原文
Neha Arora and Aboudy Kreidieh, Software Engineers, Google Research We demonstrate the effect of network-aware routing in navigation apps on improving network efficiency. 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. We ran an experiment in 10 major US cities to demonstrate the effectiveness of targeted low-cost routing interventions in improving overall traffic conditions. For this study, the Google Maps algorithm was modified to prefer alternative routes with similar travel times and segment types, effectively guiding trips away from the pre-selected congested segments. Over a six month period, we adopted a city-wide switchback (also known as crossover) experimental design, alternating between this treatment and the control (unaltered) routing algorithm over consecutive days to appropriately measure the effect of this intervention. Rather than randomly selecting individual trips, the intervention was applied systematically across the entire city. During “treatment” days, the modified routing guided all trips that encountered the pre-selected congested segments toward alternative routes with similar travel times. Under 2% of observed trips received altered routing recommendations as a result of this experiment. To set up the experiment, cities were chosen based on the congestion levels and ground truth availability. For each city, we selected roughly 100 road segments based on historical congestion patterns, characterized by recurring bottlenecks or high traffic density during peak demand. The figure below shows one such example. Within this study, we modify at the routing stage the perceived cost to trips passing through pre-selected segments depicting disproportionately high levels of demand and/or congestion. These modifications reroute trips with similarly costing alternative paths away from these segments, thereby reducing the flow of traffic that would have otherwise been experienced within them. To quantify the effect of our proposed routing intervention, we employed a hierarchical Bayesian outcome modeling framework for our analysis. This approach, which models parameters at b