arXiv Open Access 2023

Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries

Zarif Ikram Ling Pan Dianbo Liu
Lihat Sumber

Abstrak

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.

Topik & Kata Kunci

Penulis (3)

Z

Zarif Ikram

L

Ling Pan

D

Dianbo Liu

Format Sitasi

Ikram, Z., Pan, L., Liu, D. (2023). Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countries. https://arxiv.org/abs/2310.03687

Akses Cepat

Lihat di Sumber
Informasi Jurnal
Tahun Terbit
2023
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓