Transport coding has emerged as a promising approach for reducing message delays in packet-switched communication networks by introducing controlled redundancy at the transport layer. While previous studies have provided analytical foundations for transport coding and demonstrated its theoretical gain in terms of average and maximum delay reduction, the practical performance of such schemes in realistic scenarios requires detailed modeling and simulation. This paper presents a comprehensive methodology for modeling transport coding in communication networks, combining analytical formulations with discrete-event simulations. The theoretical framework is based on Kleinrock’s network model, where message delay is expressed through order statistics of exponentially distributed packet transmission times. Redundancy is introduced by encoding k original packets into n coded packets, allowing successful message reconstruction upon reception of any k packets. This mechanism shifts the effective message delay from the maximum to the k-th order statistic, thereby reducing both mean delay and variance. To evaluate the performance of transport coding under realistic conditions, we develop a simulation model in OMNeT++ with a grid topology, FIFO queuing discipline, and exponentially distributed service and transmission delays. The model captures the interplay between coding overhead, network load, and message delivery deadlines. Simulation results demonstrate consistent delay reduction ( \(\approx 9\) – \(11\%\) ) across different channel delay and load parameters, confirming the theoretical predictions. Furthermore, transport coding is shown to improve delay stability by reducing variance and mitigating the probability of late or failed message delivery, which is critical for URLLC (Ultra-Reliable Low-Latency Communication) scenarios. The findings highlight the value of combining analytical and simulation approaches to quantify the effectiveness of transport coding. The proposed modeling framework can serve as a basis for optimizing coding parameters and guiding the deployment of low-latency services in 5G and beyond communication systems.