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 2024 To 2029

The AI in transportation market is expected to grow at a CAGR of 11.80%, reaching a market size of US$6.196 billion in 2029 from US$3.797 billion in 2024.

AI technology and algorithms are being integrated into as many areas of transportation systems as possible in a bid to increase efficiency, safety, and sustainability. The key to developing and deploying the autonomous car is using AI to traverse safe environments and detect surroundings using computer vision, sensor fusion, machine learning, and deep learning to analyze complicated traffic in real-time.

Other AI application areas in traffic management include sensors, cameras, and other forms of data monitoring and optimizing traffic flow in cities and highways. AI technologies have ensured safety and security within transportation systems by detecting and managing risks such as accidents and other security-related issues. Computer vision systems analyze traffic and airports, and machine learning models compile the analysis for further application. AI-based optimization algorithms further improve traffic flow by decreasing emissions, reducing congestion, and promoting alternative fuels and modes.

What are the AI in transportation market growth drivers?

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

MaaS was developed to provide transport services in 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 growth

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 in improving 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 drive 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.

Major challenges restraining AI in transportation market

• 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.

What are the key geographical trends shaping the AI in transportation market?

• 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 the 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 would create a multimodal transport model for Hamburg, with an emphasis on passenger and commercial traffic. The model was developed over two years ago and would serve as a data-driven planning tool for the authority for Transported and Mobility Transition, guiding Hamburg's mobility environment.

AI In Transportation Market Scope:

 

Report Metric	Details
AI in Transportation Market Size in 2024	US$3.797 billion
AI in Transportation Market Size in 2029	US$6.196 billion
Growth Rate	CAGR of 11.80%
Study Period	2019 to 2029
Historical Data	2019 to 2022
Base Year	2024
Forecast Period	2024 – 2029
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 AI in transportation market is segmented and analyzed as follows:

• 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

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