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市場調查報告書
商品編碼
2074958

人工智慧驅動的交通預測市場預測至2034年：全球分析（按組件、技術、部署模式、資料來源、應用、最終用戶和地區分類）

AI-Based Traffic Prediction Market Forecasts to 2034 - Global Analysis By Component (Software and Services), Technology, Deployment Mode, Data Source, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 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交通預測市場預計將在 2026 年達到 59 億美元，到 2034 年達到 194 億美元，在預測期內以 16.0% 的複合年成長率成長。

人工智慧驅動的交通預測應用機器學習、深度學習、神經網路和預測分析演算法，對整個路網的交通流量模式進行即時和長期建模、預測和最佳化。這些系統整合來自聯網汽車、交通攝影機、GPS導航服務、物聯網道路感測器和行動網路訊號的各種資料流，以產生高度精確的交通狀況預測、擁塞預警、事故預測和動態路線引導。

來自聯網汽車的資料流激增，提高了預測模型的準確性。

全球聯網汽車的快速普及正在產生前所未有的大量即時交通數據，顯著提升了基於人工智慧的預測模型的訓練資料集和運行性能。隨著配備車載資訊系統、GPS感測器和V2X通訊模組的車輛日益普及，交通預測演算法將能夠獲取整個路網中車輛速度、跟車距離、車道變換和煞車模式等詳細且高頻的數據。這些豐富的數據使人工智慧模型能夠在擁塞發生之前識別出細微的徵兆，並準確預測交通中斷，從而有充足的時間實施有效的交通管理預防措施。

數據品質的不均衡性和基礎設施的差異限制了預測的可靠性。

基於人工智慧的交通預測系統的準確性從根本上取決於輸入資料流的完整性、一致性和時間粒度，而這些因素會因地區和路網類型而異。在感測器基礎設施稀疏、聯網汽車普及率低或資料傳輸協定不一致的地區，預測模型的效能會顯著降低，從而削弱其為交通管理機構提供的營運價值。跨異質感測器網路、傳統交通管理系統和多個商業資料供應商的資料標準化仍然是一項技術挑戰，需要對資料管治框架和互通性標準進行大量投資。

與智慧型訊號控制系統整合，實現即時自適應管理。

人工智慧交通預測與自適應號誌控制技術的融合，打造出強大的運作組合，使交通管理中心能夠根據預測的需求而非預先設定的歷史模式動態調整號誌配時方案。這種整合將預測結果轉化為可執行的交通管理措施，顯著降低路口延誤，最佳化主要道路的交通流量，並優先保障緊急車輛通行。隨著市政當局越來越強制要求升級自適應訊號控制系統，對具備原生整合訊號控制功能的人工智慧預測平台的需求日益成長，從而催生出一個具有穩定且持續收入潛力的高價值市場細分領域。

演算法偏差和模型故障的情境可能會為供應商帶來法律責任風險

基於歷史資料訓練的人工智慧交通預測模型容易受到系統性偏差的影響，這可能導致對因異常情況、假日、新開發項目或出行行為轉變而快速變化的城市交通模式做出不準確的預測。如果模型在緊急疏散或大型活動疏導等關鍵交通管理場景中失效，則可能引發嚴重的交通堵塞危機，並損害技術供應商和實施機構的聲譽。人工智慧交通預測系統缺乏標準化的準確性基準和性能認證框架，進一步加劇了採購決策的複雜性，並導致合約責任糾紛。這使得一些機構在採用尖端預測技術方面猶豫不決。

新型冠狀病毒（COVID-19）的影響：

新冠疫情從根本上擾亂了全球交通模式，使得基於疫情前出行模式訓練的歷史資料集對預測模型而言幾乎毫無用處。人工智慧預測供應商被迫根據解封後出現的新出行行為快速重新訓練模型，從而加速了對能夠快速捕捉需求結構性變化的自適應機器學習架構的投資。矛盾的是，疫情也凸顯了人工智慧驅動的交通預測在管理動態出行方面的價值，因為在分階段經濟重啟和旅行限制不斷變化的情況下，交通管理部門高度依賴預測平台。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率。該領域包括智慧型運輸系統(ITS) 軟體套件，這些套件提供核心的分析和營運價值，例如交通預測平台、分析引擎、路線最佳化工具和人工智慧預測功能。隨著雲端原生部署架構降低對硬體的依賴並實現快速擴展，軟體解決方案目前已佔據解決方案總收入的大部分，而基於訂閱的授權模式則為供應商提供了可預測且持續的收入來源。

預計在預測期內，深度學習領域將呈現最高的複合年成長率。

在預測期內，深度學習領域預計將呈現最高的成長率，因為深度神經網路架構能夠出色地捕捉到簡單的機器學習模型無法充分錶示的複雜時空交通模式。基於變壓器的序列建模和圖神經網路的進步，使得深度學習系統能夠在大規模路網的長期預測精度方面取得突破性成果，吸引了領先的交通技術供應商投入大量研發資金並積極進行商業部署。

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。這得歸功於其廣泛的聯網汽車基礎設施、資金充足的州和聯邦交通管理項目，以及服務交通系統的成熟的人工智慧技術公司生態系統。該地區早期採用智慧型運輸系統（ITS）、強大的雲端運算基礎設施以及用於利用交通數據的先進法規結構，為在主要大都會圈和州際公路走廊持續部署先進的人工智慧預測平台創造了有利條件。

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。這反映了中國、日本、韓國和印度在智慧城市交通管理基礎設施方面的大規模公共投資。中國的國家智慧型運輸系統（ITS）舉措和聯網汽車數量的激增，為其提供了尤為強大的市場基礎。同時，在東南亞快速都市化的經濟體中，人工智慧交通預測解決方案的部署也開始作為廣泛的城市基礎設施現代化項目的一部分，這些項目由國內預算和國際發展基金共同資助。

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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：全球人工智慧交通預測市場：按組件分類

  • 軟體
    • 交通預測平台
    • 交通分析軟體
    • 路線最佳化軟體
    • 智慧型運輸系統（ITS）軟體
  • 服務
    • 諮詢服務
    • 整合和配置服務
    • 維護和支援服務

第6章：全球人工智慧交通預測市場：按技術分類

  • 機器學習（ML）
  • 深度學習
  • 神經網路
  • 電腦視覺
  • 自然語言處理（NLP）
  • 強化學習
  • 預測分析

第7章：全球人工智慧交通預測市場：依部署模式分類

  • 基於雲端的
  • 現場
  • 混合實現

第8章：全球人工智慧交通預測市場：依資料來源分類

  • 網路攝影機
  • GPS和導航數據
  • 聯網汽車數據
  • 行動網路數據
  • 物聯網感測器
  • 路側單元（RSU）
  • 社群媒體和事件數據
  • 天氣數據

第9章：全球人工智慧交通預測市場：按應用領域分類

  • 交通流量預測
  • 交通堵塞管理
  • 路線規劃與最佳化
  • 事件檢測與預測
  • 智慧訊號控制
  • 大眾運輸時刻表規劃
  • 車輛和物流管理
  • 緊急應變計劃

第10章：全球人工智慧交通預測市場：以最終用戶分類

  • 政府和交通部門
  • 智慧城市管理機構
  • 大眾運輸業者
  • 物流和車輛營運商
  • 行動服務供應商
  • 道路基礎設施營運商
  • 機場和港口

第11章：全球人工智慧交通預測市場：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12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13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14章：公司簡介

  • IBM Corporation
  • Siemens AG
  • Kapsch TrafficCom AG
  • Iteris, Inc.
  • TomTom NV
  • HERE Technologies
  • INRIX, Inc.
  • Cubic Corporation
  • PTV Group
  • Miovision Technologies Inc.
  • SWARCO AG
  • Huawei Technologies Co., Ltd.
  • Cisco Systems, Inc.
  • Hitachi, Ltd.
  • Fujitsu Limited
Product Code: SMRC37466

According to Stratistics MRC, the Global AI-Based Traffic Prediction Market is accounted for $5.9 billion in 2026 and is expected to reach $19.4 billion by 2034, growing at a CAGR of 16.0% during the forecast period. AI-Based Traffic Prediction encompasses the application of machine learning, deep learning, neural networks, and predictive analytics algorithms to model, forecast, and optimize traffic flow patterns across road networks in real time and over extended time horizons. These systems ingest heterogeneous data streams from connected vehicles, traffic cameras, GPS navigation services, IoT road sensors, and mobile network signals to generate highly accurate traffic condition forecasts, congestion alerts, incident predictions, and dynamic routing recommendations.

Market Dynamics:

Driver:

Proliferation of connected vehicle data streams enhancing prediction model accuracy

The rapid expansion of connected vehicle populations globally is generating unprecedented volumes of real-time traffic data that substantially enhance the training datasets and operational performance of AI-based prediction models. As vehicles equipped with onboard telematics, GPS sensors, and V2X communication modules become increasingly prevalent, traffic prediction algorithms can access granular, high-frequency data on vehicle speeds, headways, lane changes, and braking patterns across entire road networks. This data richness enables AI models to identify subtle pre-congestion indicators and accurately predict traffic disruptions with lead times sufficient for effective proactive traffic management interventions.

Restraint:

Data quality inconsistencies and infrastructure gaps limiting prediction reliability

The accuracy of AI-based traffic prediction systems is fundamentally contingent upon the completeness, consistency, and temporal granularity of input data streams, which vary significantly across different geographic markets and road network types. In regions with sparse sensor infrastructure, limited connected vehicle penetration, or inconsistent data transmission protocols, prediction model performance degrades materially, reducing the operational value delivered to traffic management agencies. Data standardization across heterogeneous sensor networks, legacy traffic management systems, and multiple commercial data providers remains a persistent technical challenge that requires significant investment in data governance frameworks and interoperability standards.

Opportunity:

Integration with smart traffic signal control systems enabling real-time adaptive management

The convergence of AI-based traffic prediction with adaptive signal control technology creates a powerful operational pairing that enables traffic management centers to dynamically adjust signal timing plans based on anticipated demand conditions rather than pre-programmed historical patterns. This integration transforms prediction outputs into actionable traffic management interventions that measurably reduce intersection delay, smooth arterial progression, and prioritize emergency vehicle passage. As municipal governments increasingly mandate adaptive signal control upgrades, demand for AI prediction platforms with native signal control integration is expanding, creating a high-value market segment with strong recurring revenue characteristics.

Threat:

Algorithmic bias and model failure scenarios creating liability exposure for vendors

AI traffic prediction models trained on historical data may exhibit systematic biases that produce inaccurate forecasts for atypical events, holiday periods, or rapidly evolving urban traffic patterns altered by new development or mobility behavior changes. Model failures during critical traffic management scenarios, such as emergency evacuations or major event dispersals, can result in severe congestion crises and reputational damage for technology vendors and deploying agencies. The lack of standardized accuracy benchmarks and performance certification frameworks for AI traffic prediction systems further complicates procurement decisions and creates contractual liability disputes that deter some agencies from adopting cutting-edge prediction technologies.

Covid-19 Impact:

COVID-19 fundamentally disrupted traffic patterns globally, rendering historical training datasets largely irrelevant for prediction models calibrated under pre-pandemic mobility assumptions. AI prediction vendors were compelled to rapidly retrain models on emergent post-lockdown traffic behaviors, accelerating investment in adaptive machine learning architectures that can quickly incorporate structural demand shifts. Paradoxically, the pandemic demonstrated the value of AI traffic prediction in managing dynamic mobility conditions, as traffic agencies relied heavily on prediction platforms during phased reopenings and fluctuating mobility restriction periods.

The Software segment is expected to be the largest during the forecast period

The Software segment is expected to account for the largest market share during the forecast period, encompassing traffic prediction platforms, analytics engines, route optimization tools, and intelligent transportation system software suites that deliver the core analytical and operational value of AI prediction capabilities. As cloud-native deployment architectures reduce hardware dependency and enable rapid scalability, software solutions increasingly account for the dominant proportion of total solution revenue, with subscription-based licensing models providing predictable recurring income streams for vendors.

The Deep Learning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the Deep Learning segment is predicted to witness the highest growth rate, driven by the superior performance of deep neural architectures in capturing complex spatiotemporal traffic patterns that simpler machine learning models cannot adequately represent. Advances in transformer-based sequence modeling and graph neural networks are enabling deep learning systems to achieve breakthrough prediction accuracy over long time horizons across large-scale road networks, attracting substantial research investment and commercial deployment commitments from leading traffic technology vendors.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by extensive connected vehicle infrastructure, well-funded state and federal traffic management programs, and a mature ecosystem of AI technology companies serving transportation agencies. The region's early adoption of intelligent transportation systems, strong cloud computing infrastructure, and progressive regulatory frameworks for traffic data utilization create favorable conditions for the sustained deployment of advanced AI prediction platforms across major metropolitan areas and interstate highway corridors.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, reflecting massive public investment in smart city traffic management infrastructure across China, Japan, South Korea, and India. China's national intelligent transportation initiatives and proliferating connected vehicle fleet provide particularly strong market foundations, while rapidly urbanizing economies in Southeast Asia are beginning to adopt AI traffic prediction solutions as part of broader urban infrastructure modernization programs funded through both domestic budgets and international development financing.

Key players in the market

Some of the key players in AI-Based Traffic Prediction Market include IBM Corporation, Siemens AG, Kapsch TrafficCom AG, Iteris, Inc., TomTom N.V., HERE Technologies, INRIX, Inc., Cubic Corporation, PTV Group, Miovision Technologies Inc., SWARCO AG, Huawei Technologies Co., Ltd., Cisco Systems, Inc., Hitachi, Ltd., Fujitsu Limited.

Key Developments:

In April 2026, IBM Corporation launched an enhanced AI traffic prediction module integrated with its Intelligent Operations Center platform, leveraging real-time connected vehicle data streams and generative AI forecasting models to deliver 92% prediction accuracy across congested urban corridors in pilot deployments.

In January 2026, HERE Technologies announced a strategic collaboration with a major automotive OEM to integrate its AI-powered predictive traffic data service into connected vehicle navigation systems, enabling proactive route adjustments based on predicted congestion events up to 60 minutes in advance.

Components Covered:

  • Software
  • Services

Technologies Covered:

  • Machine Learning (ML)
  • Deep Learning
  • Neural Networks
  • Computer Vision
  • Natural Language Processing (NLP)
  • Reinforcement Learning
  • Predictive Analytics

Deployment Modes Covered:

  • Cloud-Based
  • On-Premises
  • Hybrid Deployment

Data Sources Covered:

  • Traffic Cameras
  • GPS and Navigation Data
  • Connected Vehicle Data
  • Mobile Network Data
  • IoT Sensors
  • Roadside Units (RSUs)
  • Social Media and Event Data
  • Weather Data

Applications Covered:

  • Traffic Flow Forecasting
  • Congestion Management
  • Route Planning and Optimization
  • Incident Detection and Prediction
  • Smart Traffic Signal Control
  • Public Transport Scheduling
  • Fleet and Logistics Management
  • Emergency Response Planning

End Users Covered:

  • Government and Transportation Authorities
  • Smart City Administrations
  • Public Transit Operators
  • Logistics and Fleet Operators
  • Mobility Service Providers
  • Road Infrastructure Operators
  • Airports and Seaports

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 Traffic Prediction Market, By Component

  • 5.1 Software
    • 5.1.1 Traffic Prediction Platforms
    • 5.1.2 Traffic Analytics Software
    • 5.1.3 Route Optimization Software
    • 5.1.4 Intelligent Transportation System (ITS) Software
  • 5.2 Services
    • 5.2.1 Consulting Services
    • 5.2.2 Integration & Deployment Services
    • 5.2.3 Maintenance & Support Services

6 Global AI-Based Traffic Prediction Market, By Technology

  • 6.1 Machine Learning (ML)
  • 6.2 Deep Learning
  • 6.3 Neural Networks
  • 6.4 Computer Vision
  • 6.5 Natural Language Processing (NLP)
  • 6.6 Reinforcement Learning
  • 6.7 Predictive Analytics

7 Global AI-Based Traffic Prediction Market, By Deployment Mode

  • 7.1 Cloud-Based
  • 7.2 On-Premises
  • 7.3 Hybrid Deployment

8 Global AI-Based Traffic Prediction Market, By Data Source

  • 8.1 Traffic Cameras
  • 8.2 GPS and Navigation Data
  • 8.3 Connected Vehicle Data
  • 8.4 Mobile Network Data
  • 8.5 IoT Sensors
  • 8.6 Roadside Units (RSUs)
  • 8.7 Social Media and Event Data
  • 8.8 Weather Data

9 Global AI-Based Traffic Prediction Market, By Application

  • 9.1 Traffic Flow Forecasting
  • 9.2 Congestion Management
  • 9.3 Route Planning and Optimization
  • 9.4 Incident Detection and Prediction
  • 9.5 Smart Traffic Signal Control
  • 9.6 Public Transport Scheduling
  • 9.7 Fleet and Logistics Management
  • 9.8 Emergency Response Planning

10 Global AI-Based Traffic Prediction Market, By End User

  • 10.1 Government and Transportation Authorities
  • 10.2 Smart City Administrations
  • 10.3 Public Transit Operators
  • 10.4 Logistics and Fleet Operators
  • 10.5 Mobility Service Providers
  • 10.6 Road Infrastructure Operators
  • 10.7 Airports and Seaports

11 Global AI-Based Traffic Prediction Market, By Geography

  • 11.1 North America
    • 11.1.1 United States
    • 11.1.2 Canada
    • 11.1.3 Mexico
  • 11.2 Europe
    • 11.2.1 United Kingdom
    • 11.2.2 Germany
    • 11.2.3 France
    • 11.2.4 Italy
    • 11.2.5 Spain
    • 11.2.6 Netherlands
    • 11.2.7 Belgium
    • 11.2.8 Sweden
    • 11.2.9 Switzerland
    • 11.2.10 Poland
    • 11.2.11 Rest of Europe
  • 11.3 Asia Pacific
    • 11.3.1 China
    • 11.3.2 Japan
    • 11.3.3 India
    • 11.3.4 South Korea
    • 11.3.5 Australia
    • 11.3.6 Indonesia
    • 11.3.7 Thailand
    • 11.3.8 Malaysia
    • 11.3.9 Singapore
    • 11.3.10 Vietnam
    • 11.3.11 Rest of Asia Pacific
  • 11.4 South America
    • 11.4.1 Brazil
    • 11.4.2 Argentina
    • 11.4.3 Colombia
    • 11.4.4 Chile
    • 11.4.5 Peru
    • 11.4.6 Rest of South America
  • 11.5 Rest of the World (RoW)
    • 11.5.1 Middle East
      • 11.5.1.1 Saudi Arabia
      • 11.5.1.2 United Arab Emirates
      • 11.5.1.3 Qatar
      • 11.5.1.4 Israel
      • 11.5.1.5 Rest of Middle East
    • 11.5.2 Africa
      • 11.5.2.1 South Africa
      • 11.5.2.2 Egypt
      • 11.5.2.3 Morocco
      • 11.5.2.4 Rest of Africa

12 Strategic Market Intelligence

  • 12.1 Industry Value Network and Supply Chain Assessment
  • 12.2 White-Space and Opportunity Mapping
  • 12.3 Product Evolution and Market Life Cycle Analysis
  • 12.4 Channel, Distributor, and Go-to-Market Assessment

13 Industry Developments and Strategic Initiatives

  • 13.1 Mergers and Acquisitions
  • 13.2 Partnerships, Alliances, and Joint Ventures
  • 13.3 New Product Launches and Certifications
  • 13.4 Capacity Expansion and Investments
  • 13.5 Other Strategic Initiatives

14 Company Profiles

  • 14.1 IBM Corporation
  • 14.2 Siemens AG
  • 14.3 Kapsch TrafficCom AG
  • 14.4 Iteris, Inc.
  • 14.5 TomTom N.V.
  • 14.6 HERE Technologies
  • 14.7 INRIX, Inc.
  • 14.8 Cubic Corporation
  • 14.9 PTV Group
  • 14.10 Miovision Technologies Inc.
  • 14.11 SWARCO AG
  • 14.12 Huawei Technologies Co., Ltd.
  • 14.13 Cisco Systems, Inc.
  • 14.14 Hitachi, Ltd.
  • 14.15 Fujitsu Limited

List of Tables

  • Table 1 Global AI-Based Traffic Prediction Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global AI-Based Traffic Prediction Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global AI-Based Traffic Prediction Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global AI-Based Traffic Prediction Market Outlook, By Traffic Prediction Platforms (2023-2034) ($MN)
  • Table 5 Global AI-Based Traffic Prediction Market Outlook, By Traffic Analytics Software (2023-2034) ($MN)
  • Table 6 Global AI-Based Traffic Prediction Market Outlook, By Route Optimization Software (2023-2034) ($MN)
  • Table 7 Global AI-Based Traffic Prediction Market Outlook, By Intelligent Transportation System (ITS) Software (2023-2034) ($MN)
  • Table 8 Global AI-Based Traffic Prediction Market Outlook, By Services (2023-2034) ($MN)
  • Table 9 Global AI-Based Traffic Prediction Market Outlook, By Consulting Services (2023-2034) ($MN)
  • Table 10 Global AI-Based Traffic Prediction Market Outlook, By Integration & Deployment Services (2023-2034) ($MN)
  • Table 11 Global AI-Based Traffic Prediction Market Outlook, By Maintenance & Support Services (2023-2034) ($MN)
  • Table 12 Global AI-Based Traffic Prediction Market Outlook, By Technology (2023-2034) ($MN)
  • Table 13 Global AI-Based Traffic Prediction Market Outlook, By Machine Learning (ML) (2023-2034) ($MN)
  • Table 14 Global AI-Based Traffic Prediction Market Outlook, By Deep Learning (2023-2034) ($MN)
  • Table 15 Global AI-Based Traffic Prediction Market Outlook, By Neural Networks (2023-2034) ($MN)
  • Table 16 Global AI-Based Traffic Prediction Market Outlook, By Computer Vision (2023-2034) ($MN)
  • Table 17 Global AI-Based Traffic Prediction Market Outlook, By Natural Language Processing (NLP) (2023-2034) ($MN)
  • Table 18 Global AI-Based Traffic Prediction Market Outlook, By Reinforcement Learning (2023-2034) ($MN)
  • Table 19 Global AI-Based Traffic Prediction Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 20 Global AI-Based Traffic Prediction Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 21 Global AI-Based Traffic Prediction Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 22 Global AI-Based Traffic Prediction Market Outlook, By On-Premises (2023-2034) ($MN)
  • Table 23 Global AI-Based Traffic Prediction Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 24 Global AI-Based Traffic Prediction Market Outlook, By Data Source (2023-2034) ($MN)
  • Table 25 Global AI-Based Traffic Prediction Market Outlook, By Traffic Cameras (2023-2034) ($MN)
  • Table 26 Global AI-Based Traffic Prediction Market Outlook, By GPS and Navigation Data (2023-2034) ($MN)
  • Table 27 Global AI-Based Traffic Prediction Market Outlook, By Connected Vehicle Data (2023-2034) ($MN)
  • Table 28 Global AI-Based Traffic Prediction Market Outlook, By Mobile Network Data (2023-2034) ($MN)
  • Table 29 Global AI-Based Traffic Prediction Market Outlook, By IoT Sensors (2023-2034) ($MN)
  • Table 30 Global AI-Based Traffic Prediction Market Outlook, By Roadside Units (RSUs) (2023-2034) ($MN)
  • Table 31 Global AI-Based Traffic Prediction Market Outlook, By Social Media and Event Data (2023-2034) ($MN)
  • Table 32 Global AI-Based Traffic Prediction Market Outlook, By Weather Data (2023-2034) ($MN)
  • Table 33 Global AI-Based Traffic Prediction Market Outlook, By Application (2023-2034) ($MN)
  • Table 34 Global AI-Based Traffic Prediction Market Outlook, By Traffic Flow Forecasting (2023-2034) ($MN)
  • Table 35 Global AI-Based Traffic Prediction Market Outlook, By Congestion Management (2023-2034) ($MN)
  • Table 36 Global AI-Based Traffic Prediction Market Outlook, By Route Planning and Optimization (2023-2034) ($MN)
  • Table 37 Global AI-Based Traffic Prediction Market Outlook, By Incident Detection and Prediction (2023-2034) ($MN)
  • Table 38 Global AI-Based Traffic Prediction Market Outlook, By Smart Traffic Signal Control (2023-2034) ($MN)
  • Table 39 Global AI-Based Traffic Prediction Market Outlook, By Public Transport Scheduling (2023-2034) ($MN)
  • Table 40 Global AI-Based Traffic Prediction Market Outlook, By Fleet and Logistics Management (2023-2034) ($MN)
  • Table 41 Global AI-Based Traffic Prediction Market Outlook, By Emergency Response Planning (2023-2034) ($MN)
  • Table 42 Global AI-Based Traffic Prediction Market Outlook, By End User (2023-2034) ($MN)
  • Table 43 Global AI-Based Traffic Prediction Market Outlook, By Government and Transportation Authorities (2023-2034) ($MN)
  • Table 44 Global AI-Based Traffic Prediction Market Outlook, By Smart City Administrations (2023-2034) ($MN)
  • Table 45 Global AI-Based Traffic Prediction Market Outlook, By Public Transit Operators (2023-2034) ($MN)
  • Table 46 Global AI-Based Traffic Prediction Market Outlook, By Logistics and Fleet Operators (2023-2034) ($MN)
  • Table 47 Global AI-Based Traffic Prediction Market Outlook, By Mobility Service Providers (2023-2034) ($MN)
  • Table 48 Global AI-Based Traffic Prediction Market Outlook, By Road Infrastructure Operators (2023-2034) ($MN)
  • Table 49 Global AI-Based Traffic Prediction Market Outlook, By Airports and Seaports (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.