Inference and prediction of routes have become of interest over the past decade owing to a dramatic increase in package delivery and ride-sharing services. Given the underlying combinatorial structure and the incorporation of probabilities, route prediction involves techniques from both formal methods and machine learning. One promising approach for predicting routes uses decision diagrams that are augmented with probability values. However, the effectiveness of this approach depends on the size of the compiled decision diagrams. The scalability of the approach is limited owing to its empirical runtime and space complexity. In this work, our contributions are two-fold: first, we introduce a relaxed encoding that uses a linear number of variables with respect to the number of vertices in a road network graph to significantly reduce the size of resultant decision diagrams. Secondly, instead of a stepwise sampling procedure, we propose a single pass sampling-based route prediction. In our evaluations arising from a real-world road network, we demonstrate that the resulting system achieves around twice the quality of suggested routes while being an order of magnitude faster compared to state-of-the-art.

翻译：过去十年间，由于包裹配送和网约车服务的急剧增长，路线的推断与预测引起了广泛关注。鉴于其潜在的组合结构以及概率的引入，路线预测涉及形式化方法和机器学习两大领域的技术。一种有前景的路线预测方法使用增强概率值的决策图。然而，该方法的有效性取决于所编译决策图的规模。因其经验性的运行时和空间复杂度，该方法的可扩展性受到限制。在本工作中，我们的贡献有两方面：首先，我们引入一种松弛编码，使用与路网图中顶点数量成线性关系的变量数量，从而显著减小生成的决策图规模。其次，我们提出一种基于单次采样的路线预测方法，替代原有的逐步采样过程。在基于真实路网数据的评估中，我们证明所提出的系统在建议路线质量上提升约两倍，同时速度比现有最优方法快一个数量级。