Coseismic landslides are a major source of transportation disruption in mountainous areas, but few approaches exist for rapidly estimating impacts to road networks. We develop a model that links the U.S. Geological Survey (USGS) near-real-time earthquake-triggered landslide hazard model with Open Street Map (OSM) road network data to rapidly estimate segment-level obstruction risk following major earthquake activity worldwide. To train and validate the model, we process OSM data for 15 historical earthquakes and calculate the average segment-level landslide hazard from the USGS model for each event. We then fit a multivariate adaptive regression spline model for the probability of road obstruction as a function of road segment length and landslide hazard, using a training and validation dataset derived from the intersections of road networks with earthquake-triggered landslide inventories. The resulting probabilistic model is well calibrated across a range of earthquake events, with estimated obstruction probabilities matching the relative frequency of potential road obstructions. The model runs quickly and is capable of producing road segment-level obstruction estimates within minutes to hours of a major earthquake. However, in near-real-time application, the accuracy of the obstruction estimates will be dependent on the quality of the ShakeMap shaking estimates, which often improves with time as more information becomes available after the earthquake. By providing a rapid first-order translation of landslide hazard into potential infrastructure impacts, this model helps provide emergency responders with tangible information on initial areas of concern.

同震滑坡是山区交通中断的主要来源，但很少有方法可以快速估计对道路网络的影响。我们开发了一个模型，该模型将美国地质调查局 (USGS) 近实时地震引发的滑坡灾害模型与开放街道地图 (OSM) 道路网络数据联系起来，以快速估计全球大地震活动后的路段级障碍风险。为了训练和验证模型，我们处理了 15 次历史地震的 OSM 数据，并根据 USGS 模型计算每个事件的平均段级滑坡危害。然后，我们拟合多元自适应回归样条模型，将道路阻塞的概率作为路段长度和滑坡危险的函数，使用源自道路网络与地震引发的滑坡清单的交叉点的训练和验证数据集。由此产生的概率模型在一系列地震事件中得到了很好的校准，估计的障碍概率与潜在道路障碍的相对频率相匹配。该模型运行迅速，能够在发生大地震的几分钟到几小时内生成路段级别的障碍估计。然而，在近实时应用中，障碍物估计的准确性将取决于 ShakeMap 振动估计的质量，随着地震后更多信息的可用，它通常会随着时间的推移而提高。通过将滑坡灾害快速一阶转化为潜在的基础设施影响，