@inproceedings{
ikram2023probabilistic,
title={Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries},
author={Zarif Ikram and Ling Pan and Dianbo Liu},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=WWqJWiyQ2D}
}
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.
Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries
Zarif Ikram Ling Pan Dianbo Liu