内容来源：https://research.google/blog/the-power-of-collaboration-how-we-can-reduce-traffic-congestion/

内容总结：

2026年7月7日，谷歌研究院软件工程师Neha Arora与Aboudy Kreidieh在《自然·城市》期刊发表一项大规模实地研究，首次验证了导航应用中的网络感知路由策略对提升整体交通效率的实际效果。研究团队在美国10座主要城市开展了为期六个月的实验，通过调整谷歌地图算法，引导约2%的出行车辆避开历史拥堵路段，转向耗时相近的替代路线。结果表明，即便只协调极小比例的出行，也能显著改善交通状况：目标路段平均行驶速度提升约2%，燃油消耗率下降0.5%至1.0%；受影响路段整体行驶速度中位数提升约0.35%，早晚高峰时段更达0.5%。按所研究城市的规模与能耗计算，每座城市每年可减少数千吨二氧化碳当量排放。研究指出，这一网络级导航干预不仅造福导航应用用户，未使用导航的驾驶者同样受益于拥堵缓解。该实验框架为未来智能城市中动态信号控制、实时路网优化等复杂交通管理提供了可复用的方法学基础。

中文翻译：

2026年7月7日
Google Research软件工程师Neha Arora与Aboudy Kreidieh
我们通过导航应用中网络感知路由功能，展示了其对提升网络效率的实际效果。

车辆运输支撑着现代生活的诸多方面，推动货物与人员的流动、生产力发展及经济增长。然而其代价同样高昂：驾驶员一生平均有2.6年耗费在道路上，而私家车与厢式货车的碳排放量目前约占全球总量的10%。因此，提升交通网络的使用效率至关重要。能否像航空管理空域或互联网路由数据包那样，对道路交通进行全局化调度？尽管地面交通历来缺少物理意义上的控制塔台，数字平台已为我们揭示了迈向更协调未来的可能性。

导航服务、联网汽车、智慧城市及自动驾驶技术的普及，为交通资源的测量与优化提供了新机遇。谷歌研究院此前已通过"绿灯计划"展示了基础设施级干预的威力——该技术利用人工智能优化城市交通信号灯。遗憾的是，车辆网络的优化仍面临挑战。尽管所有主流导航产品均已实现单一路径规划，但系统级路径优化尚未成为现实。虽然存在网络优化的理论模型，大规模实证验证仍十分有限，这阻碍了技术突破。

在发表于《自然·城市》的《通过导航应用干预缓解城市拥堵实验》一文中，我们首次开展了关于利用导航平台改善交通状况的大规模实地研究。研究表明，即使协调极小比例的出行流量进行分散，也能显著提升城市整体行驶速度并降低排放。该研究同时建立了一个实验框架，推动从单一出行优化向提升整体网络效率的协作路径规划范式演进。

我们在美国10个主要城市展开实验，验证低成本定向路径干预对改善整体交通状况的有效性。研究中，谷歌地图算法经过调整，优先推荐行驶时间相近但路段类型相似的备选路线，使出行流量主动避开预设的拥堵路段。

在为期六个月的研究中，我们采用全市范围的"交叉切换"（又称轮换）实验设计，通过连续天数交替使用干预算法与对照算法（未修改的原始算法），精准衡量干预效果。干预措施并非随机选择个别出行，而是系统性地覆盖整个城市。在"干预日"，修改后的路由算法将引导所有途经预设拥堵路段的出行，转向行驶时间相近的替代路线。实验期间，仅有不到2%的出行获得了调整后的路线推荐。

实验城市依据拥堵程度及基准数据可用性进行筛选。针对每个城市，我们根据历史拥堵模式（其特征为高峰时段反复出现的瓶颈点或高密度车流）选取约100个道路路段。下图展示了一个典型案例。

为量化路由干预的实际效果，我们采用层级贝叶斯结果建模框架进行分析。该模型可同时聚合城市层面与逐小时局部层面的参数，在不过度施加约束的前提下灵活捕捉共性变化规律，并支持跨城市、跨时段的数据共享，使特定城市或时段的效果估算能借助其他子组的效应估计值提升准确性。

研究发现，即便是微小的干预措施，也能带来可量化且具有统计显著性的交通状况改善。从城市均值来看，目标路段的行驶速度中位数提升约2%，对应燃油消耗率中位数下降0.5%至1.0%。在范围更广的受影响路段（即所有受到干预波及、包括被分流或承接车流的路段）中，行驶速度中位数提升约0.35%；在早晚交通高峰时段，提升幅度可达0.5%。按本研究涉及城市的规模与能源消耗水平换算，每个城市每年可减少数千吨二氧化碳当量排放。

行驶速度与排放率的改善在整个交通网络中普遍存在且具有统计显著性。这些成果源于将车辆从主要瓶颈点进行战略性分流：通过高效分散车流，外围道路在承载更多车辆的同时仍能维持较高平均速度与较低整体排放量。下图直观展示了这一效果。

这项研究清晰表明，网络化导航技术可以成为主动塑造交通流、服务社会利益的有力工具。通过协调极小比例的出行，我们就能实现惠及所有道路使用者的系统性效益——不仅限于使用特定导航应用的群体。值得注意的是，无论是否使用导航，所有道路使用者都能共享目标路段降堵带来的红利，表现为全网络行驶时间的改善与碳排放的减少。

除即时缓解拥堵外，该研究还为基于实验的严谨交通管理方法建立了蓝图。随着智慧城市基础设施日益成熟，本研究所验证的实验路径——利用互联技术测量并推动系统级变革——可应用于更广泛的挑战，例如复杂城市环境下的动态信号控制与实时网络优化。尽管这些成果展现了相对简单路径重规划手段的潜力，它们更关键的意义在于：为未来汽车、基础设施与网络感知路由协同运作、共同优化全社会的出行效率与可持续性奠定了基础。

本研究由Alexandre Bayen、Andrew Tomkins、Theophile Cabannes、Kevin Chen、Yechen Li、Marc Nunkesser、Prem Ramaswami、Eray Turkel、Shoshana Vasserman与张海正合作完成。

英文来源：

July 7, 2026
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.
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 both the aggregate city level and localized hourly level simultaneously, offers a flexible way to capture shared variations without imposing strict constraints. It also enables information sharing between cities and time periods, allowing estimates for a particular city or time to borrow strength from other subgroups' effect estimates.
The study found that even these small interventions led to measurable, statistically significant improvements in traffic conditions. Averaged across cities, we observe a median increase of around 2% in driving speeds on targeted segments, corresponding to a median decrease of 0.5% to 1.0% in fuel consumption rates. Over the much larger set of affected segments, i.e., all segments that were impacted by the intervention, including those to which traffic was redirected either away from or onto, driving speeds increased by around 0.35% on median, and 0.5% when traffic is highest in the morning and afternoon. At the scale and energy demands of the cities considered in this study, this translates to potential savings of thousands of tons of CO2e emissions per city per year.
Improvements in driving speeds and emission rates were both prevalent and statistically significant across the network. These gains were the result of the strategic diversion of vehicles from major bottlenecks; by dispersing this traffic efficiently, the peripheral roads maintained higher average speeds and lower overall emissions, even when absorbing higher volumes of vehicles. This behavior is illustrated in the figure below.
This research clearly shows that networked navigation technology can be a powerful tool for proactively shaping traffic flow for the benefit of society. By coordinating a small fraction of trips, we can achieve systemic gains that benefit all road users — not just those using a specific app. Notably, both navigation users and non-users share the advantages of decongesting targeted segments, leading to network-wide improvements in travel time and a reduction in CO2e emissions.
Beyond immediate congestion relief, this work establishes a blueprint for a rigorous, experiment-based approach to traffic management. As smart-city infrastructure matures, the experimental pathway demonstrated here — using connectivity to measure and facilitate system-level changes — can be applied to broader challenges like dynamic signal control and real-time network optimization in complex urban environments. While these results show the potential of relatively simple rerouting, they provide the foundation for a future where cars, infrastructure, and network-aware routing work together to optimize travel efficiency and sustainability for the entire community.
This work was conducted in collaboration with Alexandre Bayen, Andrew Tomkins, Theophile Cabannes, Kevin Chen, Yechen Li, Marc Nunkesser, Prem Ramaswami, Eray Turkel, Shoshana Vasserman, and Haizheng Zhang.