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市場調查報告書
商品編碼
1604500

交通領域人工智慧市場：未來預測（2024-2029）

AI in Transportation Market - Forecasts from 2024 to 2029
出版日期: | 出版商: Knowledge Sourcing Intelligence | 英文 149 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

交通運輸市場人工智慧預計將以11.80%的複合年成長率成長，市場規模從2024年的37.97億美元增至2029年的61.96億美元。

人工智慧技術和演算法正在整合到交通系統的盡可能多的領域，以提高效率、安全性和永續性。自動駕駛汽車的開發和部署的主要關鍵是利用電腦視覺、感測器融合、機器學習和深度學習來即時分析複雜的交通，以安全地穿越環境並檢測周圍的狀況。

交通管理中的其他人工智慧應用領域包括利用感測器、攝影機和其他形式的資料來監控和最佳化城市和高速公路的交通流量。人工智慧技術透過檢測和管理事故和其他安全相關問題等風險來確保交通系統的安全。電腦視覺系統分析交通和機場，機器學習模型編譯分析結果以供進一步應用。基於人工智慧的最佳化演算法將透過減少排放、緩解擁塞以及鼓勵替代燃料和替代交通途徑來進一步改善交通流量。

交通人工智慧市場的驅動力

  • MaaS（移動即服務）的興起有助於交通人工智慧市場的成長

MaaS 是一種整合解決方案，旨在在一個平台上提供交通服務，並可為人工智慧的實施創造槓桿。在 MaaS 系統中，人工智慧演算法用於透過最佳化路線和預測需求來提供個人化的旅行體驗。雖然市場上有許多不同的產品，但日立由 Google Cloud 提供支援的車隊營運預測維護整合了物聯網資料、RCM 技術和人工智慧技術，以最佳化車隊維護效率和資產可靠性。這是透過擴增實境、機器學習演算法和外部資料來完成的，以實現對任務關鍵型車隊資產的即時檢查和維修。

總體而言，MaaS 的出現正在推動人工智慧交通技術市場的發展，並為通勤和旅行提供更有效率、便利和永續的行動解決方案打開大門。

交通人工智慧市場的地理格局

  • 北美在預測期內將經歷指數級成長

北美交通公司、政府組織和社區迅速採用人工智慧技術來提高其交通網路的效率、安全性和永續性。這種早期採用推動該地區在交通運輸業的人工智慧方面處於領先地位。

總體而言，北美在人工智慧技術方面的領先地位及其支援生態系統、強大的工業影響力以及人工智慧在交通領域的早期採用，使其成為全球市場的強大參與企業。

為什麼要購買這份報告？

  • 富有洞察力的分析：獲得涵蓋主要和新興地區的深入市場洞察，重點關注客戶細分、政府政策和社會經濟因素、消費者偏好、行業明智以及其他子區隔。
  • 競爭格局：了解世界主要企業採取的策略策略，並了解透過正確的策略滲透市場的潛力。
  • 市場促進因素和未來趨勢：探索動態因素和關鍵市場趨勢以及它們將如何影響未來市場發展。
  • 可行的建議：利用洞察力做出策略決策，以在動態環境中發現新的業務流和收益。
  • 受眾廣泛：對於新興企業、研究機構、顧問、中小企業和大型企業有用且具有成本效益。

它有什麼用？

產業與市場考量、商機評估、產品需求預測、打入市場策略、地理擴張、資本投資決策、法律規範與影響、新產品開發、競爭影響

分析範圍

  • 歷史資料與預測（2022-2029）
  • 成長機會、挑戰、供應鏈前景、法規結構、顧客行為、趨勢分析
  • 競爭對手定位、策略和市場佔有率分析
  • 收益成長率與預測分析：按細分市場/地區（按國家）
  • 公司概況（策略、產品、財務資訊、主要趨勢等）

目錄

第1章簡介

  • 市場概況
  • 市場定義
  • 分析範圍
  • 市場區隔
  • 貨幣
  • 先決條件
  • 基準年和預測年時間表
  • 相關利益者的主要利益

第2章 分析方法

  • 分析設計
  • 分析過程

第3章執行摘要

  • 主要發現
  • CXO觀點

第4章市場動態

  • 市場促進因素
  • 市場限制因素
  • 波特五力分析
  • 產業價值鏈分析
  • 分析師觀點

第5章 交通運輸領域人工智慧市場：依技術分類

  • 介紹
  • 深度學習
  • 自然的學習過程
  • 機器學習
  • 其他

第6章 交通運輸領域人工智慧市場：依部署方式

  • 介紹
  • 雲
  • 本地

第7章 交通運輸領域人工智慧市場：依應用分類

  • 介紹
  • 路線最佳化
  • 出貨量預測
  • 預測性車輛維護
  • 即時車輛追蹤
  • 其他

第8章 交通運輸領域人工智慧市場：按地區分類

  • 介紹
  • 北美洲
    • 依技術
    • 依部署方式
    • 按用途
    • 按國家/地區
  • 南美洲
    • 依技術
    • 依部署方式
    • 按用途
    • 按國家/地區
  • 歐洲
    • 依技術
    • 依部署方式
    • 按用途
    • 按國家/地區
  • 中東/非洲
    • 依技術
    • 依部署方式
    • 按用途
    • 按國家/地區
  • 亞太地區
    • 依技術
    • 依部署方式
    • 按用途
    • 按國家/地區

第9章競爭環境及分析

  • 主要企業及策略分析
  • 市場佔有率分析
  • 企業合併(M&A)、協議與合作
  • 競爭對手儀表板

第10章 公司簡介

  • Hitachi
  • Wialon (Gurtam)
  • AltexSoft
  • Planung Transport Verkehr GmbH
  • Integrated Roadways
  • Maticz
  • FlowSpace
  • Axestrack
簡介目錄
Product Code: KSI061616759

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.

AI in transportation market 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.

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.

Reasons for buying this report:-

  • Insightful Analysis: Gain detailed market insights covering major as well as emerging geographical regions, focusing on customer segments, government policies and socio-economic factors, consumer preferences, industry verticals, other sub- segments.
  • Competitive Landscape: Understand the strategic maneuvers employed by key players globally to understand possible market penetration with the correct strategy.
  • Market Drivers & Future Trends: Explore the dynamic factors and pivotal market trends and how they will shape up future market developments.
  • Actionable Recommendations: Utilize the insights to exercise strategic decision to uncover new business streams and revenues in a dynamic environment.
  • Caters to a Wide Audience: Beneficial and cost-effective for startups, research institutions, consultants, SMEs, and large enterprises.

What do businesses use our reports for?

Industry and Market Insights, Opportunity Assessment, Product Demand Forecasting, Market Entry Strategy, Geographical Expansion, Capital Investment Decisions, Regulatory Framework & Implications, New Product Development, Competitive Intelligence

Report Coverage:

  • Historical data & forecasts from 2022 to 2029
  • Growth Opportunities, Challenges, Supply Chain Outlook, Regulatory Framework, Customer Behaviour, and Trend Analysis
  • Competitive Positioning, Strategies, and Market Share Analysis
  • Revenue Growth and Forecast Assessment of segments and regions including countries
  • Company Profiling (Strategies, Products, Financial Information, and Key Developments among others)

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

TABLE OF CONTENTS

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