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市场调查报告书
商品编码
1979967

城市交通数据分析和预测路径规划市场预测：至 2034 年——按组件、应用、最终用户和区域分類的全球分析

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个工作天内

价格

根据 Stratistics MRC 的研究，全球城市交通数据分析和预测路线市场预计将在 2026 年达到 30.1 亿美元，在预测期内以 18.1% 的复合年增长率增长，到 2034 年达到 114 亿美元。

城市交通数据分析和预测路线规划利用收集到的交通数据来改善交通管理和提升通勤便利性。透过评估从导航系统、移动平台、路侧感测器和联网汽车收集的信息，智慧模型可以检测交通趋势并预测路况。预测路线规划技术会持续调整提案路线，并考虑壅塞程度、事故、气候因素和需求变化。这些系统有助于优化公共交通、改善共用旅游服务并推动智慧城市建设。总体而言，它们有助于减少燃料消耗、最大限度地减少环境影响、缩短旅行时间并制定更合理的城市交通规划。

根据《未来交通杂誌》（2025 年）的报道，与传统的统计方法相比，应用于交通流量预测的深度学习模型已将预测误差率降低了高达 23%，这凸显了提高智慧城市预测路线效率的潜力。

都市化进程和交通拥堵加剧。

城市人口的快速成长导致交通流量增加，交通网络不堪负荷。私家车使用量的增加和基础设施的不足加剧了都市区拥挤。城市出行分析和预测路线平台透过评估交通行为和预测拥塞尖峰时段点来应对这些挑战。这些系统利用来自导航设备、联网汽车和路边感测器的即时数据，推荐优化的出行路线，并支援主动式交通管理。随着城市不断扩张，政府部门和旅游服务提供者越来越依赖智慧分析工具来提高出行效率、缩短通勤时间并提升整体交通效率，加速市场成长。

高昂的实施和基础设施成本

建构预测性路线规划和出行分析平台需要对硬体、网路系统和先进的分析工具进行大量资金投入。将现代数位解决方案整合到现有交通基础设施中通常需要进行复杂的维修。许多城市都面临挑战，尤其是在新兴经济体，此类计划的资金有限。持续的维护、技术升级和专业培训进一步增加了长期营运成本。这些成本压力会阻碍快速普及，并延迟投资决策。因此，巨额资金需求仍是限制市场广泛渗透的主要阻碍因素。

交通行动服务(MaaS) 平台的扩展

出行即服务 (MaaS) 框架的广泛应用为预测性出行系统带来了广阔的前景。 MaaS 将公车、火车、共用汽车和微出行等多种出行方式整合到一个统一的数位平台中，高度依赖数据驱动的协调。预测性路线规划工具能够评估出行行为、优化路线组合併支援动态定价策略。这些功能使用户能够体验到更顺畅、更方便的出行。随着城市负责人大力推动综合交通解决方案，出行业者也寻求先进的分析技术来协调服务并优化产品。 MaaS 环境的持续发展将显着提升出行分析市场的机会。

依赖可靠的数据质量

预测性出行技术需要持续且准确的资讯才能实现精准的路线最佳化。资料缺失、资讯过时或技术故障都可能导致预测不准确。不准确的建议会对使用者体验和系统可靠性产生负面影响。感测器故障和通讯网路不稳定会进一步降低效能可靠性。由于这些平台高度依赖不间断的资料流，资讯品质的下降会削弱系统的有效性。因此，维护可靠的资料来源对市场的永续性构成了严重威胁。

新冠疫情的影响：

新冠疫情的爆发为都市区出行行为带来了显着变化，进而影响了出行分析和预测路径规划领域。出行限制、远距办公的兴起以及通勤人数的减少导致交通拥堵和公共交通客流量大幅下降，暂时抑制了对路径规划解决方案的需求。儘管出行需求有所放缓，但疫情凸显了数据驱动型洞察在交通管理中的价值。各国政府利用旅游分析追踪人口流动，并调整交通运作以满足新的安全要求。在疫情復原阶段，预测路径规划对于应对不可预测的出行模式至关重要，它能够建立更安全、更柔软性、更以技术主导的城市出行系统。

在预测期内，软体平台细分市场预计将占据最大的市场份额。

软体平台预计将在预测期内占据最大的市场份额，因为它是分析、预测和智慧路线规划的核心引擎。透过整合来自导航工具、互联基础设施和旅行服务的输入，这些系统能够将复杂的资料集转换为可执行的营运策略。云端运算、机器学习演算法和即时分析技术提升了系统的柔软性和效能。公共部门和旅游服务供应商正大力采用软体解决方案，以实现无缝升级、系统整合和可扩展部署。随着数位转型在交通运输领域的重要性日益凸显，软体平台仍然是最具影响力和应用最广泛的领域。

在预测期内，多模态行程规划细分市场预计将呈现最高的复合年增长率。

在预测期内，多模态出行规划领域预计将呈现最高的成长率，这主要得益于人们对互联交通途径日益增长的需求。都市区出行者越来越多地将公车、火车、共用汽车和微出行服务组合在同一条出行路线中。先进的分析系统能够实现时刻表、路线组合和收费系统的即时同步。出行即服务 (MaaS) 平台的扩展以及以永续性为重点的城市策略进一步强化了这一趋势。在数位化融合和环境优先事项不断推进的背景下，综合多模态规划解决方案正在加速普及，并在市场上展现出强劲的成长动能。

市占率最大的地区：

在整个预测期内，北美预计将保持最大的市场份额，这得益于其高度发展的技术生态系统和对智慧交通系统的积极应用。高连接性、智慧型装置的广泛使用以及强大的数位网路正在推动预测性路线规划解决方案的普及。公共部门推广智慧城市和环境永续交通的措施进一步加速了市场需求。对人工智慧驱动的分析、云端平台和物联网基础设施的持续投资正在提高系统的效率和扩充性。科技公司与交通管理部门之间的紧密合作实现了全面部署，使该地区成为整体市场份额的主要驱动力。

复合年增长率最高的地区：

在预测期内，亚太地区预计将呈现最高的复合年增长率，这主要得益于城市化进程的加速和对数位基础设施投资的不断扩大。都市区人口的快速增长和日益增长的交通需求正推动政府部门采用智慧运输技术。区域各国政府正积极推广智慧交通系统、互联出行网路及综合交通管理平台。高行动普及率和数位转型措施正在加速基于分析的路线规划解决方案的普及。在持续的现代化努力和政策支持下，亚太地区正持续崛起，成为最具活力且成长最快的区域市场。

免费客製化服务：

购买此报告的客户可以选择以下免费自订选项之一：

  • 企业概况
    • 对其他市场参与者（最多 3 家公司）进行全面分析
    • 主要参与者（最多3家公司）的SWOT分析
  • 区域细分
    • 主要国家的市场估算和预测，以及根据客户需求量身定制的复合年增长率（註：需要进行可行性测试）。
  • 竞争性标竿分析
    • 根据主要参与者的产品系列、地理覆盖范围和策略联盟进行基准分析。

目录

第一章执行摘要

  • 市场概览及主要亮点
  • 驱动因素、挑战与机会
  • 竞争格局概述
  • 战略洞察与建议

第二章：研究框架

  • 研究目标和范围
  • 相关人员分析
  • 研究假设和限制
  • 调查方法

第三章 市场动态与趋势分析

  • 市场定义与结构
  • 主要市场驱动因素
  • 市场限制与挑战
  • 投资成长机会和重点领域
  • 产业威胁与风险评估
  • 技术与创新展望
  • 新兴市场/高成长市场
  • 监管和政策环境
  • 新冠疫情的影响及復苏前景

第四章：竞争环境与策略评估

  • 波特五力分析
    • 供应商的议价能力
    • 买方的议价能力
    • 替代品的威胁
    • 新进入者的威胁
    • 竞争公司之间的竞争
  • 主要企业市占率分析
  • 产品基准评效和效能比较

第五章 全球城市交通数据分析与预测路径规划市场：按组件划分

  • 软体平台
  • 服务
  • 硬体和物联网设备

第六章 全球城市交通数据分析与预测路径市场：按应用领域划分

  • 交通流量优化
  • 车队管理与优化
  • 公共运输营运规划与路线设置
  • 个人车辆的预测导航
  • 智慧停车管理
  • 多模态计划

第七章 全球城市交通资料分析与预测路径市场：依最终使用者划分

  • 政府/市政当局
  • 私人出行业者
  • 物流和配送公司
  • 公共运输
  • 城市基础设施提供者

第八章：全球城市交通数据分析与预测路径市场：按地区划分

  • 北美洲
    • 我们
    • 加拿大
    • 墨西哥
  • 欧洲
    • 英国
    • 德国
    • 法国
    • 义大利
    • 西班牙
    • 荷兰
    • 比利时
    • 瑞典
    • 瑞士
    • 波兰
    • 其他欧洲国家
  • 亚太地区
    • 中国
    • 日本
    • 印度
    • 韩国
    • 澳洲
    • 印尼
    • 泰国
    • 马来西亚
    • 新加坡
    • 越南
    • 其他亚太国家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥伦比亚
    • 智利
    • 秘鲁
    • 其他南美国家
  • 世界其他地区（RoW）
    • 中东
      • 沙乌地阿拉伯
      • 阿拉伯聯合大公国
      • 卡达
      • 以色列
      • 其他中东国家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲国家

第九章 战略市场资讯

  • 工业价值网络和供应链评估
  • 空白区域和机会地图
  • 产品演进与市场生命週期分析
  • 通路、经销商和打入市场策略的评估

第十章：产业趋势与策略倡议

  • 併购
  • 伙伴关係、联盟和合资企业
  • 新产品发布和认证
  • 扩大生产能力和投资
  • 其他策略倡议

第十一章：公司简介

  • 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.