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.

翻译：由于资源有限且经济增长迅速，以低成本方式设计最优交通路网并进行仿真验证，对于发展中国家至关重要——因为这些地区昂贵且通常不可行的大规模人工测试难以实施。当前基于规则的生成器缺乏多样性，而多样性是设计鲁棒性的关键特征。生成流网络（GFlowNets）通过学习随机策略从非归一化奖励分布中采样，在保持高质量解决方案的同时保留其多样性。在本工作中，我们将环形交叉口内连接支路与环形交叉路口的任务形式化为马尔可夫决策过程，并利用GFlowNets作为Junction-Art道路生成器。我们将所提方法与相关方法进行对比，实验结果表明，本方法在保持高有效性得分的同时，实现了更好的多样性。