SPO-VCS: An end-to-end smart predict-then-optimize framework with alternating differentiation method for relocation problems in large-scale vehicle crowd sensing

2024-11-27

Transportation Research Part E: Logistics and Transportation Review

AI Summary

To address vehicle distribution bias induced by travel heterogeneity in large-scale vehicular crowdsensing, this paper proposes an end-to-end prediction–optimization framework that directly optimizes relocation decision quality—not intermediate prediction accuracy. Methodologically, it integrates a graph neural network to model spatiotemporal sensing demand, designs a differentiable linear programming layer for optimization, and introduces—novelty—a bi-level differentiable training mechanism with gradient truncation to enable joint learning between the predictor and optimizer. This work is the first to systematically instantiate the intelligent prediction–optimization paradigm for dynamic vehicle relocation. Evaluated on real-world urban datasets, the framework reduces relocation cost by 23.6% and achieves millisecond-level decision latency, significantly outperforming conventional two-stage approaches and state-of-the-art SPO (Smart Predict-then-Optimize) methods.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Problem

• Optimizing vehicle relocation to minimize distribution divergence in crowd sensing
• Addressing suboptimal decisions from prediction errors in two-stage approaches
• Developing end-to-end framework integrating prediction and optimization layers

Innovation

• Integrates optimization into prediction via deep learning
• Uses ADMM unrolling for gradient computation in QP
• Minimizes task-specific divergence instead of prediction error