Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries
This work addresses the need for cost-effective and robust road network design in developing countries, though it appears incremental as it applies an existing generative method to a specific domain problem.
The paper tackled the problem of generating diverse and valid roundabout designs for developing countries by formulating it as a Markov decision process and using Generative Flow Networks (GFlowNets) as a generator. The results showed that the method achieved better diversity while maintaining a high validity score compared to related methods.
Due to limited resources and fast economic growth, designing optimal transportation road networks with traffic simulation and validation in a cost-effective manner is vital for developing countries, where extensive manual testing is expensive and often infeasible. Current rule-based road design generators lack diversity, a key feature for design robustness. Generative Flow Networks (GFlowNets) learn stochastic policies to sample from an unnormalized reward distribution, thus generating high-quality solutions while preserving their diversity. In this work, we formulate the problem of linking incident roads to the circular junction of a roundabout by a Markov decision process, and we leverage GFlowNets as the Junction-Art road generator. We compare our method with related methods and our empirical results show that our method achieves better diversity while preserving a high validity score.