Existing industrial-scale navigation applications contend with massive road networks, typically employing two main categories of approaches for route planning. The first relies on precomputed road costs for optimal routing and heuristic algorithms for generating alternatives, while the second, generative methods, has recently gained significant attention. However, the former struggles with personalization and route diversity, while the latter fails to meet the efficiency requirements of large-scale real-time scenarios. To address these limitations, we propose GenMRP, a generative framework for multi-route planning. To ensure generation efficiency, GenMRP first introduces a skeleton-to-capillary approach that dynamically constructs a relevant sub-network significantly smaller than the full road network. Within this sub-network, routes are generated iteratively. The first iteration identifies the optimal route, while the subsequent ones generate alternatives that balance quality and diversity using the newly proposed correctional boosting approach. Each iteration incorporates road features, user historical sequences, and previously generated routes into a Link Cost Model to update road costs, followed by route generation using the Dijkstra algorithm. Extensive experiments show that GenMRP achieves state-of-the-art performance with high efficiency in both offline and online environments. To facilitate further research, we have publicly released the training and evaluation dataset. GenMRP has been fully deployed in a real-world navigation app, demonstrating its effectiveness and benefits.

翻译：现有工业级导航应用面临大规模路网挑战，通常采用两类主要路径规划方法。第一类依赖预计算道路成本进行最优路径规划，并采用启发式算法生成备选路线；第二类生成式方法近年来受到广泛关注。然而，前者在个性化和路径多样性方面存在局限，后者则难以满足大规模实时场景的效率需求。为克服这些限制，我们提出GenMRP——一种生成式多路径规划框架。为确保生成效率，GenMRP首先引入骨架-毛细血管方法，动态构建比完整路网显著缩小的相关子网络。在该子网络中，路径通过迭代方式生成：首次迭代识别最优路径，后续迭代则采用新提出的校正增强方法，生成兼顾质量与多样性的备选路径。每次迭代将道路特征、用户历史轨迹及已生成路径输入链路成本模型以更新道路成本，随后采用Dijkstra算法生成路径。大量实验表明，GenMRP在离线与在线环境中均以高效率达到最先进性能。为促进后续研究，我们已公开训练与评估数据集。GenMRP已在真实导航应用中全面部署，验证了其有效性与实用价值。