用强化学习优化自行车传感器布局，提升稀疏数据下的流量预测精度。
INSPIRE-GNN: Intelligent Sensor Placement to Improve Sparse Bicycling Network Prediction via Reinforcement Learning Boosted Graph Neural Networks
- 结合GNN与强化学习，智能选择传感器位置以提升预测性能。
- 在99%数据缺失的墨尔本路网中，500个新传感器使误差降低超30%。
- 适合城市交通规划者用于低成本扩展传感器网络，提升数据可靠性。
准确的链路级自行车流量估计对可持续城市交通规划至关重要。然而，许多城市因自行车计数传感器覆盖有限，面临严重数据稀疏问题。为此，我们提出INSPIRE-GNN，一种基于强化学习（RL）的混合图神经网络框架，旨在优化传感器部署并提升数据稀疏环境下的链路级自行车流量估计。该框架融合图卷积网络（GCN）与图注意力网络（GAT），并引入深度Q网络（DQN）作为强化学习代理，实现数据驱动的传感器位置策略选择。在包含15,933条道路段、仅141条有传感器覆盖（99%稀疏度）的墨尔本自行车网络上，通过部署50、100、200和500个新增传感器，INSPIRE-GNN显著提升了流量估计效果。相比传统启发式方法（如介数中心性、接近中心性、实际骑行活动与随机部署），在均方误差（MSE）、均方根误差（RMSE）和平均绝对误差（MAE）等关键指标上表现更优。此外，实验还对比了标准机器学习与深度学习模型，验证了其有效性。本框架为交通规划者提供了可操作的建议，助力高效扩展传感器网络，优化部署策略，最大化自行车数据估计的准确性与可靠性。
原文摘要 · Abstract (English)
Accurate link-level bicycling volume estimation is essential for sustainable urban transportation planning. However, many cities face significant challenges of high data sparsity due to limited bicycling count sensor coverage. To address this issue, we propose INSPIRE-GNN, a novel Reinforcement Learning (RL)-boosted hybrid Graph Neural Network (GNN) framework designed to optimize sensor placement and improve link-level bicycling volume estimation in data-sparse environments. INSPIRE-GNN integrates Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) with a Deep Q-Network (DQN)-based RL agent, enabling a data-driven strategic selection of sensor locations to maximize estimation performance. Applied to Melbourne's bicycling network, comprising 15,933 road segments with sensor coverage on only 141 road segments (99% sparsity) - INSPIRE-GNN demonstrates significant improvements in volume estimation by strategically selecting additional sensor locations in deployments of 50, 100, 200 and 500 sensors. Our framework outperforms traditional heuristic methods for sensor placement such as betweenness centrality, closeness centrality, observed bicycling activity and random placement, across key metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, our experiments benchmark INSPIRE-GNN against standard machine learning and deep learning models in the bicycle volume estimation performance, underscoring its effectiveness. Our proposed framework provides transport planners actionable insights to effectively expand sensor networks, optimize sensor placement and maximize volume estimation accuracy and reliability of bicycling data for informed transportation planning decisions.
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