Problem
Research questions and friction points this paper is trying to address.
route planning
user preferences
feedback loop problem
optimal route
Customizable and Jointly Optimized Route Planning: A Deep Architecture Enabling Differentiable Shortest-Path Search
🤖 AI Summary
本文提出一种深度架构，通过联合优化成本函数和路线排名模型来解决路径规划中的最优性和用户偏好问题，使用多目标Dijkstra算法与新颖的损失函数。
📝 Abstract
With the widespread use of online navigation and ride-hailing services, achieving optimal route planning for diverse user preferences has recently attracted increasing attention. Classic graph algorithms for pathfinding use heuristic cost functions to define edge weight, thus providing no optimality guarantee of route quality. Prior data-driven approaches equating ground truth of the optimal route with user trajectory, which is however moderately influenced by the navigation service, suffers from the feedback loop problem. To address these issues, we propose a deep architecture that is able to jointly optimize cost functions and route-ranking model towards any route preference. First, we run a multi-objective Dijkstra algorithm offline to collect the set of Pareto optimal routes, deeming it as the complete candidate set. Exploiting the property of such a set, we design a neural network structure that emulates shortest-path search and route ranking in an end-to-end differentiable manner. Second, we define route preference as a task of constrained optimization of route attributes, and propose a novel loss function that optimizes a single-objective variable, with other variables strictly under constraints. We conduct extensive experiments on real-world datasets. The results show that our architecture significantly outperforms state-of-the-art methods in route quality and customizability.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Deep Architecture
Pareto Optimal Routes
Differentiable Shortest-Path Search
Constrained Optimization
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