用多层时空注意力模型预测共享单车站点流量，提升系统调度效率
Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network
- 设计双注意力网络，分别捕捉站点间空间关联与流量时间特征
- 在纽约超700个站点、逾千万骑行数据上实现高精度预测
- 适合城市交通规划、共享出行平台优化调度策略
城市微出行资源（如共享单车）的高效利用面临站点级供需失衡的挑战，导致系统维护困难。尽管已有研究致力于精准预测骑行流量（需求/取车与供给/还车），但站点级流量预测因系统时空复杂性而困难；且全系统预测因站点数量庞大而更具挑战。为此，本文提出BikeMAN——一种多层次时空注意力神经网络，用于预测整个共享单车系统的站点级流量。该网络包含编码器与解码器，通过两个注意力机制分别建模站点特征间的空间相关性与站点流量的时间特性。在纽约市超过700个站点、超1000万次骑行数据上的实验表明，该模型能高精度预测全市所有站点的流量。
原文摘要 · Abstract (English)
Efficient use of urban micromobility resources such as bike sharing is challenging due to the unbalanced station-level demand and supply, which causes the maintenance of the bike sharing systems painstaking. Prior efforts have been made on accurate prediction of bike traffics, i.e., demand/pick-up and return/drop-off, to achieve system efficiency. However, bike station-level traffic prediction is difficult because of the spatial-temporal complexity of bike sharing systems. Moreover, such level of prediction over entire bike sharing systems is also challenging due to the large number of bike stations. To fill this gap, we propose BikeMAN, a multi-level spatio-temporal attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed network consists of an encoder and a decoder with an attention mechanism representing the spatial correlation between features of bike stations in the system and another attention mechanism describing the temporal characteristic of bike station traffic. Through experimental study on over 10 millions trips of bike sharing systems (> 700 stations) of New York City, our network showed high accuracy in predicting the bike station traffic of all stations in the city.
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