{"title":"基于物理引导的深度学习模型用于大流行病下城市轨道交通系统的短期始发站-终点站需求预测","authors":"","doi":"10.1016/j.eng.2024.04.020","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate origin–destination (OD) demand prediction is crucial for the efficient operation and management of urban rail transit (URT) systems, particularly during a pandemic. However, this task faces several limitations, including real-time availability, sparsity, and high-dimensionality issues, and the impact of the pandemic. Consequently, this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network (PAG-STAN) for metro OD demand prediction under pandemic conditions. Specifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices. Subsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices. Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic. Finally, a masked physics-guided loss function (MPG-loss function) incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability. PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios, highlighting its robustness and sensitivity for metro OD demand prediction. A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.</div></div>","PeriodicalId":11783,"journal":{"name":"Engineering","volume":"41 ","pages":"Pages 276-296"},"PeriodicalIF":10.1000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic\",\"authors\":\"\",\"doi\":\"10.1016/j.eng.2024.04.020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate origin–destination (OD) demand prediction is crucial for the efficient operation and management of urban rail transit (URT) systems, particularly during a pandemic. However, this task faces several limitations, including real-time availability, sparsity, and high-dimensionality issues, and the impact of the pandemic. Consequently, this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network (PAG-STAN) for metro OD demand prediction under pandemic conditions. Specifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices. Subsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices. Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic. Finally, a masked physics-guided loss function (MPG-loss function) incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability. PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios, highlighting its robustness and sensitivity for metro OD demand prediction. A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.</div></div>\",\"PeriodicalId\":11783,\"journal\":{\"name\":\"Engineering\",\"volume\":\"41 \",\"pages\":\"Pages 276-296\"},\"PeriodicalIF\":10.1000,\"publicationDate\":\"2024-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2095809924002637\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095809924002637","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
准确的始发站(OD)需求预测对于城市轨道交通(URT)系统的高效运营和管理至关重要,尤其是在大流行病期间。然而,这项任务面临着一些限制,包括实时性、稀疏性和高维性问题,以及大流行病的影响。因此,本研究提出了一个统一的框架,称为物理引导的自适应图时空注意力网络(PAG-STAN),用于大流行病条件下的地铁外径需求预测。具体来说,PAG-STAN 引入了一个实时 OD 估算模块,用于实时估算完整的 OD 需求矩阵。随后,提出了一个新颖的动态 OD 需求矩阵压缩模块,以生成密集的实时 OD 需求矩阵。之后,PAG-STAN 利用各种异构数据了解大流行期间未来 OD 乘客量的演变趋势。最后,一个掩蔽物理引导损失函数(MPG-loss function)将外包需求和入境流量之间的物理量信息纳入损失函数,以增强模型的可解释性。PAG-STAN 在大流行和传统情景下的两个真实世界地铁外径需求数据集上表现出良好的性能,突出了其在地铁外径需求预测方面的稳健性和灵敏度。为验证 PAG-STAN 中每个模块的不可或缺性,还进行了一系列消融研究。
Physics Guided Deep Learning-Based Model for Short-Term Origin–Destination Demand Prediction in Urban Rail Transit Systems Under Pandemic
Accurate origin–destination (OD) demand prediction is crucial for the efficient operation and management of urban rail transit (URT) systems, particularly during a pandemic. However, this task faces several limitations, including real-time availability, sparsity, and high-dimensionality issues, and the impact of the pandemic. Consequently, this study proposes a unified framework called the physics-guided adaptive graph spatial–temporal attention network (PAG-STAN) for metro OD demand prediction under pandemic conditions. Specifically, PAG-STAN introduces a real-time OD estimation module to estimate real-time complete OD demand matrices. Subsequently, a novel dynamic OD demand matrix compression module is proposed to generate dense real-time OD demand matrices. Thereafter, PAG-STAN leverages various heterogeneous data to learn the evolutionary trend of future OD ridership during the pandemic. Finally, a masked physics-guided loss function (MPG-loss function) incorporates the physical quantity information between the OD demand and inbound flow into the loss function to enhance model interpretability. PAG-STAN demonstrated favorable performance on two real-world metro OD demand datasets under the pandemic and conventional scenarios, highlighting its robustness and sensitivity for metro OD demand prediction. A series of ablation studies were conducted to verify the indispensability of each module in PAG-STAN.
期刊介绍:
Engineering, an international open-access journal initiated by the Chinese Academy of Engineering (CAE) in 2015, serves as a distinguished platform for disseminating cutting-edge advancements in engineering R&D, sharing major research outputs, and highlighting key achievements worldwide. The journal's objectives encompass reporting progress in engineering science, fostering discussions on hot topics, addressing areas of interest, challenges, and prospects in engineering development, while considering human and environmental well-being and ethics in engineering. It aims to inspire breakthroughs and innovations with profound economic and social significance, propelling them to advanced international standards and transforming them into a new productive force. Ultimately, this endeavor seeks to bring about positive changes globally, benefit humanity, and shape a new future.