4.3 Article

MTMGNN: Multi-time multi-graph neural network for metro passenger flow prediction

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Artificial Intelligence

DeepCrowd: A Deep Model for Large-Scale Citywide Crowd Density and Flow Prediction

Renhe Jiang et al.

Summary: The density and flow of the crowd or traffic at a citywide level can be predicted using big data and cutting-edge AI technologies. This research topic is significant and has high social impact, as it can be applied to emergency management, traffic regulation, and urban planning. In this study, a new aggregated human mobility dataset is published, and a novel deep learning model called DeepCrowd is proposed for predicting crowd and traffic.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)

Article Computer Science, Information Systems

The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting

Jinliang Deng et al.

Summary: This study focuses on the co-evolution between different modes of transport and proposes the CEST method to tackle the challenges of co-evolving patterns, effectively predicting the demand for target transportation.

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA (2021)

Article Computer Science, Artificial Intelligence

Dual attentive graph neural network for metro passenger flow prediction

Yuhuan Lu et al.

Summary: A novel dual attentive graph neural network was developed to predict the distribution of metro traffic flow, taking spatial and temporal influences into account. The model incorporated inbound and outbound graphs with weighted matrices, as well as multi-layer graph spatial attention networks to capture dynamic inter-station correlations. The proposed model not only accurately predicted metro passenger flow, but also effectively detected emergencies and incidents within the metro system.

NEURAL COMPUTING & APPLICATIONS (2021)

Proceedings Paper Computer Science, Artificial Intelligence

ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting

Jinliang Deng et al.

Summary: Multi-variate time series data is a common type of data abstraction in the real world, generated from a hybrid dynamical system influenced by complex external impacts. By using two normalization modules, the high-frequency and local components of the raw data can be refined, enhancing the performance of traditional deep learning architectures in applications of MTS.

KDD '21: PROCEEDINGS OF THE 27TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Countrywide Origin-Destination Matrix Prediction and Its Application for COVID-19

Renhe Jiang et al.

Summary: This study collected large-scale GPS trajectory data covering 47 prefectures of Japan and applied a deep learning model, ODCRN, to successfully predict nationwide human mobility in the future. The experimental results demonstrate the superiority of this method over existing OD prediction models and the high reliability and applicability in predicting COVID-19 spread.

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2021: APPLIED DATA SCIENCE TRACK, PT IV (2021)

Proceedings Paper Computer Science, Artificial Intelligence

Multi-STGCnet: A Graph Convolution Based Spatial-Temporal Framework for Subway Passenger Flow Forecasting

Jiexia Ye et al.

2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2020)

Proceedings Paper Computer Science, Information Systems

Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network

Hongzhi Shi et al.

2020 IEEE 36TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2020) (2020)

Proceedings Paper Computer Science, Information Systems

DeepUrbanEvent: A System for Predicting Citywide Crowd Dynamics at Big Events

Renhe Jiang et al.

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)

Proceedings Paper Computer Science, Information Systems

Effective and Efficient Reuse of Past Travel Behavior for Route Recommendation

Lisi Chen et al.

KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING (2019)

Article Computer Science, Information Systems

Efficient task assignment in spatial crowdsourcing with worker and task privacy protection

An Liu et al.

GEOINFORMATICA (2018)

Article Computer Science, Hardware & Architecture

Parallel trajectory similarity joins in spatial networks

Shuo Shang et al.

VLDB JOURNAL (2018)

Proceedings Paper Computer Science, Information Systems

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Guokun Lai et al.

ACM/SIGIR PROCEEDINGS 2018 (2018)

Article Computer Science, Information Systems

Trajectory Similarity Join in Spatial Networks

Shuo Shang et al.

PROCEEDINGS OF THE VLDB ENDOWMENT (2017)

Article Computer Science, Artificial Intelligence

Collective Travel Planning in Spatial Networks

Shuo Shang et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2016)

Article Computer Science, Artificial Intelligence

Finding regions of interest using location based social media

Shuo Shang et al.

NEUROCOMPUTING (2016)

Article Computer Science, Hardware & Architecture

Personalized trajectory matching in spatial networks

Shuo Shang et al.

VLDB JOURNAL (2014)

Proceedings Paper Telecommunications

Modeling of Traffic-Aware Travel Time in Spatial Networks

Shuo Shang et al.

2013 IEEE 14TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT (MDM 2013), VOL 1 (2013)

Article Engineering, Civil

Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results

BM Williams et al.

JOURNAL OF TRANSPORTATION ENGINEERING (2003)