  We introduce the binomial line process (BLP), a novel spatial stochastic
model for the characterization of streets in the statistical evaluation of
wireless and vehicular networks. Existing stochastic geometry models for
streets, e.g., Poisson line processes (PLP) and Manhattan line processes (MLP)
lack an important aspect of city-wide street networks: streets are denser in
the city center and sparse near the suburbs. Contrary to these models, the BLP
restricts the generating points of the streets to a fixed radius centered at
the origin of the Euclidian plane, thereby capturing the inhomogeneity of the
streets with respect to the distance from the center. We derive a closed-form
expression for the contact distribution of the BLP from a random location on
the plane. Leveraging this, we introduce the novel Binomial line Cox process
(BLCP) to emulate points on individual lines of the BLP and derive the
distribution to the nearest BLCP point from an arbitrary location. Using
numerical results, we highlight that the spatial configuration of the streets
is remarkably distinct from the perspective of a city center user to that of a
suburban user. The framework developed in this paper can be integrated with the
existing models of line processes for more accurate characterization of streets
in urban and suburban environments.
