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市场调查报告书
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1604500

交通领域人工智慧市场:未来预测(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个工作天内

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简介目录

交通运输市场人工智慧预计将以11.80%的复合年增长率成长,市场规模从2024年的37.97亿美元增至2029年的61.96亿美元。

人工智慧技术和演算法正在整合到交通系统的尽可能多的领域,以提高效率、安全性和永续性。自动驾驶汽车的开发和部署的主要关键是利用电脑视觉、感测器融合、机器学习和深度学习来即时分析复杂的交通,以安全地穿越环境并检测周围的状况。

交通管理中的其他人工智慧应用领域包括利用感测器、摄影机和其他形式的资料来监控和优化城市和高速公路的交通流量。人工智慧技术透过检测和管理事故和其他安全相关问题等风险来确保交通系统的安全。电脑视觉系统分析交通和机场,机器学习模型编译分析结果以供进一步应用。基于人工智慧的最佳化演算法将透过减少排放、缓解拥塞以及鼓励替代燃料和替代交通途径来进一步改善交通流量。

交通人工智慧市场的驱动力

  • MaaS(移动即服务)的兴起有助于交通人工智慧市场的成长

MaaS 是一种整合解决方案,旨在在一个平台上提供交通服务,并可为人工智慧的实施创造槓桿。在 MaaS 系统中,人工智慧演算法用于透过优化路线和预测需求来提供个人化的旅行体验。虽然市场上有许多不同的产品,但日立由 Google Cloud 提供支援的车队营运预测维护整合了物联网资料、RCM 技术和人工智慧技术,以优化车队维护效率和资产可靠性。这是透过扩增实境、机器学习演算法和外部资料来完成的,以实现对任务关键型车队资产的即时检查和维修。

总体而言,MaaS 的出现正在推动人工智慧交通技术市场的发展,并为通勤和旅行提供更有效率、便利和永续的行动解决方案打开大门。

交通人工智慧市场的地理格局

  • 北美在预测期内将经历指数级增长

北美交通公司、政府组织和社区迅速采用人工智慧技术来提高其交通网路的效率、安全性和永续性。这种早期采用推动该地区在交通运输业的人工智慧方面处于领先地位。

总体而言,北美在人工智慧技术方面的领先地位及其支援生态系统、强大的工业影响力以及人工智慧在交通领域的早期采用,使其成为全球市场的强大参与企业。

为什么要购买这份报告?

  • 富有洞察力的分析:获得涵盖主要和新兴地区的深入市场洞察,重点关注客户细分、政府政策和社会经济因素、消费者偏好、行业明智以及其他子区隔。
  • 竞争格局:了解世界主要企业采取的策略策略,并了解透过正确的策略渗透市场的潜力。
  • 市场驱动因素和未来趋势:探索动态因素和关键市场趋势以及它们将如何影响未来市场发展。
  • 可行的建议:利用洞察力做出策略决策,以在动态环境中发现新的业务流和收益。
  • 受众广泛:对于新兴企业、研究机构、顾问、中小企业和大型企业有用且具有成本效益。

它有什么用?

产业与市场考量、商机评估、产品需求预测、打入市场策略、地理扩张、资本投资决策、法律规范与影响、新产品开发、竞争影响

分析范围

  • 历史资料与预测(2022-2029)
  • 成长机会、挑战、供应链前景、法规结构、顾客行为、趋势分析
  • 竞争对手定位、策略和市场占有率分析
  • 收益成长率与预测分析:按细分市场/地区(按国家)
  • 公司概况(策略、产品、财务资讯、主要趋势等)

目录

第一章简介

  • 市场概况
  • 市场定义
  • 分析范围
  • 市场区隔
  • 货币
  • 先决条件
  • 基准年和预测年时间表
  • 相关利益者的主要利益

第二章 分析方法

  • 分析设计
  • 分析过程

第三章执行摘要

  • 主要发现
  • CXO观点

第四章市场动态

  • 市场驱动因素
  • 市场限制因素
  • 波特五力分析
  • 产业价值链分析
  • 分析师观点

第五章 交通运输领域人工智慧市场:依技术分类

  • 介绍
  • 深度学习
  • 自然的学习过程
  • 机器学习
  • 其他的

第六章 交通运输领域人工智慧市场:依部署方式

  • 介绍
  • 本地

第七章 交通运输领域人工智慧市场:依应用分类

  • 介绍
  • 路线优化
  • 出货量预测
  • 预测性车辆维护
  • 即时车辆追踪
  • 其他的

第八章 交通运输领域人工智慧市场:按地区划分

  • 介绍
  • 北美洲
    • 依技术
    • 依部署方式
    • 按用途
    • 按国家/地区
  • 南美洲
    • 依技术
    • 依部署方式
    • 按用途
    • 按国家/地区
  • 欧洲
    • 依技术
    • 依部署方式
    • 按用途
    • 按国家/地区
  • 中东/非洲
    • 依技术
    • 依部署方式
    • 按用途
    • 按国家/地区
  • 亚太地区
    • 依技术
    • 依部署方式
    • 按用途
    • 按国家/地区

第九章竞争环境及分析

  • 主要企业及策略分析
  • 市场占有率分析
  • 企业合併(M&A)、协议与合作
  • 竞争对手仪表板

第十章 公司简介

  • Hitachi
  • Wialon (Gurtam)
  • AltexSoft
  • Planung Transport Verkehr GmbH
  • Integrated Roadways
  • Maticz
  • FlowSpace
  • Axestrack
简介目录
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