Organic and inorganic fiber reinforced composites with various fiber orientation distributions and fiber geometries are abundantly available in several natural and synthetic structures. Inorganic glass fiber composites have been introduced to numerous applications due to their economical fabrication and tailored structural properties. Numerical characterization of such composite materials is necessitated due to their intrinsic statistical nature, since elaborate experiments are prohibitively costly and time consuming. In this work, representative volume elements of unidirectional random filaments and fibers are numerically developed in PYTHON to enhance accuracy and efficiency of complex geometric representations encountered in random fiber networks. A modified random sequential adsorption algorithm is applied to increase the volume fraction of the representative volume elements, and a spatial segment shortest distance algorithm is introduced to construct a 3D random fiber composite with high fiber aspect ratio (100:1) and high volume fraction (31.8%). For the unidirectional fiber networks, volume fractions as high as 70% are achieved when an assortment of circular fiber diameters are used in the representative volume element.