MobiWave：用于自主车队再平衡的面向调度的图小波和漂移引导的选择性优化
MobiWave: Dispatch-Oriented Graph Wavelets and Drift-Guided Selective Optimization for Autonomous Fleet Rebalancing
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中文总结 AI 辅助
研究自主车队再平衡问题，提出MobiWave框架，通过面向调度的多尺度图小波模块分离图频率模式加权各尺度应对表示挑战，用漂移引导的层选择性优化测量漂移、选层及更新应对适应挑战，实验验证其有效性。
中文摘要 AI 辅助
自主车队使移动平台能够直接协调闲置车辆，实现全车队范围的再平衡。然而，重叠的区域和局部交通模式会掩盖对调度仍有用的道路，以及移动漂移会使训练好的策略不可靠，这两个障碍限制了可靠部署。现有空间聚合混合了这些模式，从有限的近期数据更新所有参数成本高且会损害稳定知识。我们提出了MobiWave框架，它将面向调度的多尺度图小波模块与漂移引导的层选择性优化（DGLS）相连。图小波模块通过分离图频率模式并根据其对需求预测和可行再平衡的价值对每个尺度加权来应对表示挑战。DGLS通过测量调度加权谱漂移、在资源预算内选择受影响的层以及通过漂移感知的快慢更新将短期冲击与持续变化分离来应对适应挑战。候选验证进一步拒绝那些在不恶化监测的服务或安全约束的情况下未能改善保留调度奖励的更新。在真实世界数据集和模拟环境上的实验证明了MobiWave与现有方法相比的有效性。
英文摘要
Autonomous fleets enable mobility platforms to coordinate idle vehicles directly, making fleet-wide rebalancing possible. However, two obstacles limit reliable deployment: overlapping regional and local traffic patterns can hide roads that remain useful for dispatch, and mobility drift can make a trained policy unreliable. Existing spatial aggregation mixes these patterns, while updating all parameters from limited recent data is costly and can damage stable knowledge. We propose \name, a framework that connects a dispatch-oriented multi-scale graph wavelet module with Drift-Guided Layer-Selective Optimization (DGLS). The first module addresses the representation challenge by separating graph-frequency patterns and weighting each scale according to its value for demand prediction and feasible rebalancing. DGLS addresses the adaptation challenge by measuring Dispatch-weighted Spectral Drift, selecting affected layers within a resource budget, and separating short shocks from persistent changes through a drift-aware fast--slow update. Candidate validation further rejects updates that fail to improve held-out dispatch reward without worsening monitored service or safety constraints. Experiments on both real-world datasets and simluated environments demonstrate the effectiveness of \name\ in comparing with state-of-the-art methods. The source code and datasets are available at https://anonymous.4open.science/r/MobiWave-40F8/.
