查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1938371

人工智慧在交通運輸領域的市場－全球產業規模、佔有率、趨勢、機會及預測（按產品、機器學習、應用、流程、地區和競爭格局分類，2021-2031年）

Artificial Intelligence in Transportation Market - Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Offering, By Machine Learning, By Application, By Process, By Region & Competition, 2021-2031F
出版日期: | 出版商: TechSci Research | 英文 182 Pages | 商品交期: 2-3個工作天內

價格

We offer 8 hour analyst time for an additional research. Please contact us for the details.

簡介目錄

全球交通運輸領域的人工智慧市場預計將從 2025 年的 38.9 億美元成長到 2031 年的 105.7 億美元，複合年成長率達 18.13%。

該領域利用機器學習、電腦視覺和預測分析來實現自動駕駛、管理交通流量和最佳化物流。推動市場發展的關鍵因素是提高營運效率的迫切需求以及為提昇道路安全而日益成長的自動駕駛技術需求。此外，即時數據處理對於改善供應鏈營運和降低油耗的需求也是重要的成長催化劑，使其有別於短暫的應用趨勢。

市場概覽
預測期 2027-2031
市場規模：2025年 38.9億美元
市場規模：2031年 105.7億美元
複合年成長率：2026-2031年 18.13%
成長最快的細分市場 深度學習
最大的市場 亞太地區

阻礙市場快速成長的主要障礙之一是將先進的人工智慧解決方案與老舊基礎設施融合的複雜性，這個過程通常需要大量的資本投入和嚴格的安全檢驗。 SITA 的研究表明，到 2024 年，約 45% 的北美航空公司將把人工智慧列為關鍵技術優先事項，凸顯了航空業致力於克服這些現代化挑戰的決心。這項數據強調了策略性資源配置的必要性，以將傳統模式轉型為智慧化的、以數據為中心的交通網路。

市場促進因素

自動駕駛技術的快速發展正在從根本上改變整個產業，需要高效能運算和神經網路整合來確保安全導航。科技公司和製造商正大力投資利用感測器融合分析不斷變化的路況的自動駕駛系統，這需要大量的資金支持來檢驗安全通訊協定，才能進行大規模部署。在2024年7月舉行的Alphabet第二季財報音訊會議上，該公司核准了對Waymo的50億美元多年投資，以擴展其自動駕駛能力。這筆大規模資金注入凸顯了人工智慧在將原型發展成為商業性可行的出行服務過程中所發揮的關鍵作用，並將直接影響對車載推理晶片和訓練基礎設施的需求。

此外，智慧交通管理系統的採用是主要驅動力，它利用即時分析來緩解都市區擁塞並提高市政基礎設施的效率。地方政府正擴大部署自適應交通號誌控制和智慧監控網路，利用電腦視覺技術來最佳化交通流量並減少排放氣體。根據美國運輸部2024年3月發布的題為「拜登-哈里斯政府宣布津貼」的新聞稿，該政府向34個市政當局撥款5000萬美元用於智慧交通（SMART）津貼，以部署先進的節能技術。這些公共資金正在推動私營部門的銷售，並為供應商培養一個強大的生態系統。例如，英偉達（NVIDIA）報告稱，其2024年汽車業務年收入成長21%，達到11億美元，這主要得益於其人工智慧駕駛座和自動駕駛平台的普及應用。

市場挑戰

將人工智慧整合到現有交通運輸系統中面臨現代運算需求與傳統基礎設施不匹配的重大阻礙。航空、鐵路和物流領域的許多營運框架已有數十年歷史，缺乏支援複雜機器學習模型所需的連接性和資料架構。對這些底層系統進行現代化改造需要大量資金投入，並且需要經過漫長的安全檢驗流程才能滿足法規要求。這些技術和資金障礙造成了瓶頸，阻礙了實驗性技術成為核心營運組件，從而減緩了整體市場發展勢頭。

這項障礙在目前的產業應用指標中顯而易見，這些指標顯示前導測試與全面部署之間存在巨大差距。根據國際鐵路聯盟（UIC）預測，到2024年，僅約25%的鐵路公司能夠成功擴展多個人工智慧應用案例，而大部分工作仍停留在實驗階段。這些數據表明，儘管提高效率的需求顯而易見，但在老舊硬體上整合新的人工智慧功能所面臨的實際挑戰，有效地阻礙了市場發展，導致只能取得漸進式進展，而無法實現變革性擴張。

市場趨勢

採用預測性維護模型進行機隊最佳化正成為一項關鍵趨勢，從根本上改變了營運商管理資產生命週期和計劃外停機時間的方式。鐵路營運商和航空公司正從定期檢查轉向基於狀態的維護方法，利用機器學習演算法分析感測器數據，並高精度地預測零件故障。這種轉變不僅減少了營運中斷，還透過主動預測零件需求來最佳化庫存管理。根據Delta航空2024年3月發布的新聞稿（宣布「Delta科技營運榮獲《航空週刊》2024年度最高榮譽獎」），該航空公司的人工智慧驅動專案APEX已將物料需求預測的準確率提高到90%以上，凸顯了此類技術對資源分配和維護效率的顯著影響。

同時，人工智慧驅動的末端配送無人機和機器人的興起正在改變物流格局，其目標是供應鏈中最昂貴的環節。企業正在使用配備先進導航系統的自動駕駛車輛和無人機，在都市區實現快速、無接觸配送，有效避免地面交通堵塞。這項技術正日益受到物流業者和零售商的青睞，他們希望在滿足消費者對即時服務需求的同時，降低配送成本。 Wing公司於2024年9月發布的《超越貨架》（Beyond the Aisle）報告指出，研究表明，轉向自動駕駛無人機系統可以將配送成本降低高達60%，凸顯了推動這些自動化解決方案廣泛應用的強大經濟動力。

目錄

第1章概述

第2章調查方法

第3章執行摘要

第4章：客戶評價

第5章 全球交通運輸人工智慧市場展望

  • 市場規模及預測
    • 按金額
  • 市佔率及預測
    • 依交付形式（硬體和軟體）
    • 透過機器學習（深度學習、電腦視覺、情境察覺、自然語言處理）
    • 按應用領域（半自動駕駛、全自動駕駛、人機互動、車隊行駛）
    • 按過程（資料探勘、影像識別、訊號辨識）
    • 按地區
    • 按公司（2025 年）
  • 市場地圖

6. 北美交通運輸領域人工智慧市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 北美洲：國家分析
    • 美國
    • 加拿大
    • 墨西哥

7. 歐洲交通運輸領域人工智慧市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 歐洲：國家分析
    • 德國
    • 法國
    • 英國
    • 義大利
    • 西班牙

8. 亞太地區人工智慧在交通運輸領域的市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 亞太地區：國家分析
    • 中國
    • 印度
    • 日本
    • 韓國
    • 澳洲

9. 中東和非洲交通運輸領域人工智慧市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 中東和非洲：國家分析
    • 沙烏地阿拉伯
    • 阿拉伯聯合大公國
    • 南非

第10章：南美洲交通運輸領域人工智慧市場展望

  • 市場規模及預測
  • 市佔率及預測
  • 南美洲：國家分析
    • 巴西
    • 哥倫比亞
    • 阿根廷

第11章 市場動態

  • 促進要素
  • 任務

第12章 市場趨勢與發展

  • 併購
  • 產品發布
  • 最新進展

第13章 全球交通運輸人工智慧市場：SWOT分析

第14章：波特五力分析

  • 產業競爭
  • 新進入者的可能性
  • 供應商電力
  • 顧客權力
  • 替代品的威脅

第15章 競爭格局

  • NVIDIA Corporation
  • Microsoft Corporation
  • Intel Corporation
  • IBM Corporation
  • Alphabet Inc.
  • Tesla, Inc.
  • Cognex Corporation
  • Valeo SA
  • Continental AG
  • Robert Bosch GmbH

第16章 策略建議

第17章：關於研究公司及免責聲明

簡介目錄
Product Code: 1631

The Global Artificial Intelligence in Transportation Market is projected to expand from USD 3.89 Billion in 2025 to USD 10.57 Billion by 2031, registering a CAGR of 18.13%. This sector encompasses the utilization of machine learning, computer vision, and predictive analytics to enable autonomous operations, manage traffic flow, and optimize logistics. The market is primarily driven by the urgent need for operational efficiency and the rising demand for autonomous vehicle technologies aimed at enhancing road safety. Additionally, the requirement for real-time data processing to improve supply chain operations and minimize fuel usage serves as a significant growth catalyst, distinguishing it from fleeting adoption trends.

Market Overview
Forecast Period2027-2031
Market Size 2025USD 3.89 Billion
Market Size 2031USD 10.57 Billion
CAGR 2026-203118.13%
Fastest Growing SegmentDeep Learning
Largest MarketAsia Pacific

One major hurdle hindering rapid market growth is the complexity of merging advanced AI solutions with aging infrastructure, a process often involving substantial capital costs and strict safety validation. According to SITA, nearly 45% of North American airlines designated artificial intelligence as their primary technology priority in 2024, highlighting the industry's dedication to overcoming these modernization challenges. This statistic emphasizes the necessity of strategic resource allocation to transform traditional frameworks into intelligent, data-centric transportation networks.

Market Driver

The rapid progression of autonomous vehicle technologies is fundamentally transforming the industry by requiring high-performance computing and neural network integration to ensure safe navigation. Technology companies and manufacturers are heavily investing in self-driving systems that use sensor fusion to analyze changing road conditions, necessitating substantial financial support to validate safety protocols prior to mass adoption. According to Alphabet Inc., in its 'Second Quarter 2024 Results' conference call in July 2024, the company authorized a new multi-year investment of $5 billion into Waymo to scale its autonomous driving capabilities. This significant capital injection underscores the critical role of artificial intelligence in evolving prototypes into commercially viable mobility services, which directly impacts the demand for onboard inference chips and training infrastructure.

Furthermore, the deployment of smart traffic management systems serves as a primary driver, utilizing real-time analytics to alleviate urban congestion and improve the efficiency of municipal infrastructure. Local governments are increasingly implementing adaptive signal controls and intelligent monitoring networks powered by computer vision to optimize traffic movement and lower emissions. According to the U.S. Department of Transportation, in a March 2024 press release titled 'Biden-Harris Administration Announces Grants', the administration allocated $50 million in SMART grants to 34 communities specifically to implement advanced efficiency-enhancing technologies. This public funding bolsters private sector sales, fostering a strong ecosystem for vendors; for instance, NVIDIA reported in 2024 that its full-year automotive revenue increased by 21% to $1.1 billion, largely fueled by the uptake of its AI cockpit and self-driving platforms.

Market Challenge

The incorporation of artificial intelligence into existing transportation systems is significantly hindered by the mismatch between modern computational needs and prevalent legacy infrastructure. Many operational frameworks within aviation, rail, and logistics were established decades ago and lack the necessary connectivity and data architecture to support intricate machine learning models. Overhauling these fundamental systems requires prohibitive capital expenditures and involves protracted safety validation processes to satisfy regulatory mandates. These technical and financial obstacles form a bottleneck that stops experimental technologies from becoming core operational components, thereby slowing overall market momentum.

This impediment is evident in current industry adoption metrics, where a substantial disparity exists between pilot testing and full-scale deployment. According to the International Union of Railways, only approximately 25% of railway companies had successfully scaled multiple AI use cases in 2024, with the majority of initiatives stuck in experimental stages. This data illustrates that, despite the obvious need for efficiency, the practical challenges of integrating new AI capabilities with outdated hardware effectively restrain the market, restricting its progress to incremental rather than transformative expansion.

Market Trends

The adoption of predictive maintenance models for fleet optimization is becoming a pivotal trend, fundamentally changing how operators handle asset lifecycles and unexpected downtime. Rail operators and airlines are shifting from scheduled servicing to condition-based approaches, utilizing machine learning algorithms to analyze sensor data and forecast component failures with high accuracy. This transition not only reduces operational interruptions but also optimizes inventory management by predicting part needs ahead of time. According to Delta Air Lines, in the March 2024 'Delta TechOps honored with Aviation Week's 2024 Grand Laureate Award' press release, the airline stated that its AI-powered APEX program boosted predictive material demand accuracy to over 90%, highlighting the significant influence of these technologies on resource allocation and maintenance efficiency.

Concurrently, the rise of AI-enabled last-mile delivery drones and robots is reshaping logistics by targeting the most costly portion of the supply chain. Businesses are utilizing autonomous ground and aerial vehicles outfitted with sophisticated navigation systems to perform fast, contactless deliveries in urban areas, effectively avoiding ground traffic congestion. This technology is becoming increasingly popular among logistics providers and retailers aiming to cut fulfillment costs while satisfying consumer demands for immediate service. According to Wing, in its September 2024 'Beyond the Aisle' report, research suggests that companies could lower delivery expenses by up to 60% by switching to autonomous drone systems, emphasizing the strong economic drivers behind the broad acceptance of these automated solutions.

Key Market Players

  • NVIDIA Corporation
  • Microsoft Corporation
  • Intel Corporation
  • IBM Corporation
  • Alphabet Inc.
  • Tesla, Inc.
  • Cognex Corporation
  • Valeo SA
  • Continental AG
  • Robert Bosch GmbH

Report Scope

In this report, the Global Artificial Intelligence in Transportation Market has been segmented into the following categories, in addition to the industry trends which have also been detailed below:

Artificial Intelligence in Transportation Market, By Offering

  • Hardware and Software

Artificial Intelligence in Transportation Market, By Machine Learning

  • Deep Learning
  • Computer Vision
  • Context Awareness
  • NLP

Artificial Intelligence in Transportation Market, By Application

  • Semi & Full-Autonomous
  • HMI
  • Platooning

Artificial Intelligence in Transportation Market, By Process

  • Data Mining
  • Image Recognition and Signal Recognition

Artificial Intelligence in Transportation Market, By Region

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • France
    • United Kingdom
    • Italy
    • Germany
    • Spain
  • Asia Pacific
    • China
    • India
    • Japan
    • Australia
    • South Korea
  • South America
    • Brazil
    • Argentina
    • Colombia
  • Middle East & Africa
    • South Africa
    • Saudi Arabia
    • UAE

Competitive Landscape

Company Profiles: Detailed analysis of the major companies present in the Global Artificial Intelligence in Transportation Market.

Available Customizations:

Global Artificial Intelligence in Transportation Market report with the given market data, TechSci Research offers customizations according to a company's specific needs. The following customization options are available for the report:

Company Information

  • Detailed analysis and profiling of additional market players (up to five).

Table of Contents

1. Product Overview

  • 1.1. Market Definition
  • 1.2. Scope of the Market
    • 1.2.1. Markets Covered
    • 1.2.2. Years Considered for Study
    • 1.2.3. Key Market Segmentations

2. Research Methodology

  • 2.1. Objective of the Study
  • 2.2. Baseline Methodology
  • 2.3. Key Industry Partners
  • 2.4. Major Association and Secondary Sources
  • 2.5. Forecasting Methodology
  • 2.6. Data Triangulation & Validation
  • 2.7. Assumptions and Limitations

3. Executive Summary

  • 3.1. Overview of the Market
  • 3.2. Overview of Key Market Segmentations
  • 3.3. Overview of Key Market Players
  • 3.4. Overview of Key Regions/Countries
  • 3.5. Overview of Market Drivers, Challenges, Trends

4. Voice of Customer

5. Global Artificial Intelligence in Transportation Market Outlook

  • 5.1. Market Size & Forecast
    • 5.1.1. By Value
  • 5.2. Market Share & Forecast
    • 5.2.1. By Offering (Hardware and Software)
    • 5.2.2. By Machine Learning (Deep Learning, Computer Vision, Context Awareness, NLP)
    • 5.2.3. By Application (Semi & Full-Autonomous, HMI, Platooning)
    • 5.2.4. By Process (Data Mining, Image Recognition and Signal Recognition)
    • 5.2.5. By Region
    • 5.2.6. By Company (2025)
  • 5.3. Market Map

6. North America Artificial Intelligence in Transportation Market Outlook

  • 6.1. Market Size & Forecast
    • 6.1.1. By Value
  • 6.2. Market Share & Forecast
    • 6.2.1. By Offering
    • 6.2.2. By Machine Learning
    • 6.2.3. By Application
    • 6.2.4. By Process
    • 6.2.5. By Country
  • 6.3. North America: Country Analysis
    • 6.3.1. United States Artificial Intelligence in Transportation Market Outlook
      • 6.3.1.1. Market Size & Forecast
        • 6.3.1.1.1. By Value
      • 6.3.1.2. Market Share & Forecast
        • 6.3.1.2.1. By Offering
        • 6.3.1.2.2. By Machine Learning
        • 6.3.1.2.3. By Application
        • 6.3.1.2.4. By Process
    • 6.3.2. Canada Artificial Intelligence in Transportation Market Outlook
      • 6.3.2.1. Market Size & Forecast
        • 6.3.2.1.1. By Value
      • 6.3.2.2. Market Share & Forecast
        • 6.3.2.2.1. By Offering
        • 6.3.2.2.2. By Machine Learning
        • 6.3.2.2.3. By Application
        • 6.3.2.2.4. By Process
    • 6.3.3. Mexico Artificial Intelligence in Transportation Market Outlook
      • 6.3.3.1. Market Size & Forecast
        • 6.3.3.1.1. By Value
      • 6.3.3.2. Market Share & Forecast
        • 6.3.3.2.1. By Offering
        • 6.3.3.2.2. By Machine Learning
        • 6.3.3.2.3. By Application
        • 6.3.3.2.4. By Process

7. Europe Artificial Intelligence in Transportation Market Outlook

  • 7.1. Market Size & Forecast
    • 7.1.1. By Value
  • 7.2. Market Share & Forecast
    • 7.2.1. By Offering
    • 7.2.2. By Machine Learning
    • 7.2.3. By Application
    • 7.2.4. By Process
    • 7.2.5. By Country
  • 7.3. Europe: Country Analysis
    • 7.3.1. Germany Artificial Intelligence in Transportation Market Outlook
      • 7.3.1.1. Market Size & Forecast
        • 7.3.1.1.1. By Value
      • 7.3.1.2. Market Share & Forecast
        • 7.3.1.2.1. By Offering
        • 7.3.1.2.2. By Machine Learning
        • 7.3.1.2.3. By Application
        • 7.3.1.2.4. By Process
    • 7.3.2. France Artificial Intelligence in Transportation Market Outlook
      • 7.3.2.1. Market Size & Forecast
        • 7.3.2.1.1. By Value
      • 7.3.2.2. Market Share & Forecast
        • 7.3.2.2.1. By Offering
        • 7.3.2.2.2. By Machine Learning
        • 7.3.2.2.3. By Application
        • 7.3.2.2.4. By Process
    • 7.3.3. United Kingdom Artificial Intelligence in Transportation Market Outlook
      • 7.3.3.1. Market Size & Forecast
        • 7.3.3.1.1. By Value
      • 7.3.3.2. Market Share & Forecast
        • 7.3.3.2.1. By Offering
        • 7.3.3.2.2. By Machine Learning
        • 7.3.3.2.3. By Application
        • 7.3.3.2.4. By Process
    • 7.3.4. Italy Artificial Intelligence in Transportation Market Outlook
      • 7.3.4.1. Market Size & Forecast
        • 7.3.4.1.1. By Value
      • 7.3.4.2. Market Share & Forecast
        • 7.3.4.2.1. By Offering
        • 7.3.4.2.2. By Machine Learning
        • 7.3.4.2.3. By Application
        • 7.3.4.2.4. By Process
    • 7.3.5. Spain Artificial Intelligence in Transportation Market Outlook
      • 7.3.5.1. Market Size & Forecast
        • 7.3.5.1.1. By Value
      • 7.3.5.2. Market Share & Forecast
        • 7.3.5.2.1. By Offering
        • 7.3.5.2.2. By Machine Learning
        • 7.3.5.2.3. By Application
        • 7.3.5.2.4. By Process

8. Asia Pacific Artificial Intelligence in Transportation Market Outlook

  • 8.1. Market Size & Forecast
    • 8.1.1. By Value
  • 8.2. Market Share & Forecast
    • 8.2.1. By Offering
    • 8.2.2. By Machine Learning
    • 8.2.3. By Application
    • 8.2.4. By Process
    • 8.2.5. By Country
  • 8.3. Asia Pacific: Country Analysis
    • 8.3.1. China Artificial Intelligence in Transportation Market Outlook
      • 8.3.1.1. Market Size & Forecast
        • 8.3.1.1.1. By Value
      • 8.3.1.2. Market Share & Forecast
        • 8.3.1.2.1. By Offering
        • 8.3.1.2.2. By Machine Learning
        • 8.3.1.2.3. By Application
        • 8.3.1.2.4. By Process
    • 8.3.2. India Artificial Intelligence in Transportation Market Outlook
      • 8.3.2.1. Market Size & Forecast
        • 8.3.2.1.1. By Value
      • 8.3.2.2. Market Share & Forecast
        • 8.3.2.2.1. By Offering
        • 8.3.2.2.2. By Machine Learning
        • 8.3.2.2.3. By Application
        • 8.3.2.2.4. By Process
    • 8.3.3. Japan Artificial Intelligence in Transportation Market Outlook
      • 8.3.3.1. Market Size & Forecast
        • 8.3.3.1.1. By Value
      • 8.3.3.2. Market Share & Forecast
        • 8.3.3.2.1. By Offering
        • 8.3.3.2.2. By Machine Learning
        • 8.3.3.2.3. By Application
        • 8.3.3.2.4. By Process
    • 8.3.4. South Korea Artificial Intelligence in Transportation Market Outlook
      • 8.3.4.1. Market Size & Forecast
        • 8.3.4.1.1. By Value
      • 8.3.4.2. Market Share & Forecast
        • 8.3.4.2.1. By Offering
        • 8.3.4.2.2. By Machine Learning
        • 8.3.4.2.3. By Application
        • 8.3.4.2.4. By Process
    • 8.3.5. Australia Artificial Intelligence in Transportation Market Outlook
      • 8.3.5.1. Market Size & Forecast
        • 8.3.5.1.1. By Value
      • 8.3.5.2. Market Share & Forecast
        • 8.3.5.2.1. By Offering
        • 8.3.5.2.2. By Machine Learning
        • 8.3.5.2.3. By Application
        • 8.3.5.2.4. By Process

9. Middle East & Africa Artificial Intelligence in Transportation Market Outlook

  • 9.1. Market Size & Forecast
    • 9.1.1. By Value
  • 9.2. Market Share & Forecast
    • 9.2.1. By Offering
    • 9.2.2. By Machine Learning
    • 9.2.3. By Application
    • 9.2.4. By Process
    • 9.2.5. By Country
  • 9.3. Middle East & Africa: Country Analysis
    • 9.3.1. Saudi Arabia Artificial Intelligence in Transportation Market Outlook
      • 9.3.1.1. Market Size & Forecast
        • 9.3.1.1.1. By Value
      • 9.3.1.2. Market Share & Forecast
        • 9.3.1.2.1. By Offering
        • 9.3.1.2.2. By Machine Learning
        • 9.3.1.2.3. By Application
        • 9.3.1.2.4. By Process
    • 9.3.2. UAE Artificial Intelligence in Transportation Market Outlook
      • 9.3.2.1. Market Size & Forecast
        • 9.3.2.1.1. By Value
      • 9.3.2.2. Market Share & Forecast
        • 9.3.2.2.1. By Offering
        • 9.3.2.2.2. By Machine Learning
        • 9.3.2.2.3. By Application
        • 9.3.2.2.4. By Process
    • 9.3.3. South Africa Artificial Intelligence in Transportation Market Outlook
      • 9.3.3.1. Market Size & Forecast
        • 9.3.3.1.1. By Value
      • 9.3.3.2. Market Share & Forecast
        • 9.3.3.2.1. By Offering
        • 9.3.3.2.2. By Machine Learning
        • 9.3.3.2.3. By Application
        • 9.3.3.2.4. By Process

10. South America Artificial Intelligence in Transportation Market Outlook

  • 10.1. Market Size & Forecast
    • 10.1.1. By Value
  • 10.2. Market Share & Forecast
    • 10.2.1. By Offering
    • 10.2.2. By Machine Learning
    • 10.2.3. By Application
    • 10.2.4. By Process
    • 10.2.5. By Country
  • 10.3. South America: Country Analysis
    • 10.3.1. Brazil Artificial Intelligence in Transportation Market Outlook
      • 10.3.1.1. Market Size & Forecast
        • 10.3.1.1.1. By Value
      • 10.3.1.2. Market Share & Forecast
        • 10.3.1.2.1. By Offering
        • 10.3.1.2.2. By Machine Learning
        • 10.3.1.2.3. By Application
        • 10.3.1.2.4. By Process
    • 10.3.2. Colombia Artificial Intelligence in Transportation Market Outlook
      • 10.3.2.1. Market Size & Forecast
        • 10.3.2.1.1. By Value
      • 10.3.2.2. Market Share & Forecast
        • 10.3.2.2.1. By Offering
        • 10.3.2.2.2. By Machine Learning
        • 10.3.2.2.3. By Application
        • 10.3.2.2.4. By Process
    • 10.3.3. Argentina Artificial Intelligence in Transportation Market Outlook
      • 10.3.3.1. Market Size & Forecast
        • 10.3.3.1.1. By Value
      • 10.3.3.2. Market Share & Forecast
        • 10.3.3.2.1. By Offering
        • 10.3.3.2.2. By Machine Learning
        • 10.3.3.2.3. By Application
        • 10.3.3.2.4. By Process

11. Market Dynamics

  • 11.1. Drivers
  • 11.2. Challenges

12. Market Trends & Developments

  • 12.1. Merger & Acquisition (If Any)
  • 12.2. Product Launches (If Any)
  • 12.3. Recent Developments

13. Global Artificial Intelligence in Transportation Market: SWOT Analysis

14. Porter's Five Forces Analysis

  • 14.1. Competition in the Industry
  • 14.2. Potential of New Entrants
  • 14.3. Power of Suppliers
  • 14.4. Power of Customers
  • 14.5. Threat of Substitute Products

15. Competitive Landscape

  • 15.1. NVIDIA Corporation
    • 15.1.1. Business Overview
    • 15.1.2. Products & Services
    • 15.1.3. Recent Developments
    • 15.1.4. Key Personnel
    • 15.1.5. SWOT Analysis
  • 15.2. Microsoft Corporation
  • 15.3. Intel Corporation
  • 15.4. IBM Corporation
  • 15.5. Alphabet Inc.
  • 15.6. Tesla, Inc.
  • 15.7. Cognex Corporation
  • 15.8. Valeo SA
  • 15.9. Continental AG
  • 15.10. Robert Bosch GmbH

16. Strategic Recommendations

17. About Us & Disclaimer