Agentic AI in Transportation and Smart Mobility

REPORT HIGHLIGHTS

Global Agentic AI in Transportation and Smart Mobility Market to Reach US$21.6 Billion by 2032

The global market for Agentic AI in Transportation and Smart Mobility estimated at US$3.4 Billion in the year 2025, is expected to reach US$21.6 Billion by 2032, growing at a CAGR of 30.3% over the analysis period 2025-2032. Software Stack Offering, one of the segments analyzed in the report, is expected to record a 27.0% CAGR and reach US$7.2 Billion by the end of the analysis period. Growth in the AI Compute Hardware Offering segment is estimated at 34.6% CAGR over the analysis period.

The U.S. Market is Estimated at US$1.0 Billion While China is Forecast to Grow at 29.0% CAGR

The Agentic AI in Transportation and Smart Mobility market in the U.S. is estimated at US$1.0 Billion in the year 2025. China, the world`s second largest economy, is forecast to reach a projected market size of US$3.6 Billion by the year 2032 trailing a CAGR of 29.0% over the analysis period 2025-2032. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 27.3% and 26.6% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 21.4% CAGR.

Global Agentic Artificial Intelligence (AI) in Transportation and Smart Mobility Market - Key Trends & Drivers Summarized

How Is Agentic AI Reframing Intelligence Across Modern Transportation Systems?

Agentic Artificial Intelligence is redefining transportation and smart mobility by enabling autonomous systems that can reason, plan, and act across highly dynamic and interconnected mobility environments. Unlike traditional traffic management or routing systems that rely on static rules or limited optimization logic, agentic AI systems function as goal-oriented mobility agents capable of interpreting objectives such as congestion reduction, safety optimization, or network efficiency and determining the actions required to achieve them. These systems continuously ingest data from vehicles, infrastructure sensors, traffic signals, mapping platforms, weather systems, and user behavior to maintain real-time situational awareness. By retaining contextual memory across travel cycles and network events, agentic AI can anticipate demand surges, adjust routing strategies, and coordinate multiple transportation modes without manual intervention. This shift is transforming transportation networks from reactive control systems into adaptive mobility ecosystems that respond continuously to changing conditions. As urbanization accelerates and mobility systems become increasingly digitized, agentic AI is emerging as the intelligence layer that synchronizes vehicles, infrastructure, and services into cohesive smart mobility networks.

Why Are Transport Authorities and Mobility Providers Accelerating Agentic Adoption?

Transport authorities and mobility service providers are accelerating adoption of agentic AI to manage rising system complexity and performance expectations. Rapid growth in urban populations, shared mobility services, and connected vehicle deployments is placing strain on traditional traffic management and public transport systems. Agentic AI systems address these challenges by autonomously coordinating traffic flows, public transit schedules, and multimodal connections across road, rail, and micro-mobility networks. In fleet operations, these systems dynamically optimize vehicle dispatch, routing, and utilization based on real-time demand and operational constraints. Public sector agencies are deploying agentic AI to improve incident response, infrastructure utilization, and service reliability without relying solely on manual control centers. The ability of agentic systems to operate continuously and adapt strategies as conditions evolve is becoming critical as mobility ecosystems expand across cities, regions, and service providers.

What Role Does Agentic AI Play in Autonomous Mobility and Network Optimization?

Agentic AI is playing a central role in advancing autonomous mobility and network optimization by enabling continuous decision-making across vehicles and infrastructure. In connected and autonomous vehicle environments, agentic systems coordinate perception, planning, and execution across fleets rather than treating each vehicle as an isolated entity. These systems evaluate traffic patterns, safety conditions, and regulatory constraints to adjust driving strategies, platooning behavior, and handoffs between human and automated control. In logistics and freight mobility, agentic AI optimizes route selection, delivery sequencing, and asset utilization across large vehicle networks. The integration of agentic intelligence with digital twins and simulation platforms allows mobility operators to test strategies and validate outcomes before deployment. This capability is improving network efficiency, reducing congestion, and enhancing safety across increasingly complex transportation ecosystems.

What Is Driving Market Expansion Across Transportation and Smart Mobility?

The growth in the Agentic Artificial Intelligence in Transportation and Smart Mobility market is driven by several factors that are directly linked to urbanization, mobility demand, and technological advancement. Rapid expansion of connected vehicle infrastructure and intelligent transport systems is increasing demand for autonomous intelligence capable of real-time coordination. Growth of shared mobility, ride-hailing, and on-demand transport services is driving adoption of agentic systems that can dynamically match supply with fluctuating demand. Rising congestion and environmental constraints in urban areas are pushing authorities to deploy continuous optimization solutions rather than static traffic controls. Increasing deployment of sensors, edge computing, and vehicle-to-everything communication is strengthening the data foundation required for agentic decision-making. Additionally, growing emphasis on safety, efficiency, and service reliability is accelerating adoption of autonomous intelligence across transportation networks. Together, these drivers are positioning agentic AI as a core capability for next-generation transportation and smart mobility systems

SCOPE OF STUDY:

The report analyzes the Agentic AI in Transportation and Smart Mobility market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Offering (Software Stack Offering, AI Compute Hardware Offering, Data & Simulation Services Offering, Integration & Consulting Offering); Deployment (Edge/On-Vehicle Deployment, Cloud Deployment, Hybrid Edge-Cloud Deployment, Infrastructure-Centric (Roadside/Control-Center) Deployment); Technology Layer (Perception & Sensor Fusion Technology Layer, Planning/Decision-Making Technology Layer, Control & Actuation Technology Layer, Edge-to-Cloud Learning Technology Layer); Transportation Mode (Passenger Cars Transportation Mode, Commercial Road Freight Transportation Mode, Micro-Mobility Transportation Mode, Rail Transportation Mode); Application (Autonomous Ride-Hailing Application, Smart Logistics & Robo-Delivery Application, Public Transit Optimization Application, Advanced Traffic Management Application, Emergency & Roadside Assistance Application)

Geographic Regions/Countries:
World; USA; Canada; Japan; China; Europe; France; Germany; Italy; UK; Rest of Europe; Asia-Pacific; Rest of World.

SELECT PLAYERS -

• Aurora Innovation, Inc.
• Baidu, Inc. (Apollo)
• Continental AG
• Cruise LLC
• Embark Technology, Inc.
• Mobileye Global Inc.
• Nuro, Inc.
• NVIDIA Corporation
• Oxbotica Ltd.
• Pony.ai Inc.
• Robert Bosch GmbH (Bosch Mobility)
• Tesla, Inc.
• TuSimple Holdings Inc.
• Waymo LLC
• Zoox Inc.

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