Accurate passenger flow prediction of urban rail transit is essential for improving the performance of intelligent transportation systems, especially during the epidemic. How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic. To solve this issue, this paper proposes a brand-new transformer-based architecture called STformer under the encoder-decoder framework specifically for COVID-19. Concretely, we develop a modified self-attention mechanism named Causal-Convolution ProbSparse Self-Attention (CPSA) to model the multiple temporal dependencies of passenger flow with low computational costs. To capture the complex and dynamic spatial dependencies, we introduce a novel Adaptive Multi-Graph Convolution Network (AMGCN) by leveraging multiple graphs in a self-adaptive manner. Additionally, the Multi-source Data Fusion block fuses the passenger flow data, COVID-19 confirmed case data, and the relevant social media data to study the impact of COVID-19 to passenger flow. Experiments on real-world passenger flow datasets demonstrate the superiority of ST-former over the other eleven state-of-the-art methods. Several ablation studies are carried out to verify the effectiveness and reliability of our model structure. Results can provide critical insights for the operation of URT systems.
A Mobile Data-Driven Hierarchical Deep Reinforcement Learning Approach for Real-time Demand-Responsive Railway Rescheduling and Station Overcrowding Mitigation
Chang'an University（长安大学）；University of Leeds（利兹大学）
Predicting Subway Passenger Flows under Incident Situation with Causality
Key Laboratory of Road and Traffic Engineering, Ministry of Education at Tongji University（同济大学交通工程教育重点实验室）；Urban Mobility Institute, Tongji University（同济大学城市交通研究院）
A data-driven approach to inferring travel trajectory during peak hours in urban rail transit systems
Beijing Jiaotong University（北京交通大学）；China Railway Signaling and Communication Research and Design Institute（中国铁路通信信号研究设计院）；Beijing Urban Construction Design & Development Group Co.（北京城建设计发展集团有限公司）
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。
Exploring the impact of weather on Metro demand forecasting using machine learning method
Qiushi Honors College, Tianjin University（天津大学求是学部）；School of Traffic and Transportation, Beijing Jiaotong University（北京交通大学交通运输学院）；Maynooth International Engineering College, Fuzhou University（福州大学梅努斯国际工程学院）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Forestry University（北京林业大学）
