mlsanityData to 7 Oct 2026

ST-former for short-term passenger flow prediction during COVID-19 in urban rail transit system

arXiv 2210.09043 ↗First posted 14 Oct 2022cs.LG cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 49 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

16 of 49 references.

References · 16

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,141
  4. 2019Generating Long Sequences with Sparse Transformerscited by 1,523
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,428
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,125
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 820
  8. 2020A Generalization of Transformer Networks to Graphscited by 653
  9. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 606
  10. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 504
  11. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 399
  12. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
4 more
  1. 2018Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Predictioncited by 119
  2. 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Predictioncited by 67
  3. 2021Learning dynamic and hierarchical traffic spatiotemporal features with Transformercited by 26
  4. 2020From Twitter to Traffic Predictor: Next-Day Morning Traffic Prediction Using Social Media Datacited by 10

Built on by

Not cited yet.