Interblock Flow Prediction With Relation Graph Network for Cold Start on Bike-Sharing System
Interblock Flow Prediction With Relation Graph Network for Cold Start on Bike-Sharing System
复制标题
共享单车系统冷启动中基于关系图网络的块间流量预测
DOI：
10.1109/jiot.2022.3142070
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发表时间：
2022-08
影响因子：
10.6
通讯作者：
Hao Liu
中科院分区：
文献类型：
--
作者：
Mingda Jiang;Chao Li;Kehan Li;Zidong Yang;Hao Liu
随着物联网技术的成熟和共享经济在全球范围内的扩张，共享单车系统(BSS)在过去十年中迅速传播。在一个新的城市引入BSS时，运营商经常面临许多挑战：例如，优化站点选址(物理或电动)、建设自行车道，以及制定自行车的初始分布和再平衡策略。这些挑战要求在部署BSS之前进行准确的块间流量预测。在本文中，我们从城市道路网络中提取街区，并根据POI的分布和类型提取街区的特征。然后，可以根据起始/结束块和相邻块的特征来预测块间流量。我们提出了一种具有广义注意力机制的统一架构，称为关系图网络(RGN)，用于跨城市提取块特征和进行预测。在真实数据集上的评估表明，RGN在多个指标上都优于其他流行的图神经网络。进一步模拟了基于RGN预测的站点选址和自行车道规划的应用，结果表明BSS的部署/运行效率有了显著的提高。
As the IoT technology becomes well established and sharing economy expands worldwide, the bike-sharing system (BSS) has spread fast in the last decade. When introducing the BSS in a new city, the operator often faces many challenges: e.g., optimizing station siting (physical or electric), constructing bike lanes, and making strategies for the initial distribution and rebalancing of bikes. These challenges require an accurate interblock flow prediction before deploying the BSS. In this article, we derive blocks from the urban road network and extract blocks’ features based on the distribution and types of POI. Then, the interblock flow can be predicted based on the features of both start/end block and neighbor blocks. We propose a unified architecture with a generalized attention mechanism named the relation graph network (RGN) to extract block features and make predictions across cities. The evaluations on real-world data sets show that RGN outperforms other popular graph neural networks in multiple metrics. We further simulate the applications of station site selection and bike lane planning based on the prediction from RGN, and the result provides a noticeable increase in the BSS’s deployment/operation efficiency.