论文标题
基于周期性的乘车需求预测的时空注意力卷积网络
A Spatial-Temporal Attention Multi-Graph Convolution Network for Ride-Hailing Demand Prediction Based on Periodicity with Offset
论文作者
论文摘要
乘车服务已成为城市运输的主要部分。为了提高乘车服务的效率，准确预测运输需求是一个基本挑战。在本文中，我们从网络结构和数据集公式的方面都解决了这个问题。对于网络设计，我们提出了一个时空注意的多画卷积网络（STA-MGCN）。开发了STA-MGCN中的时空层，以通过时间注意机制和时间栅极卷积捕获时间相关性，以及通过Multigraph卷积的空间相关性。引入特征群集层以学习潜在的区域功能并减轻计算负担。对于数据集公式，我们开发了一种新颖的方法，该方法考虑了具有偏移的周期性的运输特征。还包括在同一时期使用历史记录数据，还包括昨天和上周向后相邻时期内的历史顺序需求。在纽约，芝加哥和成都的三个现实世界数据集上进行了广泛的实验表明，拟议的算法实现了乘车需求预测的最新性能。
Ride-hailing service is becoming a leading part in urban transportation. To improve the efficiency of ride-hailing service, accurate prediction of transportation demand is a fundamental challenge. In this paper, we tackle this problem from both aspects of network structure and data-set formulation. For network design, we propose a spatial-temporal attention multi-graph convolution network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to capture the temporal correlations by temporal attention mechanism and temporal gate convolution, and the spatial correlations by multigraph convolution. A feature cluster layer is introduced to learn latent regional functions and to reduce the computation burden. For the data-set formulation, we develop a novel approach which considers the transportation feature of periodicity with offset. Instead of only using history data during the same time period, the history order demand in forward and backward neighboring time periods from yesterday and last week are also included. Extensive experiments on the three real-world datasets of New-York, Chicago and Chengdu show that the proposed algorithm achieves the state-of-the-art performance for ride-hailing demand prediction.
