With the continuing expansion of metropolitan areas, the demand for efficient mass transportation systems is increasing accordingly. An emerging wireless technology for rail signaling, which can significantly improve the efficiency of light rail, subway, and high-speed train transit systems, is communication-based train control (CBTC). In CBTC systems, trains communicate wirelessly with wayside access points, which are connected to a central control station. A prerequisite for the deployment of such systems is the existence of a suitable propagation model, which can provide accurate path-loss predictions of corresponding wireless channels. This is of particular importance due to the safety-critical nature of rail signaling and the rapid, cost-effective nature of system deployment.

This thesis presents an advanced propagation modeling tool, based on parabolic equation methods, to characterize radio wave propagation in realistic, complex railway environments. Concrete guidelines are provided on how to extract input models from point cloud data and what level of detail is necessary and sufficient to obtain results agreeing with measured data. Moreover, enhanced techniques dealing with geometrical variations of guideways and practical antenna patterns are presented. The accuracy and efficiency of the developed propagation model are demonstrated in various practical scenarios.

Furthermore, a robust approach on extracting surrogate models from arbitrary tunnel geometries is proposed. The extracted surrogate models can lead to significant computational savings, especially in optimization studies where repetitive calls of the propagation model are required. The thesis also presents two hybrid propagation modeling techniques, aimed at achieving a better trade-off between accuracy, efficiency, and generality, via using the advantages of one method to compensate for the limitations of another and vice versa. Moreover, effects of uncertainty in the environment and experimental setup on the variability of predicted signal strength are investigated. A new approach that can incorporate wall roughness into the model is introduced. It can be applied to tunnels with arbitrary cross-section geometries and large-scale surface roughness. Finally, a physics-based optimization strategy is presented to optimize the placement of access points for train communication systems. The validity and usefulness of the proposed methodology are demonstrated in an actual deployment site of CBTC systems.