Artificial Intelligence (AI) In Transportation Market Size, Share, Opportunities, And Trends By Technology (Deep Learning, Natural learning process, Machine Learning, Others), By Deployment (Cloud, On-Premise), By Application (Route optimization, Shipping volume prediction, Predictive Fleet Maintenance, Real-time Vehicle tracking, Others), And By Geography - Forecasts From 2025 To 2030

Artificial Intelligence (AI) in Transportation Market Size:

The Artificial Intelligence (AI) in the Transportation Market is expected to grow at a CAGR of 12.15%, reaching a market size of US$7.560 billion in 2030 from US$4.260 billion in 2025.

Artificial Intelligence (AI) in Transportation Market Highlights:

• Mobility-as-a-Service Growth: AI optimizes MaaS platforms for efficient, personalized travel.
• Consumer Convenience Demand: AI enhances ride-sharing and route optimization for user satisfaction.
• Cloud and On-Premise Solutions: Flexible AI deployments boost transportation efficiency and scalability.
• North American Leadership: Early AI adoption drives regional transportation market innovation.

Artificial Intelligence (AI) in Transportation Market Trends:

The AI in transportation market is rapidly expanding, driven by the integration of artificial intelligence (AI) to enhance efficiency, safety, and sustainability in transportation systems. AI technologies, including computer vision, sensor fusion, machine learning, and deep learning, are pivotal in developing autonomous vehicles. These systems enable real-time analysis of complex traffic environments, ensuring safe navigation and obstacle detection for self-driving cars.

Beyond autonomous driving, AI optimizes traffic management through sensors, cameras, and data monitoring, improving traffic flow in cities and on highways. AI-powered systems analyze data to reduce congestion, lower emissions, and promote alternative fuels, aligning with sustainable transportation goals. For instance, computer vision monitors traffic patterns and airport operations, while machine learning models enhance predictive analytics for efficient traffic control.

AI also bolsters transportation safety by detecting accidents and security risks. Risk management systems leverage AI algorithms to mitigate threats, ensuring safer public transportation and logistics. Optimization algorithms further improve fuel efficiency and support eco-friendly modes, contributing to smart cities and green mobility.

North America leads the market due to technological advancements and government support, while Asia-Pacific, particularly China and Japan, grows rapidly due to urbanization and infrastructure investments. Challenges like data privacy and system integration persist, but AI innovations drive growth. The AI in transportation market thrives on autonomous systems, traffic optimization, and sustainability, reshaping modern mobility.

Artificial Intelligence (AI) in Transportation Market Growth Drivers:

• Rising Mobility-as-a-Service (MaaS) is contributing to AI in the transportation market

MaaS was developed to provide transport services on one platform and a unified solution that can create leverage for AI adoption. In MaaS systems, AI algorithms are applied to optimize routes, predict demand, and thus provide individual travel experiences. Of the various products in the market, the Hitachi Predictive Maintenance for Fleet Operations, powered by Google Cloud, brings together IoT data, RCM methodologies, and AI technology that optimize fleet maintenance efficiency and asset dependability. This is done through augmented reality, machine learning algorithms, and external data, allowing for real-time inspections and repairs of mission-critical fleet assets.

Overall, the advent of Mobility-as-a-Service is what boosts AI technologies in the transportation market, opening doors for commuting and travel to more efficient, convenient, and sustainable mobility solutions.

• Growing consumer demand for convenience is contributing to AI in the transportation market.

The progress of artificial intelligence technologies, coupled with machine learning tools, particularly deep learning. Riding around towns and other places on demand through ride-sharing, or ride-hailing, continues to attract thousands of consumers because this kind of service is very convenient and efficient. In each of these, AI is integrated and used to improve the ride-matching algorithm, optimizing routes, and enhancing user experience. SWIFT is a technology representing one of the most significant differences between traditional and smart organizations. It provides full control of all logistical operations on one platform, as well as flexibility, integration, and comprehensive reporting and analytics.

Convenient driving among consumers in the adoption of AI technologies for transportation usage has therefore increased. Generally, it can thus be observed that the growth in customer demand for convenience is mainly driving this adoption of the use of AI technologies for transportation, thereby making travel experiences more efficient, personal, and convenient for commuters and travelers.

• Rising use of cloud and on-premise services

Transportation stakeholders can access AI capabilities remotely through cloud-based deployment. It provides scalability, flexibility, and cost-effectiveness without the need for upfront infrastructure expenditures, enabling data analysis and novel solutions.

Transportation organizations use on-premises deployment to get better control over data and system configurations. Still, it requires a significant upfront investment in hardware, software, and knowledge.

Transportation organizations may tailor AI deployment models to suit individual requirements, resource restrictions, and strategic goals. Working with the right model, the stakeholders will then be able to maximize fully the benefits of AI across road, rail, air, and marine modes of transportation by enhancing safety, efficiency, and sustainability.

Artificial Intelligence (AI) in Transportation Market Restraints:

• Data privacy and security concerns hamper the market growth

The high volumes of transported data collected, stored, and analyzed raise privacy and security concerns. Breaking or breaching sensitive personal information can damage the trust of customers and hinder the adoption of AI-powered mobility technology.

Artificial Intelligence (AI) in Transportation Market Geographical Outlook:

• North America is witnessing exponential growth during the forecast period

North American transportation firms, government organizations, and communities were among the first to employ AI technology to improve transportation networks' efficiency, safety, and sustainability. This early adoption has driven the area to the top of AI in the transportation industry.

Overall, North America's leadership in AI technology, together with its supporting ecosystem, strong industrial presence, and early adoption of AI in transportation, establishes it as a prominent participant in the worldwide market.

Recent Developments in Artificial Intelligence (AI) in Transportation Market:

• In January 2024, the U.S. Department of Transportation created a $15 million Complete Streets Artificial Intelligence Initiative for Small Businesses. It held the promise of AI breakthroughs in the services of American small businesses to improve transportation systems. They will help decide, design, and implement complete streets.
• In September 2023, PTV Group's consultancy business will create a multimodal transport model for Hamburg, with an emphasis on passenger and commercial traffic. The model was developed two years ago and would serve as a data-driven planning tool for the authority for Transport and Mobility Transition, guiding Hamburg's mobility environment.

List of Top Artificial Intelligence (AI) in Transportation Companies:

• Hitachi
• Wialon (Gurtam)
• AltexSoft
• Planung Transport Verkehr GmbH
• Integrated Roadways

Artificial Intelligence (AI) in Transportation Market Scope:

Report Metric	Details
AI in Transportation Market Size in 2025	US$4.260 billion
AI in Transportation Market Size in 2030	US$7.560 billion
Growth Rate	CAGR of 12.15%
Study Period	2020 to 2030
Historical Data	2020 to 2023
Base Year	2025
Forecast Period	2025 – 2030
Forecast Unit (Value)	USD Billion
Segmentation	Technology Deployment Application Geography
Geographical Segmentation	North America, South America, Europe, Middle East and Africa, Asia Pacific
List of Major Companies in AI in Transportation Market	Hitachi Wialon (Gurtam) AltexSoft Maticz FlowSpace
Customization Scope	Free report customization with purchase

 

The Artificial Intelligence (AI) in transportation market is analyzed into the following segments:

• By Technology
  • Deep Learning
  • Natural learning process
  • Machine Learning
  • Others
• By Deployment
  • Cloud
  • On-Premise
• By Application
  • Route optimization
  • Shipping volume prediction
  • Predictive Fleet Maintenance
  • Real-time Vehicle tracking
  • Others
• By Geography
  • North America
    • USA
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • Germany
    • France
    • UK
    • Spain
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Israel
    • Others
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Indonesia
    • Taiwan
    • Others

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1. INTRODUCTION

1.1. Market Overview

1.2. Market Definition

1.3. Scope of the Study

1.4. Market Segmentation

1.5. Currency

1.6. Assumptions

1.7. Base and Forecast Years Timeline

1.8. Key Benefits to the Stakeholder

2. RESEARCH METHODOLOGY  

2.1. Research Design

2.2. Research Processes

3. EXECUTIVE SUMMARY

3.1. Key Findings

3.2. CXO Perspective

4. MARKET DYNAMICS

4.1. Market Drivers

4.2. Market Restraints

4.3. Porter’s Five Forces Analysis

4.3.1. Bargaining Power of Suppliers

4.3.2. Bargaining Power of Buyers

4.3.3. Threat of New Entrants

4.3.4. Threat of Substitutes

4.3.5. Competitive Rivalry in the Industry

4.4. Industry Value Chain Analysis

4.5. Analyst View

5. AI IN TRANSPORTATION MARKET BY TECHNOLOGY 

5.1. Introduction

5.2. Deep Learning

5.3. Natural learning process

5.4. Machine Learning

5.5. Others

6. AI IN TRANSPORTATION MARKET BY DEPLOYMENT

6.1. Introduction

6.2. Cloud

6.3. On-Premise

7. AI IN TRANSPORTATION MARKET BY APPLICATION

7.1. Introduction

7.2. Route optimization

7.3. Shipping volume prediction

7.4. Predictive Fleet Maintenance

7.5. Real-time Vehicle tracking

7.6. Others

8. AI IN TRANSPORTATION MARKET BY GEOGRAPHY

8.1. Introduction

8.2. North America

8.2.1. By Technology 

8.2.2. By Deployment

8.2.3. By Application 

8.2.4. By Country

8.2.4.1. USA

8.2.4.2. Canada

8.2.4.3. Mexico

8.3. South America

8.3.1. By Technology 

8.3.2. By Deployment

8.3.3. By Application 

8.3.4. By Country

8.3.4.1. Brazil

8.3.4.2. Argentina

8.3.4.3. Others

8.4. Europe

8.4.1. By Technology 

8.4.2. By Deployment

8.4.3. By Application 

8.4.4. By Country

8.4.4.1. Germany

8.4.4.2. France

8.4.4.3. UK

8.4.4.4. Spain

8.4.4.5. Others

8.5. Middle East and Africa

8.5.1. By Technology 

8.5.2. By Deployment

8.5.3. By Application 

8.5.4. By Country

8.5.4.1. Saudi Arabia

8.5.4.2. UAE

8.5.4.3. Israel

8.5.4.4. Others

8.6. Asia Pacific

8.6.1. By Technology 

8.6.2. By Deployment

8.6.3. By Application 

8.6.4. By Country

8.6.4.1. China

8.6.4.2. Japan

8.6.4.3. India

8.6.4.4. South Korea

8.6.4.5. Indonesia

8.6.4.6. Taiwan

8.6.4.7. Others

9. COMPETITIVE ENVIRONMENT AND ANALYSIS

9.1. Major Players and Strategy Analysis

9.2. Market Share Analysis

9.3. Mergers, Acquisitions, Agreements, and Collaborations

9.4. Competitive Dashboard

10. COMPANY PROFILES

10.1. Hitachi

10.2. Wialon (Gurtam)

10.3. AltexSoft

10.4. Planung Transport Verkehr GmbH

10.5. Integrated Roadways

10.6. Maticz

10.7. FlowSpace

10.8. Axestrack

Hitachi

Wialon (Gurtam)

AltexSoft

Planung Transport Verkehr GmbH

Integrated Roadways

Maticz

FlowSpace

Axestrack