, Semi-autonomous trucks, Autonomous Cars, Human-machine interface, Predictive maintenance, and Precision and mapping.

Regional Insights

The North American region dominates this market with a huge share of 40% in 2023 and is likewise assessed to observe exemplary development with a CAGR of 13.3% during the conjecture time frame. U.S. holds the biggest market share in AI for transportation market and is supposed to dominate the market through the estimate period.

The U.S. government is effectively investing in the turn of events and reception of AI in transportation. For instance, the Division of Transportation has sent off a few initiatives, for example, the Shrewd City Challenge, which aims to advance the utilization of AI and other cutting-edge innovations in transportation.

Asia Pacific is supposed to develop at the most elevated rate because of its augmented vehicle framework and bigger populace segment. In Asia Pacific, China dominated the market with a share of over 36% in 2022. With the growing populace and urbanization, the interest in proficient transportation administrations is increasing. AI upgrades transportation benefits and works on their effectiveness, as would be considered normal to drive the interest in AI in transportation. The Chinese government has been effectively promoting the utilization of AI and other trend setting innovations in transportation, as a feature of its more extensive push towards becoming a forerunner in cutting edge industries.

Major Players

Major players in the AI in Transportation market, are Bosch, Alphabet, Daimler, Intel, Continental, Magna, Man, Nauto, NVIDIA, IBM Corporation, Microsoft, Paccar, Scania, Valeo, Xevo, ZF, Peloton, Zonar, and Volvo.

TABLE OF CONTENTS

TABLE OF CONTENTS

1 EXECUTIVE SUMMARY 22

2 MARKET INTRODUCTION 24

3 RESEARCH METHODOLOGY 25

4 MARKET INSIGHTS 32

5 MARKET DYNAMICS 34

6 MARKET FACTOR ANALYSIS 40

7 GLOBAL AI IN TRANSPORTATION MARKET, BY OFFERING 43

8 GLOBAL AI IN TRANSPORTATION MARKET, BY ML TECHNOLOGY 48

9 GLOBAL AI IN TRANSPORTATION MARKET, BY APPLICATION 50

10 GLOBAL AI IN TRANSPORTATION MARKET, BY IOT COMMUNICATION TECHNOLOGY 53

11 GLOBAL AI IN TRANSPORTATION MARKET, BY REGION 55

12 COMPETITIVE LANDSCAPE 125

13 COMPANY PROFILES 131

14 BIBLIOGRAPHY 181

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