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人工智慧驅動的車隊智慧市場預測至2034年——全球解決方案類型、人工智慧技術、部署模式、車隊類型、最終用戶和區域分析

AI-Based Fleet Intelligence Market Forecasts to 2034 - Global Analysis By Solution Type, AI Technology, Deployment, Fleet Type, End User, and Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 200+ Pages | 商品交期: 2-3個工作天內

價格

根據 Stratistics MRC 的數據，全球人工智慧驅動的艦隊智慧市場預計將在 2026 年達到 74 億美元，並在預測期內以 18.4% 的複合年成長率成長，到 2034 年達到 285 億美元。

人工智慧驅動的車隊智慧是指利用人工智慧、機器學習、遠端資訊處理、物聯網感測器和預測分析等技術來監控、分析和最佳化商用車隊的運作狀況。這些解決方案能夠即時洞察車輛狀態、駕駛員行為、路線最佳化、油耗、維護計劃、安全性和營運效率。人工智慧驅動的車隊智慧能夠實現預測性決策，降低營運成本，提高資產利用率，並增強車隊的永續性。聯網汽車、自動化物流和數據驅動型運輸管理的日益普及，正在推動全球對人工智慧驅動的車隊智慧解決方案的需求。

營運最佳化的需求日益成長

企業越來越重視最佳化車隊運營，以降低成本並提高效率。數位化智慧平台正被部署用於簡化路線規劃、燃油管理和預測性維護。企業正在投資人工智慧驅動的解決方案，以提供對車輛性能的即時洞察。政府正在支持現代化工作，作為智慧運輸計畫的一部分。駕駛員和乘客正受益於更安全、更可靠的車隊服務。遠端資訊處理、物聯網和機器學習的進步正在提高營運可視性。這些因素共同推動了對人工智慧驅動的車隊智慧的強勁需求。

分散式車隊數據的整合

企業在整合遠端資訊處理、服務記錄和駕駛員行為分析平台的資訊方面面臨諸多挑戰。與擁有先進IT基礎設施的大型競爭對手相比，小規模企業在實施整合解決方案方面舉步維艱。監管要求通常強制要求與舊有系統相容，這阻礙了創新。由於數據孤島阻礙了全面分析，車隊管理人員效率低。政府必須在現代化和維持營運連續性之間取得平衡。這種碎片化現象持續阻礙車隊智慧平台的廣泛應用。

車隊性能預測分析

預測分析為前瞻性車隊管理開啟了新的可能性。人工智慧平台能夠預測車輛性能、維修需求和油耗模式。企業可以從中受益，減少停機時間並提高資產利用率。各國政府正在推廣預測技術，將永續性和安全措施的一部分。車隊營運商可以獲得可操作的洞察，從而延長車輛使用壽命。機器學習的進步進一步提高了效能預測的準確性。預計這一機遇將改變全球車隊管理的現狀。

艦隊網路面臨的網路安全威脅

網路安全風險對連網汽車網路構成重大挑戰。企業被迫在安全基礎設施上投入巨資，以保護敏感的營運資料和駕駛員資料。法律規範施加了嚴格的合規要求，導致成本增加。與擁有先進網路安全能力的大型競爭對手相比，中小企業尤其脆弱。如果沒有資料保護保障，駕駛可能不願意採用數位平台。資料外洩和濫用可能會削弱人們對人工智慧驅動的車輛管理解決方案的信任。除非加強安全措施，否則這些風險將持續構成威脅。

新型冠狀病毒（COVID-19）的影響：

疫情擾亂了車輛運營，客運需求下降，而對物流和配送服務的依賴卻日益增強。封鎖措施延緩了現代化改造項目，也減緩了軟體部署。另一方面，這場危機凸顯了數位智慧對於確保韌性的重要性。各國政府在其復甦計畫中強調了非接觸式監控和遠端車輛管理。企業重新聚焦於可擴展技術，以確保服務的連續性。在整個危機期間，駕駛者和營運商更加意識到預測分析的益處。總而言之，儘管新冠疫情帶來了短期挫折，但它進一步鞏固了對人工智慧驅動的車輛管理智慧的長期需求。

在預測期內，車隊分析平台細分市場預計將佔據最大的市場佔有率。

預計在預測期內，車隊分析平台細分市場將佔據最大的市場佔有率。這是因為這些解決方案能夠提供關於車輛性能、駕駛員行為和營運效率的全面洞察。企業正依靠分析來降低成本並提高服務可靠性。政府正在優先考慮將分析技術納入智慧運輸計畫。車隊營運商正受益於決策和資源分配的改進。基於雲端的分析技術的進步正在提升擴充性和易用性。與技術提供者的合作正在加速跨行業的應用。因此，車隊分析平台將繼續成為人工智慧驅動的車隊智慧的基礎。

在預測期內，公共交通車隊細分市場預計將呈現最高的複合年成長率。

在預測期內，由於對高效、永續的城市交通需求不斷成長，公共交通車隊預計將呈現最高的成長率。各公司正在部署人工智慧平台，以最佳化公車、地鐵和共享出行的營運。政府正大力支持公共交通現代化，將其作為智慧城市計畫的一部分。通勤者將受益於更可靠的服務和更短的旅行時間。預測性調度和即時監控技術的進步正在提升公共交通的營運效率。小規模業者也在城市特定應用領域找到了商機。

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，這得益於其強大的基礎設施以及對人工智慧驅動的車輛管理平台的早期應用。美國在物流和公共交通車隊中部署預測分析和遠端資訊處理解決方案方面發揮主導作用。各公司正大力投資先進演算法和雲端系統。與其他地區相比，車輛營運商對可靠且高效解決方案的需求更高。法律規範在支持創新的同時，也確保了合規性。各國政府正為大都會圈的智慧運輸先導計畫提供資金。

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，這主要得益於公共交通網路的擴張。中國、印度和日本等國家正在擴大人工智慧驅動的車輛管理項目，以滿足日益成長的出行需求。不斷壯大的中產階級推動了對高效且價格合理的服務的需求。各國政府正在實施扶持措施，以促進車輛管理技術的本土創新。當地企業正在擴大生產規模，以滿足國內外的需求。預測分析和公共交通最佳化技術的進步正在加速該地區的應用。這種充滿活力的環境使亞太地區成為成長最快的地區。

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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：全球人工智慧驅動的車隊智慧市場：按解決方案類型分類

  • 艦隊分析平台
  • 預測性維護解決方案
  • 駕駛行為分析
  • 路線最佳化和分析解決方案
  • 其他

第6章：全球人工智慧驅動的艦隊智慧市場：以人工智慧技術分類

  • 機器學習
  • 電腦視覺
  • 自然語言處理
  • 預測分析
  • 其他

第7章：全球人工智慧驅動的艦隊智慧市場：按部署方式分類

  • 基於雲端的
  • 現場

第8章：全球人工智慧驅動的車隊智慧市場：按車隊類型分類

  • 商用車輛車隊
  • 物流車隊
  • 大眾運輸車隊
  • 施工車隊
  • 其他

第9章：全球人工智慧驅動的車隊智慧市場：按最終用戶分類

  • 物流公司
  • 車隊管理服務供應商
  • 大眾運輸業者
  • 建設公司
  • 其他

第10章：全球人工智慧驅動的車隊智慧市場：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11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12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13章：公司簡介

  • Geotab Inc.
  • Samsara Inc.
  • Verizon Connect
  • Trimble Inc.
  • Motive Technologies, Inc.
  • Michelin Connected Fleet
  • Mix Telematics Limited
  • Omnitracs LLC
  • Fleet Complete
  • Powerfleet, Inc.
  • Zonar Systems, Inc.
  • Lytx, Inc.
  • ORBCOMM Inc.
  • IBM Corporation
  • Hitachi, Ltd.
Product Code: SMRC38812

According to Stratistics MRC, the Global AI-Based Fleet Intelligence Market is accounted for $7.4 billion in 2026 and is expected to reach $28.5 billion by 2034 growing at a CAGR of 18.4% during the forecast period. AI-based fleet intelligence refers to the application of artificial intelligence, machine learning, telematics, IoT sensors, and predictive analytics to monitor, analyze, and optimize the performance of commercial vehicle fleets. These solutions provide real-time insights into vehicle health, driver behavior, route optimization, fuel consumption, maintenance scheduling, safety, and operational efficiency. AI-based fleet intelligence enables predictive decision-making, reduces operating costs, improves asset utilization, and enhances fleet sustainability. Increasing adoption of connected vehicles, logistics automation, and data-driven transportation management is driving the global demand for AI-based fleet intelligence solutions.

Market Dynamics:

Driver:

Rising demand for operational optimization

Organizations are increasingly focused on optimizing fleet operations to reduce costs and improve efficiency. Digital intelligence platforms are being adopted to streamline route planning, fuel management, and predictive maintenance. Enterprises are investing in AI-driven solutions that provide real-time insights into vehicle performance. Governments are supporting modernization initiatives as part of smart mobility programs. Drivers and passengers benefit from safer and more reliable fleet services. Advances in telematics, IoT, and machine learning are enhancing operational visibility. Collectively, these factors are fueling strong demand for AI-based fleet intelligence.

Restraint:

Fragmented fleet data integration

Enterprises face difficulties in consolidating information from telematics, maintenance logs, and driver behavior platforms. Smaller operators struggle to implement unified solutions compared to larger competitors with advanced IT infrastructure. Regulatory requirements often mandate compatibility with legacy systems, slowing innovation. Fleet managers experience inefficiencies when data silos prevent holistic analysis. Governments must balance modernization with maintaining operational continuity. This fragmentation continues to restrain widespread adoption of fleet intelligence platforms.

Opportunity:

Predictive fleet performance analytics

Predictive analytics is opening new possibilities for proactive fleet management. AI-driven platforms can forecast vehicle performance, maintenance needs, and fuel consumption patterns. Enterprises benefit from reduced downtime and improved asset utilization. Governments are encouraging predictive technologies as part of sustainability and safety initiatives. Fleet operators gain access to actionable insights that extend vehicle lifespan. Advances in machine learning enhance the accuracy of performance forecasting. This opportunity is expected to transform fleet management practices worldwide.

Threat:

Cybersecurity threats to fleet networks

Cybersecurity risks pose a significant challenge to connected fleet networks. Enterprises must invest heavily in secure infrastructure to protect sensitive operational and driver data. Regulatory frameworks impose strict compliance requirements that increase costs. Smaller firms are particularly vulnerable compared to larger competitors with advanced cybersecurity capabilities. Drivers may hesitate to adopt digital platforms without assurances of data protection. Breaches or misuse of information could undermine trust in AI-driven fleet solutions. Unless security safeguards are strengthened, risks will remain a persistent threat.

Covid-19 Impact:

The pandemic disrupted fleet operations, reducing demand in passenger transport while increasing reliance on logistics and delivery services. Lockdowns delayed modernization projects and slowed down software deployments. At the same time, the crisis highlighted the importance of digital intelligence for resilience. Governments emphasized contactless monitoring and remote fleet management in recovery plans. Enterprises renewed focus on scalable technologies that ensure continuity of services. Drivers and operators became more aware of the benefits of predictive analytics during the crisis. Overall, Covid-19 created short-term setbacks but reinforced the long-term case for AI-based fleet intelligence.

The fleet analytics platforms segment is expected to be the largest during the forecast period

The fleet analytics platforms segment is expected to account for the largest market share during the forecast period as these solutions provide comprehensive insights into vehicle performance, driver behavior, and operational efficiency. Enterprises rely on analytics to reduce costs and improve service reliability. Governments are prioritizing analytics adoption as part of smart mobility programs. Fleet operators benefit from improved decision-making and resource allocation. Advances in cloud-based analytics enhance scalability and usability. Partnerships with technology providers are accelerating deployment across industries. Consequently, fleet analytics platforms remain the backbone of AI-based fleet intelligence.

The public transit fleets segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the public transit fleets segment is predicted to witness the highest growth rate due to rising demand for efficient and sustainable urban mobility. Enterprises are deploying AI-based platforms to optimize bus, metro, and shared mobility operations. Governments are supporting public transit modernization as part of smart city initiatives. Commuters benefit from more reliable services and reduced travel times. Advances in predictive scheduling and real-time monitoring enhance performance. Smaller operators find opportunities in niche urban applications.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to strong infrastructure and early adoption of AI-based fleet platforms. The U.S. leads in deploying predictive analytics and telematics solutions across logistics and transit fleets. Enterprises are investing heavily in advanced algorithms and cloud-based systems. Fleet operators demand reliable and efficient solutions at higher rates compared to other regions. Regulatory frameworks support innovation while ensuring compliance. Governments are funding pilot projects for smart mobility across metropolitan areas.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR driven by expanding public transit networks. Countries such as China, India, and Japan are scaling up AI-based fleet projects to meet rising mobility needs. Growing middle-class populations are fueling demand for efficient and affordable services. Governments are introducing supportive policies to encourage domestic innovation in fleet technologies. Local companies are expanding production to meet both domestic and export requirements. Advances in predictive analytics and public transit optimization accelerate adoption in this region. This dynamic environment positions Asia Pacific as the fastest-growing region.

Key players in the market

Some of the key players in AI-Based Fleet Intelligence Market include Geotab Inc., Samsara Inc., Verizon Connect, Trimble Inc., Motive Technologies, Inc., Michelin Connected Fleet, Mix Telematics Limited, Omnitracs LLC, Fleet Complete, Powerfleet, Inc., Zonar Systems, Inc., Lytx, Inc., ORBCOMM Inc., IBM Corporation and Hitachi, Ltd.

Key Developments:

In February 2026, Geotab Inc. introduced its next-generation GO and GO Plus telematics hardware built on an advanced AI processing architecture. The platform delivers real-time predictive video safety analytics, enhanced tamper protection, and satellite connectivity for complex commercial enterprise fleet operations.

In December 2025, Samsara Inc. launched its enhanced AI-driven Asset Management and Fleet Safety engine across its connected operations cloud. The platform features edge-computed computer vision models designed to predict collision risks, reduce idle time, and optimize real-time routing for global enterprise fleets.

Solution Types Covered:

  • Fleet Analytics Platforms
  • Predictive Maintenance Solutions
  • Driver Behavior Analytics
  • Route Intelligence Solutions
  • Other Solution Types

AI Technologies Covered:

  • Machine Learning
  • Computer Vision
  • Natural Language Processing
  • Predictive Analytics
  • Other AI Technologies

Deployments Covered:

  • Cloud-Based
  • On-Premise

Fleet Types Covered:

  • Commercial Vehicle Fleets
  • Logistics Fleets
  • Public Transit Fleets
  • Construction Fleets
  • Other Fleet Types

End Users Covered:

  • Logistics Companies
  • Fleet Management Service Providers
  • Public Transportation Operators
  • Construction Companies
  • Other End Users

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-Based Fleet Intelligence Market, By Solution Type

  • 5.1 Fleet Analytics Platforms
  • 5.2 Predictive Maintenance Solutions
  • 5.3 Driver Behavior Analytics
  • 5.4 Route Intelligence Solutions
  • 5.5 Other Solution Types

6 Global AI-Based Fleet Intelligence Market, By AI Technology

  • 6.1 Machine Learning
  • 6.2 Computer Vision
  • 6.3 Natural Language Processing
  • 6.4 Predictive Analytics
  • 6.5 Other AI Technologies

7 Global AI-Based Fleet Intelligence Market, By Deployment

  • 7.1 Cloud-Based
  • 7.2 On-Premise

8 Global AI-Based Fleet Intelligence Market, By Fleet Type

  • 8.1 Commercial Vehicle Fleets
  • 8.2 Logistics Fleets
  • 8.3 Public Transit Fleets
  • 8.4 Construction Fleets
  • 8.5 Other Fleet Types

9 Global AI-Based Fleet Intelligence Market, By End User

  • 9.1 Logistics Companies
  • 9.2 Fleet Management Service Providers
  • 9.3 Public Transportation Operators
  • 9.4 Construction Companies
  • 9.5 Other End Users

10 Global AI-Based Fleet Intelligence Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 Geotab Inc.
  • 13.2 Samsara Inc.
  • 13.3 Verizon Connect
  • 13.4 Trimble Inc.
  • 13.5 Motive Technologies, Inc.
  • 13.6 Michelin Connected Fleet
  • 13.7 Mix Telematics Limited
  • 13.8 Omnitracs LLC
  • 13.9 Fleet Complete
  • 13.10 Powerfleet, Inc.
  • 13.11 Zonar Systems, Inc.
  • 13.12 Lytx, Inc.
  • 13.13 ORBCOMM Inc.
  • 13.14 IBM Corporation
  • 13.15 Hitachi, Ltd.

List of Tables

  • Table 1 Global AI-Based Fleet Intelligence Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Fleet Intelligence Market, By Solution Type (2023-2034) ($MN)
  • Table 3 Global AI-Based Fleet Intelligence Market, By Fleet Analytics Platforms (2023-2034) ($MN)
  • Table 4 Global AI-Based Fleet Intelligence Market, By Predictive Maintenance Solutions (2023-2034) ($MN)
  • Table 5 Global AI-Based Fleet Intelligence Market, By Driver Behavior Analytics (2023-2034) ($MN)
  • Table 6 Global AI-Based Fleet Intelligence Market, By Route Intelligence Solutions (2023-2034) ($MN)
  • Table 7 Global AI-Based Fleet Intelligence Market, By Other Solution Types (2023-2034) ($MN)
  • Table 8 Global AI-Based Fleet Intelligence Market, By AI Technology (2023-2034) ($MN)
  • Table 9 Global AI-Based Fleet Intelligence Market, By Machine Learning (2023-2034) ($MN)
  • Table 10 Global AI-Based Fleet Intelligence Market, By Computer Vision (2023-2034) ($MN)
  • Table 11 Global AI-Based Fleet Intelligence Market, By Natural Language Processing (2023-2034) ($MN)
  • Table 12 Global AI-Based Fleet Intelligence Market, By Predictive Analytics (2023-2034) ($MN)
  • Table 13 Global AI-Based Fleet Intelligence Market, By Other AI Technologies (2023-2034) ($MN)
  • Table 14 Global AI-Based Fleet Intelligence Market, By Deployment (2023-2034) ($MN)
  • Table 15 Global AI-Based Fleet Intelligence Market, By Cloud-Based (2023-2034) ($MN)
  • Table 16 Global AI-Based Fleet Intelligence Market, By On-Premise (2023-2034) ($MN)
  • Table 17 Global AI-Based Fleet Intelligence Market, By Fleet Type (2023-2034) ($MN)
  • Table 18 Global AI-Based Fleet Intelligence Market, By Commercial Vehicle Fleets (2023-2034) ($MN)
  • Table 19 Global AI-Based Fleet Intelligence Market, By Logistics Fleets (2023-2034) ($MN)
  • Table 20 Global AI-Based Fleet Intelligence Market, By Public Transit Fleets (2023-2034) ($MN)
  • Table 21 Global AI-Based Fleet Intelligence Market, By Construction Fleets (2023-2034) ($MN)
  • Table 22 Global AI-Based Fleet Intelligence Market, By Other Fleet Types (2023-2034) ($MN)
  • Table 23 Global AI-Based Fleet Intelligence Market, By End User (2023-2034) ($MN)
  • Table 24 Global AI-Based Fleet Intelligence Market, By Logistics Companies (2023-2034) ($MN)
  • Table 25 Global AI-Based Fleet Intelligence Market, By Fleet Management Service Providers (2023-2034) ($MN)
  • Table 26 Global AI-Based Fleet Intelligence Market, By Public Transportation Operators (2023-2034) ($MN)
  • Table 27 Global AI-Based Fleet Intelligence Market, By Construction Companies (2023-2034) ($MN)
  • Table 28 Global AI-Based Fleet Intelligence Market, By Other End Users (2023-2034) ($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.