Introducing Mobility AI: Advancing urban transportation (opens in new tab)

Google Research has introduced Mobility AI, a comprehensive program designed to provide transportation agencies with data-driven tools for managing urban congestion, road safety, and evolving transit patterns. By leveraging advancements in measurement, simulation, and optimization, the initiative translates decades of Google’s geospatial research into actionable technologies for infrastructure planning and real-time traffic management. The program aims to empower policymakers and engineers to mitigate gridlock and environmental impacts through high-resolution modeling and continuous monitoring of urban transportation systems.

Measurement: Understanding Mobility Patterns

The measurement pillar focuses on establishing a precise baseline of current transportation conditions using real-time and historical data.

• Congestion Functions: Researchers utilize machine learning and floating car data to develop city-wide models that mathematically describe the relationship between vehicle volume and travel speeds, even on roads with limited data.
• Geospatial Foundation Models: By applying self-supervised learning to movement patterns, the program creates embeddings that capture local spatial characteristics. This allows for better reasoning about urban mobility in data-sparse environments.
• Analytical Formulation: Specific research explores how adjusting traffic signal timing influences the distribution of flow across urban networks, revealing patterns in how congestion propagates.

Simulation: Forecasting and Scenario Analysis

Mobility AI uses simulation technologies to create digital twins of cities, allowing planners to test interventions before implementing them physically.

• Traffic Simulation API: This tool enables the modeling of complex "what-if" scenarios, such as the impact of closing a major bridge or reconfiguring lane assignments on a highway.
• High-Fidelity Calibration: The simulations are calibrated using large-scale, real-world data to ensure that the virtual models accurately reflect local driver behavior and infrastructure constraints.
• Scalable Evaluation: These digital environments provide a risk-free way to assess how new developments, such as the rise of autonomous vehicles or e-commerce logistics, will reshape existing traffic patterns.

Optimization: Improving Urban Flow

The optimization pillar focuses on applying AI to solve large-scale coordination problems, such as signal timing and routing efficiency.

• Project Green Light: This initiative uses AI to provide traffic signal timing recommendations to city engineers, specifically targeting a reduction in stop-and-go traffic to lower greenhouse gas emissions.
• System-Wide Coordination: Optimization algorithms work to balance the needs of multiple modes of transport, including public transit, cycling, and pedestrian infrastructure, rather than focusing solely on personal vehicles.
• Integration with Google Public Sector: Research breakthroughs from this program are being integrated into Google Maps Platform and Google Public Sector tools to provide agencies with accessible, enterprise-grade optimization capabilities.

Transportation agencies and researchers can leverage these foundational AI technologies to transition from reactive traffic management to proactive, data-driven policymaking. By participating in the Mobility AI program, public sector leaders can gain access to advanced simulation and measurement tools designed to build more resilient and efficient urban mobility networks.