Smart card data has emerged in recent years and provide a comprehensive, and cheap source of information for planning and managing public transport systems. This paper presents a multi-stage machine learning framework to predict passengers’ boarding stops using smart card data. The framework addresses the challenges arising from the imbalanced nature of the data (e.g. many non-travelling data) and the ‘many-class’ issues (e.g. many possible boarding stops) by decomposing the prediction of hourly ridership into three stages: whether to travel or not in that one-hour time slot, which bus line to use, and at which stop to board. A simple neural network architecture, fully connected networks (FCN), and two deep learning architectures, recurrent neural networks (RNN) and long short-term memory networks (LSTM) are implemented. The proposed approach is applied to a real-life bus network. We show that the data imbalance has a profound impact on the accuracy of prediction at individual level. At aggregated level, FCN is able to accurately predict the rideship at individual stops, it is poor at capturing the temporal distribution of ridership. RNN and LSTM are able to measure the temporal distribution but lack the ability to capture the spatial distribution through bus lines.

近年来出现了智能卡数据，为规划和管理公共交通系统提供了全面、廉价的信息来源。本文提出了一个多阶段机器学习框架，使用智能卡数据预测乘客的登机停靠点。该框架通过将每小时乘客量的预测分解为三个阶段，解决了数据不平衡性（例如许多非旅行数据）和“多类”问题（例如许多可能的登机站）所带来的挑战：是否旅行或者不在那个一小时的时间段内，使用哪条公交线路，在哪个站上车。实现了一个简单的神经网络架构、全连接网络 (FCN) 和两个深度学习架构、循环神经网络 (RNN) 和长短期记忆网络 (LSTM)。所提出的方法应用于现实生活中的总线网络。我们表明，数据不平衡对个体层面的预测准确性有深远的影响。在聚合层面，FCN 能够准确预测各个站点的乘车量，但在捕捉乘车量的时间分布方面表现不佳。RNN 和 LSTM 能够测量时间分布，但缺乏通过总线捕获空间分布的能力。