Traffic on roads outside of urban areas (i.e. extra-urban roads) can have major ecological and environmental impacts on agricultural, forested, and natural areas. Yet, data on extra-urban traffic volumes is lacking in many regions. To address this data gap, we produced a global time-series of traffic volumes (Annual Average Daily Traffic; AADT) on all extra-urban highways, primary roads, and secondary roads for the years 1975, 1990, 2000 and 2015. We constructed time series of road networks from existing global datasets on roads, population density, and socio-economic indicators, and combined these with a large collection of empirical AADT data from all continents except Antarctica. We used quantile regression forests to predict the median and 5% and 95% prediction intervals of AADT on each road section. The validation accuracy of the model was high (pseudo-R² = 0.7407) and AADT predictions from 1975 were also accurate. The resulting map series provides standardised and fine-scaled information on the development of extra-urban road traffic and has a wide variety of practical and scientific applications.

城市地区以外的道路（即城外道路）上的交通会对农业、森林和自然区域产生重大的生态和环境影响。然而，许多地区缺乏城外交通量的数据。为了解决这一数据差距，我们制作了 1975 年、1990 年、2000 年和 2015 年所有城外高速公路、主要道路和次要道路的全球交通量时间序列（年平均每日交通量；AADT）。道路网络的时间序列来自现有的全球道路数据集、人口密度和社会经济指标，并将这些数据与来自除南极洲以外的所有大陆的大量经验AADT数据相结合。我们使用分位数回归森林来预测每个路段上 AADT 的中值以及 5% 和 95% 预测区间。该模型的验证精度很高（伪 R ²  = 0.7407），1975 年的 AADT 预测也很准确。由此产生的地图系列提供了有关城外道路交通发展的标准化和精细比例的信息，并具有广泛的实际和科学应用。