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Roadway free-flow speed captures the typical vehicle speed in low trafficconditions. Modeling free-flow speed is an important problem in transportationengineering with applications to a variety of design, operation, planning, andpolicy decisions of highway systems. Unfortunately, collecting large-scalehistorical traffic speed data is expensive and time consuming. Traditionalapproaches for estimating free-flow speed use geometric properties of theunderlying road segment, such as grade, curvature, lane width, lateralclearance and access point density, but for many roads such features areunavailable. We propose a fully automated approach, RasterNet, for estimatingfree-flow speed without the need for explicit geometric features. RasterNet isa neural network that fuses large-scale overhead imagery and aerial LiDAR pointclouds using a geospatially consistent raster structure. To support trainingand evaluation, we introduce a novel dataset combining free-flow speeds of roadsegments, overhead imagery, and LiDAR point clouds across the state ofKentucky. Our method achieves state-of-the-art results on a benchmark dataset.

RasterNet: Modeling Free-Flow Speed using LiDAR and Overhead Imagery