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市場調查報告書
商品編碼
2092871

2034年交通運輸產業人工智慧市場預測：按技術、部署模式、交通途徑、企業規模、應用、最終用戶和地區分類的全球分析

AI in Transportation Market Forecasts to 2034 - Global Analysis By Technology, Deployment Mode, Transportation Mode, Enterprise Size, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，預計到 2026 年，全球交通運輸領域的 AI 市場規模將達到 89 億美元，到 2034 年將達到 564 億美元，預測期內的複合年成長率為 25.9%。

在交通運輸領域，人工智慧（AI）指的是應用機器學習、深度學習、自然語言處理、電腦視覺、情境感知運算、生成式人工智慧和邊緣人工智慧等人工智慧技術，來提升包括公路、鐵路、航空、海運和城市交通在內的各種交通途徑的運輸系統、營運和服務。人工智慧技術能夠實現自動駕駛汽車、交通管理、車輛管理、預測性維護、路線最佳化、智慧停車、駕駛員監控系統、最佳化貨運物流和乘客資訊系統等功能。

自動駕駛汽車和智慧型運輸系統（ITS）的需求日益成長

自動駕駛技術的快速發展和對智慧型運輸系統（ITS）日益成長的需求是推動交通運輸領域人工智慧市場發展的主要動力。人工智慧是自動駕駛車輛感知、決策和控制能力的基礎，使其能夠安全地在複雜環境中行駛。智慧型運輸系統（ITS）利用人工智慧進行交通預測、擁塞管理和事故偵測，進而提高整體交通效率。減少交通事故、改善出行和增強交通永續性的日益重視正在推動人工智慧的應用。隨著交通系統日益自動化和數據驅動，對先進人工智慧解決方案的需求也持續成長。

高昂的實施成本和資料隱私方面的擔憂

人工智慧在交通運輸領域的市場面臨許多挑戰，包括高昂的部署成本和資料隱私問題，這些問題可能會限制其應用。部署人工智慧解決方案需要對基礎設施、感測器、資料儲存、運算資源和專業知識進行大量投資。開發和部署用於交通運輸應用的人工智慧系統的複雜性也推高了成本。此外，人工智慧系統依賴大量數據，這引發了人們對數據收集、儲存和使用方面的隱私和安全擔憂。如何在確保符合資料保護條例的同時維持人工智慧系統的有效性是一項挑戰。這些成本和隱私問題可能會減緩人工智慧的普及，尤其對於預算有限的小規模企業而言更是如此。

擴大智慧城市和永續交通計劃

智慧城市計畫的擴展以及對永續交通解決方案日益成長的關注，為人工智慧在交通運輸領域的應用帶來了巨大的機會。智慧城市正在利用人工智慧最佳化交通、管理公共交通並提供停車解決方案，從而提高城市交通效率。減少排放和推廣永續交通途徑的舉措，催生了對人工智慧驅動的最佳化和管理解決方案的需求。人工智慧能夠實現智慧路線引導、最佳化共享出行和多模態規劃。隨著城市對智慧基礎設施和永續交通系統的投資不斷增加，對支援這些措施的人工智慧解決方案的需求也持續成長，這為人工智慧提供者創造了巨大的商機。

監管挑戰和安全問題

人工智慧在交通運輸領域的市場面臨許多重大威脅，包括監管挑戰和安全隱患，這些都可能影響其普及和廣泛應用。人工智慧在關鍵交通運輸應用中的部署引發了安全和責任問題，法律規範必須對此予以解決。確保人工智慧系統在各種運作條件下的安全性和可靠性至關重要，這需要進行嚴格的測試和檢驗。此外，目前交通運輸領域缺乏既定的人工智慧標準和指南，這給開發人員和營運商帶來了不確定性。不同地區的監管方式也為全球部署帶來了挑戰。這些監管和安全的挑戰可能會減緩人工智慧的普及速度，並增加交通運輸相關人員的合規負擔。

新型冠狀病毒（COVID-19）的影響：

新冠疫情對交通運輸領域的人工智慧市場產生了重大影響，加速了數位化技術的應用，並凸顯了高效能、高韌性運輸系統的重要性。疫情危機激發了人們對非接觸式技術、自動配送和遠端監控解決方案的濃厚興趣。疫情期間對供應鏈韌性和物流最佳化的重視，推動了對人工智慧貨運和物流解決方案的需求。隨著交通運輸系統的復甦，對效率、安全和永續性的關注持續推動人工智慧在各種交通途徑中的應用。疫情有效地加速了交通運輸領域的數位轉型，使人工智慧技術供應商從中受益。

在預測期內，機器學習領域預計將佔據最大的市場佔有率。

預計在預測期內，機器學習領域將佔據最大的市場佔有率，這主要得益於其在各種交通運輸應用場景中的廣泛應用，包括自動駕駛汽車、交通管理、預測性維護和路線最佳化。機器學習使系統能夠從資料中學習、識別模式並進行預測，而無需明確編程。機器學習在各種交通運輸應用中的多功能性是其主導的基礎。

在預測期內，深度學習領域預計將錄得最高的複合年成長率。

在預測期內，深度學習領域預計將呈現最高的成長率，這主要得益於其卓越的複雜非結構化數據處理能力，例如影像、影片和感測器數據，這些數據對於自動駕駛、電腦視覺和感知應用至關重要。深度學習能夠為自動駕駛車輛提供先進的感知能力，包括目標偵測、分類和追蹤。交通運輸領域應用的日益複雜化以及對高精度感知和決策支援日益成長的需求，都推動了該領域的成長。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於該地區眾多大型科技公司和先進汽車製造商的存在，以及對自動駕駛汽車開發和人工智慧研究的大量投資。該地區對技術創新和交通運輸轉型的高度重視正在推動人工智慧的普及應用。此外，成熟的技術生態系統、大量的研發投入以及完善的法規結構也促成了該地區的高普及率。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於快速的都市化、對智慧交通基礎設施投資的增加、自動駕駛技術的日益普及，以及中國、日本、印度、韓國和新加坡等國政府對人工智慧創新的大力支持。該地區人口密度大規模，交通網路不斷擴展，對人工智慧解決方案的需求也十分旺盛。此外，該地區的技術領先地位和對交通效率的重視，正在加速人工智慧在各種交通途徑中的應用。

免費客製化服務：

所有購買此報告的客戶均可從以下免費自訂選項中選擇一項：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 對主要公司進行SWOT分析（最多3家公司）
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章：全球交通運輸領域人工智慧市場：依技術分類

  • 機器學習（ML）
  • 深度學習
  • 自然語言處理（NLP）
  • 電腦視覺
  • 情境感知計算
  • 人工智慧世代
  • 邊緣人工智慧

第6章：全球交通運輸領域人工智慧市場：依部署模式分類

  • 雲
  • 現場
  • 混合

第7章：全球交通運輸領域人工智慧市場：以交通方式分類

  • 路
  • 鐵路
  • 空運
  • 船運
  • 城市交通

第8章：全球交通運輸領域人工智慧市場：依公司規模分類

  • 大公司
  • 中小企業

第9章：全球交通運輸領域人工智慧市場：按應用領域分類

  • 自動駕駛汽車
  • 交通管理
  • 車隊管理
  • 預測性保護
  • 路線最佳化
  • 智慧停車
  • 駕駛員監控系統
  • 貨物和物流最佳化
  • 乘客資訊系統

第10章：全球交通運輸領域人工智慧市場：以最終用戶分類

  • 政府和公共機構
  • 運輸和物流公司
  • 汽車原廠設備製造商
  • 大眾運輸
  • 航空
  • 鐵路營運商
  • 航運公司
  • 交通行動服務(MaaS) 供應商

第11章：全球交通運輸領域人工智慧市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第12章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第13章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第14章：公司簡介

  • NVIDIA Corporation
  • Alphabet Inc.
  • Tesla, Inc.
  • Intel Corporation
  • Siemens AG
  • IBM Corporation
  • Microsoft Corporation
  • Amazon Web Services, Inc.（AWS）
  • Huawei Technologies Co., Ltd.
  • Continental AG
  • Robert Bosch GmbH
  • Hitachi, Ltd.
  • Thales Group
  • Hexagon AB
  • HERE Technologies
Product Code: SMRC38035

According to Stratistics MRC, the Global AI in Transportation Market is accounted for $8.9 billion in 2026 and is expected to reach $56.4 billion by 2034, growing at a CAGR of 25.9% during the forecast period. AI in transportation refers to the application of artificial intelligence technologies including machine learning, deep learning, natural language processing, computer vision, context-aware computing, generative AI, and edge AI to enhance transportation systems, operations, and services across various modes including roadways, railways, airways, maritime, and urban mobility. AI technologies enable autonomous vehicles, traffic management, fleet management, predictive maintenance, route optimization, smart parking, driver monitoring systems, freight logistics optimization, and passenger information systems.

Market Dynamics:

Driver:

Growing demand for autonomous vehicles and intelligent transportation systems

The rapid advancement of autonomous vehicle technology and the increasing demand for intelligent transportation systems serve as primary catalysts for the AI in transportation market. AI is fundamental to autonomous vehicle perception, decision-making, and control capabilities, enabling vehicles to navigate complex environments safely. Intelligent transportation systems leverage AI for traffic prediction, congestion management, and incident detection, improving overall transportation efficiency. The growing focus on reducing traffic accidents, improving mobility, and enhancing transportation sustainability drives AI adoption. As transportation systems become increasingly automated and data-driven, the demand for sophisticated AI solutions continues to accelerate.

Restraint:

High implementation costs and data privacy concerns

The AI in transportation market faces significant challenges from high implementation costs and data privacy concerns that can limit adoption. Deploying AI solutions requires substantial investment in infrastructure, sensors, data storage, computing resources, and specialized expertise. The complexity of developing and deploying AI systems for transportation applications adds to implementation costs. Additionally, AI systems rely on vast amounts of data, raising privacy and security concerns about data collection, storage, and usage. Ensuring compliance with data protection regulations while maintaining AI system effectiveness presents challenges. These cost and privacy concerns can slow AI adoption, particularly among smaller organizations with limited budgets.

Opportunity:

Growth of smart cities and sustainable mobility initiatives

The expansion of smart city initiatives and the growing focus on sustainable mobility solutions present significant opportunities for AI in transportation. Smart cities leverage AI for traffic optimization, public transit management, and parking solutions, enhancing urban mobility efficiency. The focus on reducing emissions and promoting sustainable transportation modes creates demand for AI-enabled optimization and management solutions. AI enables intelligent routing, shared mobility optimization, and multimodal transportation planning. As cities invest in smart infrastructure and sustainable transportation systems, the demand for AI solutions that support these initiatives continues to grow, creating substantial opportunities for AI providers.

Threat:

Regulatory challenges and safety concerns

The AI in transportation market faces significant threats from regulatory challenges and safety concerns that can impact deployment and adoption. The deployment of AI in critical transportation applications raises safety and liability questions that regulatory frameworks must address. The complexity of ensuring AI system safety and reliability in diverse operating conditions requires rigorous testing and validation. Additionally, the lack of established standards and guidelines for AI in transportation creates uncertainty for developers and operators. Varying regulatory approaches across regions present challenges for global deployment. These regulatory and safety challenges can slow AI adoption and increase compliance burdens for transportation stakeholders.

Covid-19 Impact:

The COVID-19 pandemic significantly impacted the AI in transportation market by accelerating the adoption of digital technologies and highlighting the importance of efficient, resilient transportation systems. The crisis drove increased interest in contactless technologies, autonomous delivery, and remote monitoring solutions. The emphasis on supply chain resilience and logistics optimization during the pandemic increased demand for AI-enabled freight and logistics solutions. As transportation systems recovered, the focus on efficiency, safety, and sustainability continued to support AI adoption across various transportation modes. The pandemic effectively accelerated the digital transformation of transportation, benefiting AI technology providers.

The machine learning segment is expected to be the largest during the forecast period

The machine learning segment is expected to account for the largest market share during the forecast period, driven by its widespread application across various transportation use cases including autonomous vehicles, traffic management, predictive maintenance, and route optimization. Machine learning enables systems to learn from data, identify patterns, and make predictions without explicit programming. The versatility of machine learning across different transportation applications supports its dominant position.

The deep learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the deep learning segment is predicted to witness the highest growth rate, driven by its superior capabilities in processing complex, unstructured data including images, video, and sensor data critical for autonomous driving, computer vision, and perception applications. Deep learning enables advanced perception capabilities for autonomous vehicles, including object detection, classification, and tracking. The growing complexity of transportation applications and the need for high-accuracy perception and decision-making support segment growth.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, driven by the presence of major technology companies, advanced automotive manufacturers, and significant investment in autonomous vehicle development and AI research. The region's strong focus on technology innovation and transportation transformation supports AI adoption. Additionally, a mature technology ecosystem, substantial research and development investments, and supportive regulatory frameworks contribute to the high adoption rate in this region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, fueled by rapid urbanization, increasing investment in smart transportation infrastructure, growing adoption of autonomous vehicle technology, and strong government support for AI innovation across countries like China, Japan, India, South Korea, and Singapore. The region's large population centers and expanding transportation networks create substantial demand for AI solutions. The region's technology leadership and focus on transportation efficiency accelerate AI adoption across various transportation modes.

Key players in the market

Some of the key players in AI in Transportation Market include NVIDIA Corporation, Alphabet Inc., Tesla Inc., Intel Corporation, Siemens AG, IBM Corporation, Microsoft Corporation, Amazon Web Services Inc. (AWS), Huawei Technologies Co. Ltd., Continental AG, Robert Bosch GmbH, Hitachi Ltd., Thales Group, Hexagon AB, and HERE Technologies.

Key Developments:

In March 2025, NVIDIA Corporation announced a new AI computing platform for autonomous vehicles featuring enhanced deep learning capabilities and improved processing performance. The platform enables more sophisticated perception, decision-making, and control for autonomous driving applications.

In February 2025, Alphabet Inc. announced advancements in its AI-powered autonomous driving technology, expanding operational capabilities and enabling deployment in new geographic regions. The development demonstrates the maturity of AI for autonomous mobility applications.

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Natural Language Processing (NLP)
  • Computer Vision
  • Context-Aware Computing
  • Generative AI
  • Edge AI

Deployment Modes Covered:

  • Cloud
  • On-Premises
  • Hybrid

Transportation Modes Covered:

  • Roadways
  • Railways
  • Airways
  • Maritime
  • Urban Mobility

Enterprise Sizes Covered:

  • Large Enterprises
  • Small & Medium Enterprises (SMEs)

Applications Covered:

  • Autonomous Vehicles
  • Traffic Management
  • Fleet Management
  • Predictive Maintenance
  • Route Optimization
  • Smart Parking
  • Driver Monitoring Systems
  • Freight & Logistics Optimization
  • Passenger Information Systems

End Users Covered:

  • Government & Public Authorities
  • Transportation & Logistics Companies
  • Automotive OEMs
  • Public Transit Agencies
  • Airlines
  • Rail Operators
  • Maritime Operators
  • Mobility-as-a-Service (MaaS) Providers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global AI in Transportation Market, By Technology

  • 5.1 Machine Learning (ML)
  • 5.2 Deep Learning
  • 5.3 Natural Language Processing (NLP)
  • 5.4 Computer Vision
  • 5.5 Context-Aware Computing
  • 5.6 Generative AI
  • 5.7 Edge AI

6 Global AI in Transportation Market, By Deployment Mode

  • 6.1 Cloud
  • 6.2 On-Premises
  • 6.3 Hybrid

7 Global AI in Transportation Market, By Transportation Mode

  • 7.1 Roadways
  • 7.2 Railways
  • 7.3 Airways
  • 7.4 Maritime
  • 7.5 Urban Mobility

8 Global AI in Transportation Market, By Enterprise Size

  • 8.1 Large Enterprises
  • 8.2 Small & Medium Enterprises (SMEs)

9 Global AI in Transportation Market, By Application

  • 9.1 Autonomous Vehicles
  • 9.2 Traffic Management
  • 9.3 Fleet Management
  • 9.4 Predictive Maintenance
  • 9.5 Route Optimization
  • 9.6 Smart Parking
  • 9.7 Driver Monitoring Systems
  • 9.8 Freight & Logistics Optimization
  • 9.9 Passenger Information Systems

10 Global AI in Transportation Market, By End User

  • 10.1 Government & Public Authorities
  • 10.2 Transportation & Logistics Companies
  • 10.3 Automotive OEMs
  • 10.4 Public Transit Agencies
  • 10.5 Airlines
  • 10.6 Rail Operators
  • 10.7 Maritime Operators
  • 10.8 Mobility-as-a-Service (MaaS) Providers

11 Global AI in Transportation Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 NVIDIA Corporation
  • 14.2 Alphabet Inc.
  • 14.3 Tesla, Inc.
  • 14.4 Intel Corporation
  • 14.5 Siemens AG
  • 14.6 IBM Corporation
  • 14.7 Microsoft Corporation
  • 14.8 Amazon Web Services, Inc. (AWS)
  • 14.9 Huawei Technologies Co., Ltd.
  • 14.10 Continental AG
  • 14.11 Robert Bosch GmbH
  • 14.12 Hitachi, Ltd.
  • 14.13 Thales Group
  • 14.14 Hexagon AB
  • 14.15 HERE Technologies

List of Tables

  • Table 1 Global AI in Transportation Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI in Transportation Market Outlook, By Technology (2023-2034) ($MN)
  • Table 3 Global AI in Transportation Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 4 Global AI in Transportation Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 5 Global AI in Transportation Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 6 Global AI in Transportation Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 7 Global AI in Transportation Market Outlook, By Context-Aware Computing (2023-2034) ($MN)
  • Table 8 Global AI in Transportation Market Outlook, By Generative AI (2023-2034) ($MN)
  • Table 9 Global AI in Transportation Market Outlook, By Edge AI (2023-2034) ($MN)
  • Table 10 Global AI in Transportation Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 11 Global AI in Transportation Market Outlook, By Cloud (2023-2034) ($MN)
  • Table 12 Global AI in Transportation Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 13 Global AI in Transportation Market Outlook, By Hybrid (2023-2034) ($MN)
  • Table 14 Global AI in Transportation Market Outlook, By Transportation Mode (2023-2034) ($MN)
  • Table 15 Global AI in Transportation Market Outlook, By Roadways (2023-2034) ($MN)
  • Table 16 Global AI in Transportation Market Outlook, By Railways (2023-2034) ($MN)
  • Table 17 Global AI in Transportation Market Outlook, By Airways (2023-2034) ($MN)
  • Table 18 Global AI in Transportation Market Outlook, By Maritime (2023-2034) ($MN)
  • Table 19 Global AI in Transportation Market Outlook, By Urban Mobility (2023-2034) ($MN)
  • Table 20 Global AI in Transportation Market Outlook, By Enterprise Size (2023-2034) ($MN)
  • Table 21 Global AI in Transportation Market Outlook, By Large Enterprises (2023-2034) ($MN)
  • Table 22 Global AI in Transportation Market Outlook, By Small & Medium Enterprises (SMEs) (2023-2034) ($MN)
  • Table 23 Global AI in Transportation Market Outlook, By Application (2023-2034) ($MN)
  • Table 24 Global AI in Transportation Market Outlook, By Autonomous Vehicles (2023-2034) ($MN)
  • Table 25 Global AI in Transportation Market Outlook, By Traffic Management (2023-2034) ($MN)
  • Table 26 Global AI in Transportation Market Outlook, By Fleet Management (2023-2034) ($MN)
  • Table 27 Global AI in Transportation Market Outlook, By Predictive Maintenance (2023-2034) ($MN)
  • Table 28 Global AI in Transportation Market Outlook, By Route Optimization (2023-2034) ($MN)
  • Table 29 Global AI in Transportation Market Outlook, By Smart Parking (2023-2034) ($MN)
  • Table 30 Global AI in Transportation Market Outlook, By Driver Monitoring Systems (2023-2034) ($MN)
  • Table 31 Global AI in Transportation Market Outlook, By Freight & Logistics Optimization (2023-2034) ($MN)
  • Table 32 Global AI in Transportation Market Outlook, By Passenger Information Systems (2023-2034) ($MN)
  • Table 33 Global AI in Transportation Market Outlook, By End User (2023-2034) ($MN)
  • Table 34 Global AI in Transportation Market Outlook, By Government & Public Authorities (2023-2034) ($MN)
  • Table 35 Global AI in Transportation Market Outlook, By Transportation & Logistics Companies (2023-2034) ($MN)
  • Table 36 Global AI in Transportation Market Outlook, By Automotive OEMs (2023-2034) ($MN)
  • Table 37 Global AI in Transportation Market Outlook, By Public Transit Agencies (2023-2034) ($MN)
  • Table 38 Global AI in Transportation Market Outlook, By Airlines (2023-2034) ($MN)
  • Table 39 Global AI in Transportation Market Outlook, By Rail Operators (2023-2034) ($MN)
  • Table 40 Global AI in Transportation Market Outlook, By Maritime Operators (2023-2034) ($MN)
  • Table 41 Global AI in Transportation Market Outlook, By Mobility-as-a-Service (MaaS) Providers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) are also represented in the same manner as above.