Optimizing Very Large Scale ITS Applications with Fast Fitness Evaluation

As wireless communication technology advances, the complexity of Vehicular Ad-hoc Network (VANET) simulations increases. This, however, is at odds with the need for increasingly large-scale Intelligent Transportation Systems (ITS) scenarios to satisfy the demands of increasingly Artificial Intelligence (AI) based solutions. This paper aims to demonstrate that a high-performance simplified VANET simulator can be used for fitness evaluation without loss in solution quality. As an example, we implement a Roadside Unit (RSU) deployment approach based on a genetic algorithm. Disolv, a simplified VANET simulator, is used as a fitness evaluation tool. To validate the solution quality, the best solutions of a few select generations are evaluated with a fully-featured ns-3 driven VANET simulation. From the comparison of fitness values, it can be observed that the values from Disolv allow us to predict those obtained via ns-3. Further, the execution time analysis showcases the substantial performance gains of using a more abstract VANET simulator. A 4.6-hour analysis with Disolv contrasts approximately 300 days with ns-3 for the given scenario and settings. Finally, the potential applications and limitations of using a simplified VANET simulator are discussed.