Vehicular communication networks are essential to the development of intelligent navigation systems and improvement of road safety. Unlike most terrestrial networks of today, vehicular networks are characterized by stringent reliability and latency requirements. In order to design efficient networks to meet these requirements, it is important to understand the system-level performance of vehicular networks. Stochastic geometry has recently emerged as a powerful tool for the modeling and analysis of wireless communication networks. However, the canonical spatial models such as the 2D Poisson point process (PPP) does not capture the peculiar spatial layout of vehicular networks, where the locations of vehicular nodes are restricted to roadways. Motivated by this, we consider a doubly stochastic spatial model that captures the spatial coupling between the vehicular nodes and the roads and analyze the performance of vehicular communication networks. We model the spatial layout of roads by a Poisson line process (PLP) and the locations of nodes on each line (road) by a 1D PPP, thereby forming a Cox process driven by a PLP or Poisson line Cox process (PLCP). In this dissertation, we develop the theory of the PLCP and apply it to study key performance metrics such as coverage probability and rate coverage for vehicular networks under different scenarios.

First, we compute the signal-to-interference plus noise ratio (SINR)-based success probability of the typical communication link in a vehicular ad hoc network (VANET). Using this result, we also compute the area spectral efficiency (ASE) of the network. Our results show that the optimum transmission probability that maximizes the ASE of the network obtained for the Cox process differs significantly from that of the conventional 1D and 2D PPP models.

Second, we calculate the signal-to-interference ratio (SIR)-based downlink coverage probability of the typical receiver in a vehicular network for the cellular network model in which each receiver node connects to its closest transmitting node in the network. The conditioning on the serving node imposes constraints on the spatial configuration of interfering nodes and also the underlying distribution of lines. We carefully handle these constraints using various fundamental distance properties of the PLCP and derive the exact expression for the coverage probability.

Third, building further on the above mentioned works, we consider a more complex cellular vehicle-to-everything (C-V2X) communication network in which the vehicular nodes are served by roadside units (RSUs) as well as cellular macro base stations (MBSs). For this setup, we present the downlink coverage analysis of the typical receiver in the presence of shadowing effects. We address the technical challenges induced by the inclusion of shadowing effects by leveraging the asymptotic behavior of the Cox process. These results help us gain useful insights into the behavior of the networks as a function of key network parameters, such as the densities of the nodes and selection bias.

Fourth, we characterize the load on the MBSs due to vehicular users, which is defined as the number of vehicular nodes that are served by the MBS. Since the limited network resources are shared by multiple users in the network, the load distribution is a key indicator of the demand of network resources. We first compute the distribution of the load on MBSs due to vehicular users in a single-tier vehicular network. Building on this, we characterize the load on both MBSs and RSUs in a heterogeneous C-V2X network. Using these results, we also compute the rate coverage of the typical receiver in the network.

Fifth and last, we explore the applications of the PLCP that extend beyond vehicular communications. We derive the exact distribution of the shortest path distance between the typical point and its nearest neighbor in the sense of path distance in a Manhattan Poisson line Cox process (MPLCP), which is a special variant of the PLCP. The analytical framework developed in this work allows us to answer several important questions pertaining to transportation networks, urban planning, and personnel deployment.