查看購物車
  • English
  • Japanese
  • Korean
封面
市場調查報告書
商品編碼
1979967

城市交通數據分析和預測路徑規劃市場預測：至 2034 年——按組件、應用、最終用戶和區域分類的全球分析

Urban Mobility Data Analytics and Predictive Routing Market Forecasts to 2034 - Global Analysis By Component (Software Platforms, Services and Hardware & IoT Devices), 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 的研究，全球城市交通數據分析和預測路線市場預計將在 2026 年達到 30.1 億美元，在預測期內以 18.1% 的複合年成長率成長，到 2034 年達到 114 億美元。

城市交通數據分析和預測路線規劃利用收集到的交通數據來改善交通管理和提升通勤便利性。透過評估從導航系統、移動平台、路側感測器和聯網汽車收集的資訊，智慧模型可以檢測交通趨勢並預測路況。預測路線規劃技術會持續調整提案路線，並考慮壅塞程度、事故、氣候因素和需求變化。這些系統有助於最佳化公共交通、改善共用旅遊服務並推動智慧城市建設。總體而言，它們有助於減少燃料消耗、最大限度地減少環境影響、縮短旅行時間並制定更合理的城市交通規劃。

根據《未來交通雜誌》（2025 年）的報道，與傳統的統計方法相比，應用於交通流量預測的深度學習模型已將預測誤差率降低了高達 23%，這凸顯了提高智慧城市預測路線效率的潛力。

都市化進程和交通堵塞加劇。

城市人口的快速成長導致交通流量增加，交通網路不堪負荷。私家車使用量的增加和基礎設施的不足加劇了都市區擁擠。城市出行分析和預測路線平台透過評估交通行為和預測擁塞尖峰時段點來應對這些挑戰。這些系統利用來自導航設備、聯網汽車和路邊感測器的即時數據，推薦最佳化的出行路線，並支援主動式交通管理。隨著城市不斷擴張，政府部門和旅遊服務提供者越來越依賴智慧分析工具來提高出行效率、縮短通勤時間並提升整體交通效率，加速市場成長。

高昂的實施和基礎設施成本

建構預測性路線規劃和出行分析平台需要對硬體、網路系統和先進的分析工具進行大量資金投入。將現代數位解決方案整合到現有交通基礎設施中通常需要進行複雜的維修。許多城市都面臨挑戰，尤其是在新興經濟體，此類計劃的資金有限。持續的維護、技術升級和專業培訓進一步增加了長期營運成本。這些成本壓力會阻礙快速普及，並延遲投資決策。因此，巨額資金需求仍是限制市場廣泛滲透的主要阻礙因素。

交通行動服務(MaaS) 平台的擴展

出行即服務 (MaaS) 框架的廣泛應用為預測性出行系統帶來了廣闊的前景。 MaaS 將公車、火車、共用汽車和微出行等多種出行方式整合到一個統一的數位平台中，高度依賴數據驅動的協調。預測性路線規劃工具能夠評估出行行為、最佳化路線組合併支援動態定價策略。這些功能使用戶能夠體驗到更順暢、更方便的出行。隨著城市負責人大力推動綜合交通解決方案，出行業者也尋求先進的分析技術來協調服務並最佳化產品。 MaaS 環境的持續發展將顯著提升出行分析市場的機會。

依賴可靠的數據質量

預測性出行技術需要持續且準確的資訊才能實現精準的路線最佳化。資料缺失、資訊過時或技術故障都可能導致預測不準確。不準確的建議會對使用者體驗和系統可靠性產生負面影響。感測器故障和通訊網路不穩定會進一步降低效能可靠性。由於這些平台高度依賴不間斷的資料流，資訊品質的下降會削弱系統的有效性。因此，維護可靠的資料來源對市場的永續性構成了嚴重威脅。

新冠疫情的影響：

新冠疫情的爆發為都市區出行行為帶來了顯著變化，進而影響了出行分析和預測路徑規劃領域。出行限制、遠距辦公的興起以及通勤人數的減少導致交通堵塞和公共交通客流量大幅下降，暫時抑制了對路徑規劃解決方案的需求。儘管出行需求有所放緩，但疫情凸顯了數據驅動型洞察在交通管理中的價值。各國政府利用旅遊分析追蹤人口流動，並調整交通運作以滿足新的安全要求。在疫情復原階段，預測路徑規劃對於應對不可預測的出行模式至關重要，它能夠建立更安全、更柔軟性、更以技術主導的城市出行系統。

在預測期內，軟體平台細分市場預計將佔據最大的市場佔有率。

軟體平台預計將在預測期內佔據最大的市場佔有率，因為它是分析、預測和智慧路線規劃的核心引擎。透過整合來自導航工具、互聯基礎設施和旅行服務的輸入，這些系統能夠將複雜的資料集轉換為可執行的營運策略。雲端運算、機器學習演算法和即時分析技術提升了系統的柔軟性和效能。公共部門和旅遊服務供應商正大力採用軟體解決方案，以實現無縫升級、系統整合和可擴展部署。隨著數位轉型在交通運輸領域的重要性日益凸顯，軟體平台仍然是最具影響力和應用最廣泛的領域。

在預測期內，多模態行程規劃細分市場預計將呈現最高的複合年成長率。

在預測期內，多模態出行規劃領域預計將呈現最高的成長率，這主要得益於人們對互聯交通途徑日益成長的需求。都市區出行者擴大將公車、火車、共用汽車和微出行服務組合在同一條出行路線中。先進的分析系統能夠實現時刻表、路線組合和收費系統的即時同步。出行即服務 (MaaS) 平台的擴展以及以永續性為重點的城市策略進一步強化了這一趨勢。在數位化融合和環境優先事項不斷推進的背景下，綜合多模態規劃解決方案正在加速普及，並在市場上展現出強勁的成長動能。

市佔率最大的地區：

在整個預測期內，北美預計將保持最大的市場佔有率，這得益於其高度發展的技術生態系統和對智慧交通系統的積極應用。高連接性、智慧型裝置的廣泛使用以及強大的數位網路正在推動預測性路線規劃解決方案的普及。公共部門推廣智慧城市和環境永續交通的措施進一步加速了市場需求。對人工智慧驅動的分析、雲端平台和物聯網基礎設施的持續投資正在提高系統的效率和擴充性。科技公司與交通管理部門之間的緊密合作實現了全面部署，使該地區成為整體市場佔有率的主要驅動力。

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，這主要得益於城市化進程的加速和對數位基礎設施投資的不斷擴大。都市區人口的快速成長和日益成長的交通需求正推動政府部門採用智慧運輸技術。區域各國政府正積極推廣智慧交通系統、互聯出行網路及綜合交通管理平台。高行動普及率和數位轉型措施正在加速基於分析的路線規劃解決方案的普及。在持續的現代化努力和政策支持下，亞太地區正持續崛起，成為最具活力且成長最快的區域市場。

免費客製化服務：

購買此報告的客戶可以選擇以下免費自訂選項之一：

  • 企業概況
    • 對其他市場參與者（最多 3 家公司）進行全面分析
    • 主要參與者（最多3家公司）的SWOT分析
  • 區域細分
    • 主要國家的市場估算和預測，以及根據客戶需求量身定做的複合年成長率（註：需要進行可行性測試）。
  • 競爭性標竿分析
    • 根據主要參與者的產品系列、地理覆蓋範圍和策略聯盟進行基準分析。

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
  • 主要企業市佔率分析
  • 產品基準評效和效能比較

第5章 全球城市交通數據分析與預測路徑規劃市場：按組件分類

  • 軟體平台
  • 服務
  • 硬體和物聯網設備

第6章 全球城市交通數據分析與預測路徑市場：按應用領域分類

  • 交通流量最佳化
  • 車隊管理與最佳化
  • 公共運輸營運規劃與路線設置
  • 個人車輛的預測導航
  • 智慧停車管理
  • 多模態計劃

第7章 全球城市交通資料分析與預測路徑市場：依最終使用者分類

  • 政府/市政當局
  • 私人出行業者
  • 物流和配送公司
  • 公共運輸
  • 城市基礎設施提供者

第8章：全球城市交通數據分析與預測路徑市場：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9章 戰略市場資訊

  • 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10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11章：公司簡介

  • Targa Telematics
  • INRIX
  • PTV Group
  • HERE Technologies
  • UrbanLogiq
  • Siemens Mobility
  • DataTerminal
  • SmartTraffic Solutions
  • MobilityInsight
  • TomTom
  • Moovit
  • Transit
  • FarEye
  • Ualabee
  • Transport Foundry
  • Via Transportation
  • Bentley Systems
  • Iteris
Product Code: SMRC34130

According to Stratistics MRC, the Global Urban Mobility Data Analytics and Predictive Routing Market is accounted for $3.01 billion in 2026 and is expected to reach $11.40 billion by 2034 growing at a CAGR of 18.1% during the forecast period. Urban mobility data analytics and predictive routing utilize collected transport data to enhance traffic management and commuter convenience. Through evaluation of information gathered from navigation systems, mobile platforms, roadside sensors, and connected cars, intelligent models detect movement trends and anticipate roadway conditions. Predictive routing technologies continuously adjust suggested paths by considering congestion levels, incidents, climate factors, and shifting demand. These systems assist in optimizing public transportation, improving shared mobility services, and advancing smart city development. Overall, they contribute to reduced fuel consumption, minimized environmental impact, shorter travel durations, and more informed urban transport planning.

According to Future Transportation Journal (2025), deep learning models applied to traffic flow prediction reduced forecasting error rates by up to 23% compared to traditional statistical methods, highlighting the efficiency gains possible in predictive routing for smart cities.

Market Dynamics:

Driver:

Growing urbanization and traffic congestion

The rapid expansion of urban populations is leading to heavier traffic and strained transportation networks. Increased private vehicle use and insufficient infrastructure intensify congestion in cities. Urban mobility analytics and predictive routing platforms address these issues by evaluating traffic behavior and predicting peak pressure points. Using live data from navigation devices, connected cars, and roadside sensors, these systems recommend optimized travel routes and support proactive traffic management. As cities expand further, authorities and mobility providers increasingly rely on intelligent analytics tools to streamline movement, minimize commute times, and improve overall transport efficiency, accelerating market growth.

Restraint:

High implementation and infrastructure costs

Establishing predictive routing and mobility analytics platforms involves considerable capital expenditure on hardware, networking systems, and advanced analytical tools. Integrating modern digital solutions with existing transportation infrastructure often demands complex technical modifications. Many cities, particularly in emerging economies, struggle with limited funding to support such projects. Ongoing maintenance, technology upgrades, and specialized workforce training add to long-term operational expenses. These cost pressures can discourage rapid implementation and slow investment decisions. Therefore, substantial financial requirements continue to act as a key limitation for widespread market penetration.

Opportunity:

Expansion of mobility-as-a-service (MaaS) platforms

The increasing adoption of MaaS frameworks offers promising prospects for predictive mobility systems. By combining buses, trains, shared vehicles, and micro-mobility options into single digital platforms, MaaS depends heavily on data-driven coordination. Predictive routing tools evaluate travel behavior, optimize route combinations, and support dynamic pricing strategies. These capabilities enable smoother, more convenient travel experiences for users. As urban planners promote integrated transportation solutions, mobility operators seek advanced analytics to synchronize services and tailor offerings. The continued development of MaaS environments significantly strengthens opportunities within the mobility analytics market.

Threat:

Dependence on reliable data quality

Predictive mobility technologies require consistent and precise information to deliver accurate route optimization. Gaps in data collection, outdated information, or technical malfunctions may result in flawed forecasting outcomes. Inaccurate recommendations can negatively affect user experience and confidence in the system. Malfunctioning sensors or unstable communication networks further compromise performance reliability. Since these platforms depend extensively on uninterrupted data flows, any decline in information quality can weaken system effectiveness. Therefore, challenges related to maintaining dependable data sources pose a serious threat to market sustainability.

Covid-19 Impact:

The outbreak of COVID-19 caused major shifts in urban travel behavior, influencing the mobility analytics and predictive routing sector. Movement restrictions, widespread adoption of remote working, and reduced commuting significantly lowered traffic congestion and transit ridership, temporarily limiting demand for routing solutions. Despite this slowdown, the pandemic highlighted the value of data-driven insights in transportation management. Governments utilized mobility analytics to track population movement and adapt transit operations to new safety requirements. During recovery phases, predictive routing became essential for handling unpredictable travel patterns, enabling safer, more flexible, and technology-driven urban mobility systems.

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

The software platforms segment is expected to account for the largest market share during the forecast period as they serve as the core engine for analysis, forecasting, and intelligent routing. By consolidating inputs from navigation tools, connected infrastructure, and mobility services, these systems convert complex datasets into practical operational strategies. Cloud computing, machine learning algorithms, and real-time analytics enhance flexibility and performance. Public agencies and mobility providers focus heavily on software solutions because they allow seamless upgrades, system integration, and scalable deployment. With growing emphasis on digital transformation in transportation, software platforms continue to represent the most influential and widely adopted segment.

The multimodal journey planning segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the multimodal journey planning segment is predicted to witness the highest growth rate, supported by the rising preference for interconnected transport options. Urban travelers are increasingly blending buses, trains, shared vehicles, and micro-mobility services within single journeys. Advanced analytics systems facilitate real-time synchronization of schedules, route combinations, and pricing structures. Expansion of Mobility-as-a-Service platforms and sustainability-focused urban strategies further strengthen this trend. As digital integration and environmental priorities advance, comprehensive multimodal planning solutions are witnessing accelerated adoption and significant market momentum.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, supported by its well-developed technological ecosystem and proactive implementation of intelligent transport systems. High levels of connectivity, extensive use of smart devices, and robust digital networks contribute to widespread adoption of predictive routing solutions. Public sector initiatives promoting smart cities and environmentally sustainable transportation further accelerate demand. Continuous investments in AI-driven analytics, cloud platforms, and IoT-enabled infrastructure improve system efficiency and scalability. Strong partnerships between technology firms and transportation authority's enable comprehensive deployment, positioning the region as the leading contributor to overall market share.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR due to accelerating urban development and expanding digital infrastructure investments. Rapid population growth in cities and increasing transportation demand are prompting authorities to implement smart mobility technologies. Regional governments are actively promoting intelligent traffic systems, connected mobility networks, and integrated transit management platforms. High mobile penetration and digital transformation initiatives enhance the adoption of analytics-based routing solutions. With ongoing modernization efforts and policy support, Asia-Pacific continues to emerge as the most dynamic and rapidly growing regional market.

Key players in the market

Some of the key players in Urban Mobility Data Analytics and Predictive Routing Market include Targa Telematics, INRIX, PTV Group, HERE Technologies, UrbanLogiq, Siemens Mobility, DataTerminal, SmartTraffic Solutions, MobilityInsight, TomTom, Moovit, Transit, FarEye, Ualabee, Transport Foundry, Via Transportation, Bentley Systems and Iteris.

Key Developments:

In February 2026, INRIX announced an expanded partnership with the Texas Department of Transportation (TxDOT) to deliver advanced traffic and safety insights statewide. Building on a 15-year collaboration, this new contract will help make travel across Texas safer, smarter, and more efficient for all road users.

In February 2026, Siemens Mobility and Stadler has officially confirmed the framework agreement signed with DSB for the delivery of 226 fully automated electric multiple units for the S-Bane suburban network in Copenhagen. The project is valued at approximately EUR 3 billion and will create the world's largest open rail system with automatic train operation.

In October 2025, TomTom announced the expansion of its partnership with Hyundai AutoEver (HAE), the mobility software provider of the Hyundai Motor Group (HMG), further enhancing the driving experience for millions of HMG vehicles across Europe. This renewed agreement solidifies TomTom's position as a maps supplier for HAE, integrating TomTom's live services, including real-time traffic data and the newly awarded speed camera service, into Hyundai AutoEver's navigation software to support all Hyundai Motor, Kia, and Genesis models in Europe over the next several years.

Components Covered:

  • Software Platforms
  • Services
  • Hardware & IoT Devices

Applications Covered:

  • Traffic Flow Optimization
  • Fleet Management & Optimization
  • Public Transit Scheduling & Routing
  • Private Vehicle Predictive Navigation
  • Smart Parking Management
  • Multimodal Journey Planning

End Users Covered:

  • Government & Municipal Authorities
  • Private Mobility Operators
  • Logistics & Delivery Companies
  • Public Transport Agencies
  • Urban Infrastructure Providers

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 Urban Mobility Data Analytics and Predictive Routing Market, By Component

  • 5.1 Software Platforms
  • 5.2 Services
  • 5.3 Hardware & IoT Devices

6 Global Urban Mobility Data Analytics and Predictive Routing Market, By Application

  • 6.1 Traffic Flow Optimization
  • 6.2 Fleet Management & Optimization
  • 6.3 Public Transit Scheduling & Routing
  • 6.4 Private Vehicle Predictive Navigation
  • 6.5 Smart Parking Management
  • 6.6 Multimodal Journey Planning

7 Global Urban Mobility Data Analytics and Predictive Routing Market, By End User

  • 7.1 Government & Municipal Authorities
  • 7.2 Private Mobility Operators
  • 7.3 Logistics & Delivery Companies
  • 7.4 Public Transport Agencies
  • 7.5 Urban Infrastructure Providers

8 Global Urban Mobility Data Analytics and Predictive Routing Market, By Geography

  • 8.1 North America
    • 8.1.1 United States
    • 8.1.2 Canada
    • 8.1.3 Mexico
  • 8.2 Europe
    • 8.2.1 United Kingdom
    • 8.2.2 Germany
    • 8.2.3 France
    • 8.2.4 Italy
    • 8.2.5 Spain
    • 8.2.6 Netherlands
    • 8.2.7 Belgium
    • 8.2.8 Sweden
    • 8.2.9 Switzerland
    • 8.2.10 Poland
    • 8.2.11 Rest of Europe
  • 8.3 Asia Pacific
    • 8.3.1 China
    • 8.3.2 Japan
    • 8.3.3 India
    • 8.3.4 South Korea
    • 8.3.5 Australia
    • 8.3.6 Indonesia
    • 8.3.7 Thailand
    • 8.3.8 Malaysia
    • 8.3.9 Singapore
    • 8.3.10 Vietnam
    • 8.3.11 Rest of Asia Pacific
  • 8.4 South America
    • 8.4.1 Brazil
    • 8.4.2 Argentina
    • 8.4.3 Colombia
    • 8.4.4 Chile
    • 8.4.5 Peru
    • 8.4.6 Rest of South America
  • 8.5 Rest of the World (RoW)
    • 8.5.1 Middle East
      • 8.5.1.1 Saudi Arabia
      • 8.5.1.2 United Arab Emirates
      • 8.5.1.3 Qatar
      • 8.5.1.4 Israel
      • 8.5.1.5 Rest of Middle East
    • 8.5.2 Africa
      • 8.5.2.1 South Africa
      • 8.5.2.2 Egypt
      • 8.5.2.3 Morocco
      • 8.5.2.4 Rest of Africa

9 Strategic Market Intelligence

  • 9.1 Industry Value Network and Supply Chain Assessment
  • 9.2 White-Space and Opportunity Mapping
  • 9.3 Product Evolution and Market Life Cycle Analysis
  • 9.4 Channel, Distributor, and Go-to-Market Assessment

10 Industry Developments and Strategic Initiatives

  • 10.1 Mergers and Acquisitions
  • 10.2 Partnerships, Alliances, and Joint Ventures
  • 10.3 New Product Launches and Certifications
  • 10.4 Capacity Expansion and Investments
  • 10.5 Other Strategic Initiatives

11 Company Profiles

  • 11.1 Targa Telematics
  • 11.2 INRIX
  • 11.3 PTV Group
  • 11.4 HERE Technologies
  • 11.5 UrbanLogiq
  • 11.6 Siemens Mobility
  • 11.7 DataTerminal
  • 11.8 SmartTraffic Solutions
  • 11.9 MobilityInsight
  • 11.10 TomTom
  • 11.11 Moovit
  • 11.12 Transit
  • 11.13 FarEye
  • 11.14 Ualabee
  • 11.15 Transport Foundry
  • 11.16 Via Transportation
  • 11.17 Bentley Systems
  • 11.18 Iteris

List of Tables

  • Table 1 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Software Platforms (2023-2034) ($MN)
  • Table 4 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Hardware & IoT Devices (2023-2034) ($MN)
  • Table 6 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Application (2023-2034) ($MN)
  • Table 7 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Traffic Flow Optimization (2023-2034) ($MN)
  • Table 8 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Fleet Management & Optimization (2023-2034) ($MN)
  • Table 9 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Public Transit Scheduling & Routing (2023-2034) ($MN)
  • Table 10 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Private Vehicle Predictive Navigation (2023-2034) ($MN)
  • Table 11 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Smart Parking Management (2023-2034) ($MN)
  • Table 12 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Multimodal Journey Planning (2023-2034) ($MN)
  • Table 13 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By End User (2023-2034) ($MN)
  • Table 14 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Government & Municipal Authorities (2023-2034) ($MN)
  • Table 15 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Private Mobility Operators (2023-2034) ($MN)
  • Table 16 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Logistics & Delivery Companies (2023-2034) ($MN)
  • Table 17 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Public Transport Agencies (2023-2034) ($MN)
  • Table 18 Global Urban Mobility Data Analytics and Predictive Routing Market Outlook, By Urban Infrastructure Providers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.