GenMRP: A Generative Multi-Route Planning Framework for Efficient and Personalized Real-Time Industrial Navigation

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.

Comprehensive Comparison Network: a framework for locality-aware, routes-comparable and interpretable route recommendation

Route recommendation (RR) is a core task of route planning in the Amap app, with the goal of recommending the optimal route among candidate routes to users. Unlike traditional recommendation methods, insights into the local quality of routes and comparisons between candidate routes are crucial for enhancing recommendation performance but often overlooked in previous studies. To achieve these, we propose a novel model called Comprehensive Comparison Network (CCN). CCN not only uses query-level features (e.g. user features) and item-level features (e.g. route features, item embedding) that are common in traditional recommendations, but also introduces comparison-level features which describe the non-overlapping segments between different routes to capture the local quality of routes. The key component Comprehensive Comparison Block (CCB) in CCN is designed to enable comparisons between routes. CCB includes a Comprehensive Comparison Operator (CCO) and a multi-scenario MLP, which can update the representations of candidate routes based on a comprehensive comparison. By stacking multiple CCBs, CCN can determine the final scores of candidate routes and recommend the optimal one to the user. Additionally, since routes directly affect the costs and risks experienced by users, the RR model must be interpretable for online deployment. Therefore, we designed an interpretable pair scoring network to achieve interpretability. Both offline and online experiments demonstrate that CCN significantly improves RR performance and exhibits strong interpretability. CCN has been fully deployed in the Amap app for over a year, providing stable and optimal benefits for route recommendations.