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市場調查報告書
商品編碼
2112973

互聯車隊分析市場預測至2034年－按分析類型、資料收集、部署模式、車隊細分、最終使用者和地區分類的全球分析

Connected Fleet Analytics Market Forecasts to 2034 - Global Analysis By Analytics Type, Data Acquisition, Deployment Model, Fleet Category, 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，全球互聯車隊分析市場預計將在 2026 年達到 79 億美元，並在預測期內以 17.5% 的複合年成長率成長，到 2034 年達到 286 億美元。

互聯車隊分析是指利用先進的分析技術，透過持續收集和分析遠端資訊處理和營運數據，來監控和最佳化連網車隊的性能。這些解決方案整合了物聯網感測器、GPS、人工智慧、雲端平台和機器學習技術，用於評估車輛使用情況、駕駛員行為、燃油和能源消耗、維護需求、路線效率和安全性能。互聯車隊分析能夠實現預測性決策，降低營運成本，提高車隊可靠性，並增強永續性。聯網汽車和數位化車隊管理的日益普及正在推動全球市場的成長。

對預測性洞察力的需求日益成長

車隊營運商越來越重視預測性洞察，以預測維護需求、最佳化路線並提高安全性。分析平台正被部署用於將原始遠端資訊處理資料轉化為可執行的洞察。企業正在投資人工智慧驅動的工具，以預測效能趨勢並減少停機時間。政府正在支持數位化車隊舉措，作為智慧運輸計畫的一部分。機器學習和雲端整合技術的進步正在提高擴充性。這些趨勢正在推動對互聯車隊分析的需求。

即時資料整合的局限性

跨不同系統的即時數據整合仍然是一項重大挑戰。營運商常常難以整合來自遠端資訊處理系統、感測器和傳統平台的數據。與大型競爭對手相比，中小企業 (SME) 實現無縫連接的成本往往更高。監管要求要求數據處理標準化，這減緩了系統的普及應用。數據碎片化阻礙了全面分析，導致車隊管理人員效率低落。客戶在獲取準確資訊方面可能會遇到延遲。這種整合不足的問題持續限制系統的部署速度。

人工智慧驅動的預測性車隊分析

人工智慧驅動的預測分析正在為主動式車隊管理開闢新的可能性。平台能夠更精準地預測車輛狀態、駕駛表現和燃油消耗。企業可以從中受益，降低維護成本並提高資產利用率。各國政府正在推廣預測技術，將其作為永續性和安全策略的一部分。營運商可以從中獲得洞察，從而延長車輛使用壽命並提高合規性。深度學習的進步正在提升預測模型的準確性。預計這一機遇將徹底改變全球車隊營運模式。

針對連網艦隊的網路攻擊

網路安全風險正日益成為互聯車隊面臨的嚴峻挑戰。企業必須投入大量資金建設安全基礎設施，以保護敏感的營運資料和駕駛員資料。監管機構不斷提高合規要求，導致成本上升。與擁有先進安全能力的大型競爭對手相比，中小企業尤其脆弱。如果沒有資料保護方面的保障，駕駛可能不願意採用數位化平台。資料外洩和濫用會削弱人們對分析解決方案的信心。除非採取強而有力的安全措施，否則網路攻擊將持續構成威脅。

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項目，並減緩了分析工具的普及應用。同時，這場危機凸顯了預測工具對提升韌性的重要性。各國政府在其復甦計畫中強調了數位轉型，並強化了互聯分析的角色。企業重新聚焦於可擴展平台，以確保服務的連續性。在整個危機期間，司機和營運商對預測分析的優勢有了更深刻的理解。總而言之，新冠疫情帶來了短期挫折，但也鞏固了車隊分析的長期必要性。

在預測期內，營運分析領域預計將佔據最大的市場佔有率。

預計在預測期內，營運分析領域將佔據最大的市場佔有率。這是因為這些解決方案能夠全面展現車隊性能、駕駛行為和資源利用。企業正依靠營運洞察來降低成本並提高服務可靠性。各國政府正將分析技術的應用作為智慧運輸計畫的優先事項。營運商正受益於更有效率的決策和更精簡的工作流程。雲端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。

預計在預測期內，公共產業細分市場將呈現最高的複合年成長率。

在預測期內，由於能源、通訊和基礎設施產業對高效服務車輛管理的需求不斷成長，公共產業車輛領域預計將呈現最高的成長率。各公司正在部署分析平台以最佳化調度並減少停機時間。政府正在支持公共產業事業車輛的現代化改造，將其作為基礎設施建設的一部分。客戶將受益於更可靠的服務和更低的營運成本。預測性維護和路線最佳化技術的進步正在提升車輛性能。中小企業正在該領域的細分應用中找到商機。

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和對互聯分析平台的早期應用。美國在物流、公共產業和公共交通車隊中引領預測分析技術的應用。各公司正大力投資先進的演算法和雲端系統。與其他地區相比，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，這主要得益於汽車營運的擴張。中國、印度和日本等國家正在擴大分析項目，以滿足日益成長的旅遊需求。不斷壯大的中產階級正在推動對高效且價格合理的出行服務的需求。各國政府正在實施扶持政策，以促進國內汽車技術的創新。當地企業正在擴大生產規模，以服務區域和全球市場。人工智慧驅動的預測分析技術的進步正在加速其在該地區的應用。

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  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
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    • 根據產品系列、企業發展和策略聯盟對重點公司進行基準分析。

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：全球互聯車隊分析市場：依分析類型分類

  • 營運分析
  • 預測性維護分析
  • 駕駛員行為分析
  • 燃油效率分析
  • 其他分析類型

第6章 全球互聯車隊分析市場：依資料蒐集方法分類

  • 車載資訊設備
  • 車載診斷系統（OBD）
  • 嵌入式車輛系統
  • 物聯網感測器
  • 其他數據採集方法

第7章 全球互聯車隊分析市場：依部署模式分類

  • 基於雲端的
  • 現場

第8章 全球互聯車隊分析市場：依車隊細分市場分類

  • 物流車輛
  • 大眾運輸工具
  • 施工車隊
  • 公共產業車輛
  • 其他車隊分類

第9章 全球互聯車隊分析市場：依最終用戶分類

  • 物流公司
  • 大眾運輸業者
  • 建設公司
  • 公共產業公司
  • 其他最終用戶

第10章：全球互聯車隊分析市場：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.
  • Mix Telematics
  • Powerfleet, Inc.
  • ORBCOMM Inc.
  • Zonar Systems, Inc.
  • Lytx, Inc.
  • Omnitracs LLC
  • Michelin Connected Fleet
  • IBM Corporation
  • Hitachi, Ltd.
  • Hexagon AB
Product Code: SMRC38990

According to Stratistics MRC, the Global Connected Fleet Analytics Market is accounted for $7.9 billion in 2026 and is expected to reach $28.6 billion by 2034 growing at a CAGR of 17.5% during the forecast period. Connected fleet analytics refers to the use of advanced analytics to monitor and optimize the performance of connected vehicle fleets through continuous collection and analysis of telematics and operational data. These solutions integrate IoT sensors, GPS, artificial intelligence, cloud platforms, and machine learning to evaluate vehicle utilization, driver behavior, fuel or energy consumption, maintenance requirements, route efficiency, and safety performance. Connected fleet analytics enables predictive decision-making, lowers operating costs, improves fleet reliability, and enhances sustainability. Increasing adoption of connected vehicles and digital fleet management is driving global market growth.

Market Dynamics:

Driver:

Rising demand for predictive insights

Fleet operators are increasingly seeking predictive insights to anticipate maintenance needs, optimize routes, and improve safety. Analytics platforms are being deployed to transform raw telematics into actionable intelligence. Enterprises are investing in AI-driven tools that forecast performance trends and reduce downtime. Governments are supporting digital fleet initiatives as part of smart mobility programs. Advances in machine learning and cloud integration are enhancing scalability. These developments are propelling demand for connected fleet analytics.

Restraint:

Limited real-time data integration

Real-time data integration across diverse systems remains a significant challenge. Operators often struggle to unify inputs from telematics, sensors, and legacy platforms. Smaller firms face higher costs in achieving seamless connectivity compared to larger competitors. Regulatory requirements demand standardized data handling, slowing deployment. Fleet managers encounter inefficiencies when fragmented data prevents holistic analysis. Customers may experience delays in accessing accurate insights. This lack of integration continues to limit adoption speed.

Opportunity:

AI-powered predictive fleet analytics

AI-powered predictive analytics is opening new possibilities for proactive fleet management. Platforms can forecast vehicle health, driver performance, and fuel consumption with greater accuracy. Enterprises benefit from reduced maintenance costs and improved asset utilization. Governments are encouraging predictive technologies as part of sustainability and safety strategies. Operators gain access to insights that extend vehicle lifespan and improve compliance. Advances in deep learning enhance the precision of forecasting models. This opportunity is expected to reshape fleet operations globally.

Threat:

Cyberattacks on connected fleets

Cybersecurity risks pose a growing challenge for connected fleets. Enterprises must invest heavily in secure infrastructure to protect sensitive operational and driver data. Regulators impose strict compliance requirements that increase costs. Smaller firms are particularly vulnerable compared to larger competitors with advanced security capabilities. Drivers may hesitate to adopt digital platforms without assurances of data protection. Breaches or misuse of information could undermine trust in analytics solutions. Unless robust safeguards are implemented, cyberattacks will remain a persistent threat.

Covid-19 Impact:

The pandemic disrupted fleet operations, reducing demand in passenger transport while boosting reliance on logistics and delivery services. Lockdowns delayed modernization projects and slowed down analytics deployments. At the same time, the crisis highlighted the importance of predictive tools for resilience. Governments emphasized digital transformation in recovery plans, reinforcing the role of connected analytics. Enterprises renewed focus on scalable platforms that ensure continuity of services. Drivers and operators became more aware of the benefits of predictive insights during the crisis. Overall, Covid-19 created short-term setbacks but strengthened the long-term case for fleet analytics.

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

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

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

Over the forecast period, the utility fleets segment is predicted to witness the highest growth rate due to rising demand for efficient management of service vehicles in energy, telecom, and infrastructure sectors. Enterprises are deploying analytics platforms to optimize scheduling and reduce downtime. Governments are supporting modernization of utility fleets as part of infrastructure development. Customers benefit from more reliable services and reduced operational costs. Advances in predictive maintenance and route optimization enhance performance. Smaller firms find opportunities in niche applications within this segment.

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 connected analytics platforms. The U.S. leads in deploying predictive insights across logistics, utilities, and transit fleets. Enterprises are investing heavily in advanced algorithms and cloud-based systems. 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 fleet operations. Countries such as China, India, and Japan are scaling up analytics 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 serve both regional and global markets. Advances in AI-powered predictive analytics accelerate adoption in this region.

Key players in the market

Some of the key players in Connected Fleet Analytics Market include Geotab Inc., Samsara Inc., Verizon Connect, Trimble Inc., Motive Technologies, Inc., Mix Telematics, Powerfleet, Inc., ORBCOMM Inc., Zonar Systems, Inc., Lytx, Inc., Omnitracs LLC, Michelin Connected Fleet, IBM Corporation, Hitachi, Ltd. and Hexagon AB.

Key Developments:

In June 2026, IBM Corporation integrated its watsonx generative AI capabilities into its Maximo for Transportation mobility management platform. The update allows municipal transport authorities to query real-time traffic sensor streams using natural language, automated anomaly detection, and predictive maintenance scheduling for urban transit infrastructure.

In March 2026, Hitachi, Ltd. deployed its Lumada Mobility Intelligence software suite in partnership with a major European logistics and transit corridor operator. The system processes real-time traffic density, EV charging availability, and transit demand patterns to balance electric fleet routing and minimize regional grid power consumption.

In February 2026, Trimble Inc. launched an updated high-precision GNSS positioning and spatial telematics engine built for driverless and connected commercial vehicle fleets. The technology delivers sub-meter lane guidance and continuous route optimization, allowing mobility operators to improve freight flow across dense interstate highway corridors.

Analytics Types Covered:

  • Operational Analytics
  • Predictive Maintenance Analytics
  • Driver Behavior Analytics
  • Fuel Efficiency Analytics
  • Other Analytics Types

Data Acquisition Methods Covered:

  • Telematics Devices
  • Onboard Diagnostics (OBD)
  • Embedded Vehicle Systems
  • IoT Sensors
  • Other Data Acquisition Methods

Deployment Models Covered:

  • Cloud-Based
  • On-Premise

Fleet Categories Covered:

  • Logistics Fleets
  • Public Transport Fleets
  • Construction Fleets
  • Utility Fleets
  • Other Fleet Categories

End Users Covered:

  • Logistics Companies
  • Public Transportation Operators
  • Construction Companies
  • Utility 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 Connected Fleet Analytics Market, By Analytics Type

  • 5.1 Operational Analytics
  • 5.2 Predictive Maintenance Analytics
  • 5.3 Driver Behavior Analytics
  • 5.4 Fuel Efficiency Analytics
  • 5.5 Other Analytics Types

6 Global Connected Fleet Analytics Market, By Data Acquisition

  • 6.1 Telematics Devices
  • 6.2 Onboard Diagnostics (OBD)
  • 6.3 Embedded Vehicle Systems
  • 6.4 IoT Sensors
  • 6.5 Other Data Acquisition Methods

7 Global Connected Fleet Analytics Market, By Deployment Model

  • 7.1 Cloud-Based
  • 7.2 On-Premise

8 Global Connected Fleet Analytics Market, By Fleet Category

  • 8.1 Logistics Fleets
  • 8.2 Public Transport Fleets
  • 8.3 Construction Fleets
  • 8.4 Utility Fleets
  • 8.5 Other Fleet Categories

9 Global Connected Fleet Analytics Market, By End User

  • 9.1 Logistics Companies
  • 9.2 Public Transportation Operators
  • 9.3 Construction Companies
  • 9.4 Utility Companies
  • 9.5 Other End Users

10 Global Connected Fleet Analytics 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 Mix Telematics
  • 13.7 Powerfleet, Inc.
  • 13.8 ORBCOMM Inc.
  • 13.9 Zonar Systems, Inc.
  • 13.10 Lytx, Inc.
  • 13.11 Omnitracs LLC
  • 13.12 Michelin Connected Fleet
  • 13.13 IBM Corporation
  • 13.14 Hitachi, Ltd.
  • 13.15 Hexagon AB

List of Tables

  • Table 1 Global Connected Fleet Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Connected Fleet Analytics Market, By Analytics Type (2023-2034) ($MN)
  • Table 3 Global Connected Fleet Analytics Market, By Operational Analytics (2023-2034) ($MN)
  • Table 4 Global Connected Fleet Analytics Market, By Predictive Maintenance Analytics (2023-2034) ($MN)
  • Table 5 Global Connected Fleet Analytics Market, By Driver Behavior Analytics (2023-2034) ($MN)
  • Table 6 Global Connected Fleet Analytics Market, By Fuel Efficiency Analytics (2023-2034) ($MN)
  • Table 7 Global Connected Fleet Analytics Market, By Other Analytics Types (2023-2034) ($MN)
  • Table 8 Global Connected Fleet Analytics Market, By Data Acquisition (2023-2034) ($MN)
  • Table 9 Global Connected Fleet Analytics Market, By Telematics Devices (2023-2034) ($MN)
  • Table 10 Global Connected Fleet Analytics Market, By Onboard Diagnostics (OBD) (2023-2034) ($MN)
  • Table 11 Global Connected Fleet Analytics Market, By Embedded Vehicle Systems (2023-2034) ($MN)
  • Table 12 Global Connected Fleet Analytics Market, By IoT Sensors (2023-2034) ($MN)
  • Table 13 Global Connected Fleet Analytics Market, By Other Data Acquisition Methods (2023-2034) ($MN)
  • Table 14 Global Connected Fleet Analytics Market, By Deployment Model (2023-2034) ($MN)
  • Table 15 Global Connected Fleet Analytics Market, By Cloud-Based (2023-2034) ($MN)
  • Table 16 Global Connected Fleet Analytics Market, By On-Premise (2023-2034) ($MN)
  • Table 17 Global Connected Fleet Analytics Market, By Fleet Category (2023-2034) ($MN)
  • Table 18 Global Connected Fleet Analytics Market, By Logistics Fleets (2023-2034) ($MN)
  • Table 19 Global Connected Fleet Analytics Market, By Public Transport Fleets (2023-2034) ($MN)
  • Table 20 Global Connected Fleet Analytics Market, By Construction Fleets (2023-2034) ($MN)
  • Table 21 Global Connected Fleet Analytics Market, By Utility Fleets (2023-2034) ($MN)
  • Table 22 Global Connected Fleet Analytics Market, By Other Fleet Categories (2023-2034) ($MN)
  • Table 23 Global Connected Fleet Analytics Market, By End User (2023-2034) ($MN)
  • Table 24 Global Connected Fleet Analytics Market, By Logistics Companies (2023-2034) ($MN)
  • Table 25 Global Connected Fleet Analytics Market, By Public Transportation Operators (2023-2034) ($MN)
  • Table 26 Global Connected Fleet Analytics Market, By Construction Companies (2023-2034) ($MN)
  • Table 27 Global Connected Fleet Analytics Market, By Utility Companies (2023-2034) ($MN)
  • Table 28 Global Connected Fleet Analytics Market, By Other End Users (2023-2034) ($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.