Short-term passenger flow forecasting is a crucial task for urban rail transit operations. Emerging deep-learning technologies have become effective methods used to overcome this problem. In this study, the authors propose a deep-learning architecture called Conv-GCN that combines a graph convolutional network (GCN) and a three-dimensional (3D) convolutional neural network (3D CNN). First, they introduce a multi-graph GCN to deal with three inflow and outflow patterns (recent, daily, and weekly) separately. Multi-graph GCN networks can capture spatiotemporal correlations and topological information within the entire network. A 3D CNN is then applied to deeply integrate the inflow and outflow information. High-level spatiotemporal features between different inflow and outflow patterns and between stations that are nearby and far away can be extracted by 3D CNN. Finally, a fully connected layer is used to output results. The Conv-GCN model is evaluated on smart card data of the Beijing subway under the time interval of 10, 15, and 30 min. Results show that this model yields the best performance compared with seven other models. In terms of the root-mean-square errors, the performances under three time intervals have been improved by 9.402, 7.756, and 9.256%, respectively. This study can provide critical insights for subway operators to optimise urban rail transit operations.
Study on Key Technologies of Transit Passengers Travel Pattern Mining and Applications based on Multiple Sources of Data
在本研究中，我们提出了一系列的方法来挖掘公交乘客的出行模式和行为偏好，然后我们使用这些知识来调整和优化公交系统。贡献包括：1）为了提高数据的有效性：a）提出了一种新的AFC（Automated Fare Collection）系统和AVL（Automated Vehicle Location）系统之间数据的时间差异校正方法，该方法将数据事件转换为信号，并应用时域相关技术检测和校正它们之间的相对差异。b）通过结合历史数据和乘客售票时间戳，我们引入和补偿AVL数据集中的缺失信息。2)为了推断乘客的下车点，我们引入了一个最大概率模型，结合乘客的家乡，以恢复其完整的过境轨迹从半完整的登机记录，然后我们提出了一个增强的活动识别算法，能够指定乘客的短期活动，从普通的换乘。最后，分析了公交客流的时空特征。3)发现旅客出行需求。我们整合每个乘客在多天的轨迹数据，构建一个混合出行图（HTG）。然后，我们使用深度搜索算法来获得空间封闭的过境出行链;最后，我们使用封闭的过境出行链的乘客，从不同的角度来研究他们的出行模式。最后，通过对乘客关键出行链的聚合，对城市公交走廊进行分析。4）推导出8个影响因素，构建了不同情景下乘客出行选择模型。接下来，我们验证我们的模型使用乘客重新分配模拟。最后，我们对乘客的时间选择偏好进行了综合分析，并利用这些信息来优化城市交通系统。
