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2129257

人工智慧驅動的貨運和物流路線規劃市場預測（2034 年）—全球路線規劃方法、路線規劃輸入資料、最佳化目標、車輛配置、最終用戶和區域分析

AI-Powered Freight Routing Market Forecasts to 2034 - Global Analysis By Routing Method, Routing Input, Optimization Objective, Fleet Configuration, 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

全球人工智慧驅動的貨運和物流路線規劃市場預計將在 2026 年達到 27 億美元，並在預測期內以 20.2% 的複合年成長率成長，到 2034 年達到 118 億美元。

人工智慧驅動的貨運路線規劃是指利用人工智慧 (AI) 和先進的分析技術，根據交通狀況、配送時間表、車輛負載容量、天氣、路況、燃油消耗和營運限制等因素，確定最佳貨運路線。機器學習演算法持續評估運輸數據並調整路線，以提高配送效率。這些系統有助於物流業者減少運輸時間、燃油成本、空轉里程和配送延誤，同時提高車輛運轉率。對高效貨運、即時決策和成本最佳化的日益成長的需求，正在推動全球範圍內由人工智慧驅動的貨運路線最佳化技術的應用。

路線最佳化的需求日益成長

人工智慧驅動的貨運路線規劃解決方案透過分析運輸數據，識別高效率的貨運路線。這些解決方案在路線規劃中會考慮交通狀況、配送時間表、車輛負載容量和路況等因素。更最佳化的路線規劃能夠減少不必要的行程，並提高車輛運轉率。即時調整功能還能幫助企業快速應對突發的運輸中斷。物流公司正擴大採用自動化路線規劃來提高配送可靠性並控制營運成本。這些因素正在推動人工智慧驅動的貨運路線規劃解決方案的進一步普及。

不準確的即時交通訊息

準確的交通資訊對於可靠的基於人工智慧的路線規劃至關重要。來自道路、車輛、地圖系統和公共資訊來源的資訊不一致會導致資料缺失。更新延遲也會降低即時路線調整的有效性。低品質的交通資訊會影響預計的行程時間和配送計劃。因此，物流業者可能需要額外的資料來源來提高路線規劃的準確性。這些挑戰可能會削弱人們對自動化貨運路線規劃系統的信心。

動態多模態路徑最佳化

動態多模態路線最佳化為人工智慧驅動的貨運路線規劃平台創造了新的機會。這些系統可以評估公路、鐵路、航空和海運的各種組合。人工智慧可以比較各種可用運輸方式的運輸時間、運能、成本和營運狀態。如果某種運輸方式出現中斷，它還可以調整建議路線。多模態最佳化使企業能夠選擇更有效率的運輸資源組合。與貨物追蹤系統的整合可以實現整個運輸過程中的持續更新。

交通運輸數據的快速變化

交通狀況會因交通中斷、天氣現象、道路封閉和貨運需求變化等因素而迅速改變。快速變化的交通數據會導致路線提案很快過時。因此，人工智慧系統需要持續存取可靠及時的資訊。頻繁的數據變化也會增加即時路線規劃平台的計算負荷。不準確的更新會導致不必要的路線變更和營運延誤。物流業者可能需要整合多個資料來源才能提供可靠的提案。

新型冠狀病毒（COVID-19）的影響：

新冠疫情透過邊境限制、交通模式改變、人手不足和供應鏈中斷等方式擾亂了貨運。隨著運輸狀況的快速變化，物流業者難以維持既定路線。這種混亂局面促使人們對能夠根據即時狀況調整路線的技術產生了濃厚的興趣。數位化路線規劃工具可協助企業應對不斷變化的交付需求和運輸限制。疫情也凸顯了在不確定環境下靈活物流規劃的重要性。隨著貨運的復甦，企業繼續投資於能夠提高路線效率和韌性的技術。

在預測期內，動態路線規劃細分市場預計將佔據最大的市場佔有率。

隨著物流業者對基於不斷變化的運輸條件的靈活路線規劃的需求日益成長，動態路線規劃領域預計將在預測期內佔據最大的市場佔有率。這些系統能夠在交通狀況、配送優先順序或路況發生變化時更新路線。即時資訊使物流公司能夠更快地應對突發狀況。動態規劃還可以提高車輛利用率並減少不必要的里程。與車輛管理系統和貨物追蹤系統的整合增強了路線的可視性。為了應對不斷提高的配送期望，企業正在尋求提高運輸規劃的速度和準確性。

預計在預測期內，「天氣狀況」細分市場將呈現最高的複合年成長率。

在預測期內，由於貨運路線規劃中擴大採用即時環境信息，天氣狀況細分市場預計將呈現最高的成長率。天氣資料有助於識別可能影響道路安全、行駛時間和交貨時間表的狀況。人工智慧系統可以將預測和當前天氣資訊整合到路線提案中。物流業者可以利用這些資訊來避開高風險路線或調整交貨時間。與交通和車輛數據的整合能夠提供更全面的運輸狀況視圖。對主動中斷管理日益成長的需求正在提升基於天氣的路線最佳化資訊的價值。

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，這主要得益於車輛管理、物流軟體和先進交通技術的積極應用。美國擁有大規模的貨運網路，對高效率路線規劃的需求非常強勁。物流業者正擴大利用人工智慧和分析工具來提高車輛生產力和配送效率。先進的數位基礎設施正在促進交通、地圖、天氣和車輛數據的整合。此外，電子商務的蓬勃發展也對物流業者的及時交付提出了更高的要求。這些因素共同推動了全部區域對人工智慧驅動的貨運路線規劃的持續投資。

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，這主要得益於物流行業的快速數字化轉型。中國和印度正在加大對交通運輸技術的投資，以提高其龐大而複雜的貨運網路的效率。都市區日益成長的貨物運輸量推動了智慧路線規劃的需求。物流公司正在採用能夠整合交通、天氣和車輛資訊的雲端平台。智慧交通基礎設施的建置進一步提升了數位旅遊數據的可用性。隨著企業尋求自動化方法來管理其複雜的運輸運營，人工智慧的應用也不斷增加。

免費客製化服務：

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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
    • 對主要公司進行SWOT分析（最多3家公司）
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    • 根據產品系列、地理覆蓋範圍和策略聯盟對領先公司進行基準分析。

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：全球人工智慧驅動的貨運和物流路線規劃市場：依路線設定方法分類

  • 靜態路線規劃
  • 動態路線規劃
  • 持續路徑最佳化
  • 多站點路線最佳化
  • 多階段路徑最佳化
  • 其他路徑確定方法

第6章：全球人工智慧驅動的貨運和物流路線規劃市場：依路線輸入分類

  • 交通狀況
  • 貨物特性
  • 車輛限制
  • 送貨時限
  • 天氣狀況
  • 路由輸入

第7章：全球人工智慧驅動的貨運和物流路線規劃市場：依最佳化目標分類

  • 最大限度縮短所需時間
  • 最小化距離
  • 最大限度地減少燃料消耗
  • 最大限度地運轉率車輛
  • 提高交付可靠性
  • 其他最佳化目標

第8章：全球人工智慧驅動的貨運和物流路線規劃市場：按車輛配置分類

  • 單車車隊
  • 由多輛車組成的一組車輛
  • 多基地機隊
  • 混合式車輛車隊
  • 自主與半自動自主車隊
  • 其他車輛配置

第9章：全球人工智慧驅動的貨運和物流路線規劃市場：按最終用戶分類

  • 貨運代理公司
  • 第三方物流供應商
  • 貨運代理
  • 托運人
  • 物流運營商
  • 其他最終用戶

第10章：全球人工智慧驅動的貨運和物流路線規劃市場：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

  • Descartes Systems Group Inc.
  • Trimble Inc.
  • PTV Group
  • ORTEC
  • OptimoRoute Inc.
  • Route4Me, Inc.
  • Verizon Communications Inc.
  • Samsara Inc.
  • Geotab Inc.
  • Manhattan Associates, Inc.
  • Blue Yonder Group, Inc.
  • Kinaxis Inc.
  • E2open Parent Holdings, Inc.
  • Oracle Corporation
  • SAP SE
Product Code: SMRC39324

According to Stratistics MRC, the Global AI-Powered Freight Routing Market is accounted for $2.70 billion in 2026 and is expected to reach $11.80 billion by 2034 growing at a CAGR of 20.2% during the forecast period. AI-powered freight routing refers to the use of artificial intelligence and advanced analytics to determine optimal routes for freight transportation based on factors such as traffic, delivery schedules, vehicle capacity, weather, road conditions, fuel consumption, and operational constraints. Machine learning algorithms continuously evaluate transportation data to adjust routes and improve delivery efficiency. These systems help logistics operators reduce travel time, fuel costs, empty miles, and delivery delays while improving fleet utilization. Growing demand for efficient freight operations, real-time decision-making, and cost optimization is driving global adoption of AI-powered freight routing.

Market Dynamics:

Driver:

Rising demand for route optimization

AI-powered freight routing solutions analyze transportation data to identify efficient routes for freight movement. These solutions can consider traffic, delivery schedules, vehicle capacity, and road conditions when planning routes. Better route planning can help reduce unnecessary travel and improve fleet utilization. Real-time adjustments can also support faster responses to unexpected transportation disruptions. Logistics companies are increasingly using automated routing to improve delivery reliability and control operating costs. These factors are supporting wider adoption of AI-powered freight routing solutions.

Restraint:

Inaccurate real-time traffic data

Accurate traffic information is important for reliable AI-based route planning. Data gaps may occur when information from roads, vehicles, mapping systems, and public sources is inconsistent. Delayed updates can also reduce the effectiveness of real-time route adjustments. Poor-quality traffic information may affect estimated travel times and delivery schedules. Logistics providers may therefore need additional data sources to improve routing accuracy. These challenges can limit confidence in automated freight routing systems.

Opportunity:

Dynamic multimodal route optimization

Dynamic multimodal route optimization is creating new opportunities for AI-powered freight routing platforms. These systems can evaluate different combinations of road, rail, air, and maritime transportation. AI can compare travel time, capacity, cost, and operational conditions across available transport options. Route recommendations can also be adjusted when disruptions affect a particular transportation mode. Multimodal optimization can help companies select more efficient combinations of transport resources. Integration with shipment tracking systems can provide continuous updates throughout the journey.

Threat:

Rapid transportation data changes

Transportation conditions can change quickly because of traffic disruptions, weather events, road closures, and changes in freight demand. Rapid transportation data changes can make routing recommendations outdated within a short period. AI systems therefore need continuous access to reliable and timely information. Frequent data changes can also increase the computational requirements of real-time routing platforms. Incorrect updates may result in unnecessary route changes or operational delays. Logistics providers may need to combine multiple data sources to maintain reliable recommendations.

Covid-19 Impact:

The COVID-19 pandemic disrupted freight transportation through border restrictions, changing traffic patterns, labor shortages, and supply chain interruptions. Logistics providers faced difficulty maintaining planned routes as transportation conditions changed rapidly. These disruptions increased interest in technologies capable of adjusting routes based on real-time conditions. Digital routing tools helped companies respond to changing delivery requirements and transportation constraints. The pandemic also highlighted the importance of flexible logistics planning during periods of uncertainty. As freight activity recovered, businesses continued investing in technologies that could improve route efficiency and resilience.

The dynamic route planning segment is expected to be the largest during the forecast period

The dynamic route planning segment is expected to account for the largest market share during the forecast period as logistics providers increasingly require flexible routing based on changing transportation conditions. These systems can update routes when traffic, delivery priorities, or road conditions change. Real-time information allows logistics companies to respond more quickly to unexpected disruptions. Dynamic planning can also improve vehicle utilization and reduce unnecessary travel. Integration with fleet management and shipment tracking systems strengthens routing visibility. Growing delivery expectations are encouraging companies to improve the speed and accuracy of transportation planning.

The weather conditions segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the weather conditions segment is predicted to witness the highest growth rate due to increasing use of real-time environmental information in freight routing decisions. Weather data can help identify conditions that may affect road safety, travel time, and delivery schedules. AI systems can incorporate forecasts and current weather information into route recommendations. Logistics providers can use these insights to avoid high-risk routes or adjust delivery timing. Integration with traffic and vehicle data can provide a more complete view of transportation conditions. Growing demand for proactive disruption management is increasing the value of weather-based routing intelligence.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share owing to strong adoption of fleet management, logistics software, and intelligent transportation technologies. The United States has a large freight transportation network that creates substantial demand for efficient route planning. Logistics providers are increasingly using AI and analytics to improve fleet productivity and delivery performance. Advanced digital infrastructure supports the integration of traffic, mapping, weather, and vehicle data. The growth of e-commerce is also increasing pressure on logistics operators to provide timely deliveries. These factors are supporting continued investment in AI-powered freight routing across the region.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR driven by rapid logistics digitalization. China and India are investing in transportation technologies to improve the efficiency of large and complex freight networks. Growing urban delivery volumes are increasing the need for intelligent route planning. Logistics companies are adopting cloud-based platforms that can integrate traffic, weather, and vehicle information. Development of smart transportation infrastructure is further improving the availability of digital mobility data. AI adoption is also increasing as businesses seek automated approaches to manage transportation complexity.

Key players in the market

Some of the key players in AI-Powered Freight Routing Market include Descartes Systems Group Inc., Trimble Inc., PTV Group, ORTEC, OptimoRoute Inc., Route4Me, Inc., Verizon Communications Inc., Samsara Inc., Geotab Inc., Manhattan Associates, Inc., Blue Yonder Group, Inc., Kinaxis Inc., E2open Parent Holdings, Inc., Oracle Corporation, SAP SE.

Key Developments:

In April 2026, The Descartes Systems Group Inc. introduced the Fleet Data Intelligence platform on its Global Logistics Network, featuring the AI agent Rene and advanced machine learning algorithms. The platform automates route planning, predicts precise service times, and improves route density by up to 30% for high-volume freight operations.

In January 2026, ORTEC expanded its cloud-native logistics and route optimization suite, introducing machine-learning models for dynamic load building and real-time dispatch planning. The platform optimizes multi-stop freight routes based on continuous driver availability, dock constraints, and customer time-window updates.

Routing Methods Covered:

  • Static Route Planning
  • Dynamic Route Planning
  • Continuous Route Optimization
  • Multi-Stop Route Optimization
  • Multi-Tier Route Optimization
  • Other Routing Methods

Routing Inputs Covered:

  • Traffic Conditions
  • Shipment Characteristics
  • Vehicle Constraints
  • Delivery Time Windows
  • Weather Conditions
  • Other Routing Inputs

Optimization Objectives Covered:

  • Travel Time Minimization
  • Distance Minimization
  • Fuel Consumption Minimization
  • Vehicle Utilization Maximization
  • Delivery Reliability Improvement
  • Other Optimization Objectives

Fleet Configurations Covered:

  • Single-Vehicle Fleets
  • Multi-Vehicle Fleets
  • Multi-Depot Fleets
  • Mixed Vehicle Fleets
  • Autonomous & Semi-Autonomous Fleets
  • Other Fleet Configurations

End Users Covered:

  • Freight Carriers
  • Third-Party Logistics Providers
  • Freight Forwarders
  • Shippers
  • Distribution Operators
  • 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-Powered Freight Routing Market, By Routing Method

  • 5.1 Static Route Planning
  • 5.2 Dynamic Route Planning
  • 5.3 Continuous Route Optimization
  • 5.4 Multi-Stop Route Optimization
  • 5.5 Multi-Tier Route Optimization
  • 5.6 Other Routing Methods

6 Global AI-Powered Freight Routing Market, By Routing Input

  • 6.1 Traffic Conditions
  • 6.2 Shipment Characteristics
  • 6.3 Vehicle Constraints
  • 6.4 Delivery Time Windows
  • 6.5 Weather Conditions
  • 6.6 Other Routing Inputs

7 Global AI-Powered Freight Routing Market, By Optimization Objective

  • 7.1 Travel Time Minimization
  • 7.2 Distance Minimization
  • 7.3 Fuel Consumption Minimization
  • 7.4 Vehicle Utilization Maximization
  • 7.5 Delivery Reliability Improvement
  • 7.6 Other Optimization Objectives

8 Global AI-Powered Freight Routing Market, By Fleet Configuration

  • 8.1 Single-Vehicle Fleets
  • 8.2 Multi-Vehicle Fleets
  • 8.3 Multi-Depot Fleets
  • 8.4 Mixed Vehicle Fleets
  • 8.5 Autonomous & Semi-Autonomous Fleets
  • 8.6 Other Fleet Configurations

9 Global AI-Powered Freight Routing Market, By End User

  • 9.1 Freight Carriers
  • 9.2 Third-Party Logistics Providers
  • 9.3 Freight Forwarders
  • 9.4 Shippers
  • 9.5 Distribution Operators
  • 9.6 Other End Users

10 Global AI-Powered Freight Routing 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 Descartes Systems Group Inc.
  • 13.2 Trimble Inc.
  • 13.3 PTV Group
  • 13.4 ORTEC
  • 13.5 OptimoRoute Inc.
  • 13.6 Route4Me, Inc.
  • 13.7 Verizon Communications Inc.
  • 13.8 Samsara Inc.
  • 13.9 Geotab Inc.
  • 13.10 Manhattan Associates, Inc.
  • 13.11 Blue Yonder Group, Inc.
  • 13.12 Kinaxis Inc.
  • 13.13 E2open Parent Holdings, Inc.
  • 13.14 Oracle Corporation
  • 13.15 SAP SE

List of Tables

  • Table 1 Global AI-Powered Freight Routing Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Powered Freight Routing Market, By Routing Method (2023-2034) ($MN)
  • Table 3 Global AI-Powered Freight Routing Market, By Static Route Planning (2023-2034) ($MN)
  • Table 4 Global AI-Powered Freight Routing Market, By Dynamic Route Planning (2023-2034) ($MN)
  • Table 5 Global AI-Powered Freight Routing Market, By Continuous Route Optimization (2023-2034) ($MN)
  • Table 6 Global AI-Powered Freight Routing Market, By Multi-Stop Route Optimization (2023-2034) ($MN)
  • Table 7 Global AI-Powered Freight Routing Market, By Multi-Tier Route Optimization (2023-2034) ($MN)
  • Table 8 Global AI-Powered Freight Routing Market, By Other Routing Methods (2023-2034) ($MN)
  • Table 9 Global AI-Powered Freight Routing Market, By Routing Input (2023-2034) ($MN)
  • Table 10 Global AI-Powered Freight Routing Market, By Traffic Conditions (2023-2034) ($MN)
  • Table 11 Global AI-Powered Freight Routing Market, By Shipment Characteristics (2023-2034) ($MN)
  • Table 12 Global AI-Powered Freight Routing Market, By Vehicle Constraints (2023-2034) ($MN)
  • Table 13 Global AI-Powered Freight Routing Market, By Delivery Time Windows (2023-2034) ($MN)
  • Table 14 Global AI-Powered Freight Routing Market, By Weather Conditions (2023-2034) ($MN)
  • Table 15 Global AI-Powered Freight Routing Market, By Other Routing Inputs (2023-2034) ($MN)
  • Table 16 Global AI-Powered Freight Routing Market, By Optimization Objective (2023-2034) ($MN)
  • Table 17 Global AI-Powered Freight Routing Market, By Travel Time Minimization (2023-2034) ($MN)
  • Table 18 Global AI-Powered Freight Routing Market, By Distance Minimization (2023-2034) ($MN)
  • Table 19 Global AI-Powered Freight Routing Market, By Fuel Consumption Minimization (2023-2034) ($MN)
  • Table 20 Global AI-Powered Freight Routing Market, By Vehicle Utilization Maximization (2023-2034) ($MN)
  • Table 21 Global AI-Powered Freight Routing Market, By Delivery Reliability Improvement (2023-2034) ($MN)
  • Table 22 Global AI-Powered Freight Routing Market, By Other Optimization Objectives (2023-2034) ($MN)
  • Table 23 Global AI-Powered Freight Routing Market, By Fleet Configuration (2023-2034) ($MN)
  • Table 24 Global AI-Powered Freight Routing Market, By Single-Vehicle Fleets (2023-2034) ($MN)
  • Table 25 Global AI-Powered Freight Routing Market, By Multi-Vehicle Fleets (2023-2034) ($MN)
  • Table 26 Global AI-Powered Freight Routing Market, By Multi-Depot Fleets (2023-2034) ($MN)
  • Table 27 Global AI-Powered Freight Routing Market, By Mixed Vehicle Fleets (2023-2034) ($MN)
  • Table 28 Global AI-Powered Freight Routing Market, By Autonomous & Semi-Autonomous Fleets (2023-2034) ($MN)
  • Table 29 Global AI-Powered Freight Routing Market, By Other Fleet Configurations (2023-2034) ($MN)
  • Table 30 Global AI-Powered Freight Routing Market, By End User (2023-2034) ($MN)
  • Table 31 Global AI-Powered Freight Routing Market, By Freight Carriers (2023-2034) ($MN)
  • Table 32 Global AI-Powered Freight Routing Market, By Third-Party Logistics Providers (2023-2034) ($MN)
  • Table 33 Global AI-Powered Freight Routing Market, By Freight Forwarders (2023-2034) ($MN)
  • Table 34 Global AI-Powered Freight Routing Market, By Shippers (2023-2034) ($MN)
  • Table 35 Global AI-Powered Freight Routing Market, By Distribution Operators (2023-2034) ($MN)
  • Table 36 Global AI-Powered Freight Routing Market, By Other End Users (2023-2034) ($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.