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.
@article{arxiv.2310.03687,
title = {Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries},
author = {Zarif Ikram and Ling Pan and Dianbo Liu},
journal= {arXiv preprint arXiv:2310.03687},
year = {2023}
}