Vehicle transportation underpins much of modern life, enabling the movement of goods and people, productivity, and economic growth. However, the costs are high: drivers spend an average of 2.6 years of their life on the road, and private cars and vans now account for around 10% of global CO2 emissions. Hence, the efficient use of transportation networks is of paramount importance. Can road traffic routing be managed system-wide the way aviation manages airspace or the internet routes data packets? While ground transportation has historically lacked a physical control tower, digital platforms offer a powerful glimpse into a more coordinated future.
The proliferation of navigation services, connected vehicles, smart cities, and autonomous vehicles all provide opportunities to improve both measurement and optimization of transportation resources. Google Research has already demonstrated the power of infrastructure-level intervention with Project Green Light, which uses AI to optimize city traffic lights. Unfortunately, optimizing vehicle networks has proven challenging. While individual vehicle routing is standard across all the top navigation products, optimizing routing system-wide is not yet present. Although theoretical models for network optimization exist, large-scale empirical validation remains limited, thereby hindering forward progress.
In “Urban congestion relief experiments through routing-app interventions”, published in Nature Cities, we present the first large-scale, real-world study into the use of navigation platforms to improve traffic. We show that coordinating even a small fraction of trips to disperse traffic can measurably improve driving speeds and reduce emissions for the entire city. It also establishes an experimentation framework for evolving from individual trip optimization toward a cooperative routing paradigm that enhances total network efficiency.
