Analytical Evaluation of VANETs Routing Strategies

Abstract

Vehicular ad hoc networks (VANETs) have emerged as a critical enabler of intelligent transportation systems, particularly when integrated with 5G infrastructure to achieve high-throughput, low-latency vehicle-to-everything (V2X) communication. Nevertheless, optimizing message routing in such environments remains a significant challenge, as the operational complexity and prohibitive cost of large-scale physical deployments severely limit empirical evaluation of alternative transmission strategies. This paper presents a stochastic Petri nets (SPNs) model for evaluating routing configurations in 5G-enabled vehicular ad hoc networks (5G-VANETs). The proposed model evaluates mean response time, drop probability, utilization, and throughput, enabling the identification of communication bottlenecks without requiring physical deployment. By abstracting the system's stochastic behavior through SPN formalism, the model supports both steady-state analysis and sensitivity evaluation under varying traffic workloads. Results demonstrate that Route 1, with direct RSU connection, achieves the lowest mean response time and highest throughput, while Route 3, which relays messages through a rear vehicle and an auxiliary RSU, yields the lowest drop probability. A sensitivity analysis based on Design of Experiments reveals that cloud capacity and cloud service time are the dominant factors affecting mean response time. The SPN model thus enables system architects to compare routing configurations, identify performance bottlenecks, and size infrastructure components without requiring physical deployment.