Graph Neural Networks for Transit Passenger Flow Prediction

交通运输部门是加拿大温室气体排放的第二大来源。公共交通是加拿大未来减少温室气体排放目标的关键。因此，改善交通服务有助于吸引人们使用公共交通工具，从而有助于减少交通部门的排放。改善公共交通运营的一个关键因素是减少公共汽车上的拥挤。能够做到这一点的核心是能够准确预测公共汽车上的乘客数量，以便公交运营商可以调整他们的运营来管理和减少拥挤。与此同时，在过去的二十年里，人工智能和“深度学习”技术的发展和使用取得了非凡的进步。这些技术的许多进步都归功于卷积神经网络（cnn）在图像处理和分类中的应用。这些方法很好地适应了数据可以在规则（例如网格）欧几里得空间中表示的情况，但不太适应数据更适合被描述为包括链接（路段）和节点（路口）的网络或“图”的情况（如交通）。幸运的是，基于cnn的深度学习的进步刺激了创新，从而能够将深度学习技术用于图和图数据。这是通过图形表示学习的进步来实现的。图表示学习使卷积神经网络的功能能够应用于图学习，从而允许称为图神经网络（或gnn）的深度学习技术应用于与网络相关的应用，例如交通运输。虽然在道路性能预测中使用基于深度图的方法已经出现了爆炸式增长，但公共交通受到的关注较少，公共汽车交通几乎没有被探索过。本研究计划的目标是扩展gnn在公共交通，特别是公共汽车交通中的使用，以自动乘客计数和自动车辆定位的形式使用历史和实时大数据来预测公共汽车乘客水平。这将有助于将深度学习的力量引入公共交通规划，更广泛地为交通和计算机科学中的图形表示学习做出贡献，并帮助加拿大实现其温室气体排放目标。

The transportation sector is the second largest contributor to GHG emissions in Canada. Public Transportation is key to Canada's goals to reduce GHG emissions in the future. Improving transit services can thus help attract people towards public transportation thereby contributing to transportation sector emissions reductions. A key contributing factor to improving transit operations is the reduction of crowding on buses. Central to being able to do this is being able to accurately predict passenger levels on buses so that transit operators can adjust their operations to manage and reduce crowding. At the same time, the past twenty years has seen extraordinary advances in the development and use of Artificial Intelligence and "deep learning" techniques. Many of the advances from these techniques are thanks to the use of convolutional neural networks (CNNs) in image processing and classification. These methods are well adapted to situations in which data can be represented in regular (e.g. grids) Euclidean space, but are less well adapted to situations (like transportation) in which data is more appropriately described as networks or "graphs" that include links (road segments) and nodes (intersections). Luckily, advances in deep learning based on CNNs have spurred innovations leading to the ability to use deep learning techniques with graphs and graph data. This has been done through advances in graph representation learning. Graph representation learning has enabled the power of convolutional neural networks to be applied to graph learning and thus to allow such deep learning techniques called Graph Neural Networks (or GNNs) to be applied to network related applications such as those in transportation. While there has been an explosion in the use of deep graph-based approaches in road performance prediction, transit has received less attention, and bus transit has been almost unexplored. The goal of this research program is to extend the use of GNNs in public transportation and particularly bus transportation to predict bus passenger levels using both historical and real-time Big Data in the form of automatic passenger counts and automatic vehicle location. This will help bring the power of deep learning to public transportation planning, make contributions in graph representation learning in transportation and computer science more broadly, and help Canada meet its GHG emissions goals.