Data-based optimisation of traffic flow simulations: A gradient based approach

The increasing spread of automated driving functions, ranging from relatively simple parking assistants to fully automated driving systems, has led to a surge in the demand for traffic data used for development, validation, and verification in order to minimize the safety risks. Synthetic data plays an ever-growing role at this point since real-world data alone cannot satisfy all demands due to costs and complexity of data acquisition. To generate such synthetic data, realistic, agent-based traffic flow simulations can be used where numerous individual agents interact according to parametric behavior models. In order to be able to realistically simulate traffic using such models, sufficiently accurate data-based optimization is crucial. Until now, the optimization of such models has required the use of gradient-free algorithms or machine learning methods, which can become computationally disproportionate for complex models. The concept for sample-based gradient approximation presented in this work has the potential to make the optimization of such complex parametric simulation models feasible and efficient by making gradient-based optimization algorithms usable. The application of this concept is presented at the example of optimizing model parameters in the traffic simulation framework PTV Vissim.