Ubiquitous mobile devices have catalyzed the development of vehicle crowd sensing (VCS). In particular, vehicle sensing systems show great potential in the flexible acquisition of spatio-temporal urban data through built-in sensors under diverse sensing scenarios. However, vehicle systems often exhibit biased coverage due to the heterogeneous nature of trip requests and routes. To achieve a high sensing coverage, a critical challenge lies in optimally relocating vehicles to minimize the divergence between vehicle distributions and target sensing distributions. Conventional approaches typically employ a two-stage predict-then-optimize (PTO) process: first predicting real-time vehicle distributions and subsequently generating an optimal relocation strategy based on the predictions. However, this approach can lead to suboptimal decision-making due to the propagation of errors from upstream prediction. To this end, we develop an end-to-end Smart Predict-then-Optimize (SPO) framework by integrating optimization into prediction within the deep learning architecture, and the entire framework is trained by minimizing the task-specific matching divergence rather than the upstream prediction error. Methodologically, we formulate the vehicle relocation problem by quadratic programming (QP) and incorporate a novel unrolling approach based on the Alternating Direction Method of Multipliers (ADMM) within the SPO framework to compute gradients of the QP layer, facilitating backpropagation and gradient-based optimization for end-to-end learning. The effectiveness of the proposed framework is validated by real-world taxi datasets in Hong Kong. Utilizing the alternating differentiation method, the general SPO framework presents a novel concept of addressing decision-making problems with uncertainty, demonstrating significant potential for advancing applications in intelligent transportation systems.

翻译：无处不在的移动设备推动了车辆群智感知（VCS）的发展。具体而言，车辆感知系统通过内置传感器，在多样化的感知场景下，在灵活获取城市时空数据方面展现出巨大潜力。然而，由于出行请求和路线的异质性，车辆系统常常表现出有偏的覆盖。为实现高感知覆盖率，一个关键挑战在于如何最优地重定位车辆，以最小化车辆分布与目标感知分布之间的差异。传统方法通常采用两阶段的"预测-然后-优化"（PTO）流程：首先预测实时车辆分布，随后基于预测生成最优的重定位策略。然而，由于上游预测误差的传播，这种方法可能导致次优决策。为此，我们开发了一个端到端的智能预测-然后-优化（SPO）框架，通过在深度学习架构中将优化整合到预测中，并且整个框架通过最小化任务特定的匹配差异而非上游预测误差来进行训练。在方法论上，我们通过二次规划（QP）来形式化车辆重定位问题，并在SPO框架内结合了一种基于交替方向乘子法（ADMM）的新型展开方法，以计算QP层的梯度，从而促进端到端学习中的反向传播和基于梯度的优化。所提框架的有效性通过香港的真实出租车数据集得到了验证。利用交替微分方法，通用的SPO框架为解决具有不确定性的决策问题提出了一种新颖思路，展现了在推进智能交通系统应用方面的巨大潜力。