Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

• URL: http://arxiv.org/abs/2109.00924v1
• Date: Sun, 29 Aug 2021 13:07:18 GMT
• Title: Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction
• Authors: Fuchen Gao, Zhanquan Wang, Zhenguang Liu
• Abstract summary: This paper proposes a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) Experiments on two real-world datasets of subway passenger flow show the efficacy of the model.
• Score: 8.536743588315696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate prediction of metro passenger volume (number of passengers) is valuable to realize real-time metro system management, which is a pivotal yet challenging task in intelligent transportation. Due to the complex spatial correlation and temporal variation of urban subway ridership behavior, deep learning has been widely used to capture non-linear spatial-temporal dependencies. Unfortunately, the current deep learning methods only adopt graph convolutional network as a component to model spatial relationship, without making full use of the different spatial correlation patterns between stations. In order to further improve the accuracy of metro passenger volume prediction, a deep learning model composed of Parallel multi-graph convolution and stacked Bidirectional unidirectional Gated Recurrent Unit (PB-GRU) was proposed in this paper. The parallel multi-graph convolution captures the origin-destination (OD) distribution and similar flow pattern between the metro stations, while bidirectional gated recurrent unit considers the passenger volume sequence in forward and backward directions and learns complex temporal features. Extensive experiments on two real-world datasets of subway passenger flow show the efficacy of the model. Surprisingly, compared with the existing methods, PB-GRU achieves much lower prediction error.

Related papers

• Traffic Prediction considering Multiple Levels of Spatial-temporal Information: A Multi-scale Graph Wavelet-based Approach [3.343804744266258]
  This study proposes a graph wavelet temporal convolution network (MSGWTCN) to predict the traffic states in complex transportation networks. Two real-world datasets are used to investigate the model performance, including a highway network in Seattle and a dense road network of Manhattan in New York City.
  arXiv  Detail & Related papers  (2024-06-18T20:05:47Z)
• Multi-Scale Spatial-Temporal Recurrent Networks for Traffic Flow Prediction [13.426775574655135]
  We propose a Multi-Scale Spatial-Temporal Recurrent Network for traffic flow prediction, namely MSSTRN. We propose a spatial-temporal synchronous attention mechanism that integrates adaptive position graph convolutions into the self-attention mechanism to achieve synchronous capture of spatial-temporal dependencies. Our model achieves the best prediction accuracy with non-trivial margins compared to all the twenty baseline methods.
  arXiv  Detail & Related papers  (2023-10-12T08:52:36Z)
• D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights [68.76631399516823]
  We present a trajectory prediction approach with respect to traffic lights, D2-TPred, using a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) Our experimental results show that our model achieves more than 20.45% and 20.78% in terms of ADE and FDE, respectively, on VTP-TL.
  arXiv  Detail & Related papers  (2022-07-21T10:19:07Z)
• Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction [52.0977259978343]
  This paper proposes a Continuous-time and Multi-level dynamic graph representation learning method for Origin-Destination demand prediction (CMOD) The state vectors keep historical transaction information and are continuously updated according to the most recently happened transactions. Experiments are conducted on two real-world datasets from Beijing Subway and New York Taxi, and the results demonstrate the superiority of our model against the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-30T03:37:50Z)
• Multi-View TRGRU: Transformer based Spatiotemporal Model for Short-Term Metro Origin-Destination Matrix Prediction [12.626657411944949]
  We propose a hy-brid framework Multi-view TRGRU to address OD metro matrix prediction. In particular, it uses three modules to model three flow change patterns: recent trend, daily trend, weekly trend. In each module, a multi-view representation based on embedding for each station is constructed and fed into a transformer based re- gated current structure.
  arXiv  Detail & Related papers  (2021-08-09T09:32:42Z)
• Multi-Graph Convolutional-Recurrent Neural Network (MGC-RNN) for Short-Term Forecasting of Transit Passenger Flow [7.9132565523269625]
  Short-term forecasting of passenger flow is critical for transit management and crowd regulation. An innovative deep learning approach, Multi-Graph Convolutional-Recurrent Neural Network (MGC-NN), is proposed to forecast passenger flow in urban rail transit systems.
  arXiv  Detail & Related papers  (2021-07-28T08:41:12Z)
• Online Metro Origin-Destination Prediction via Heterogeneous Information Aggregation [99.54200992904721]
  We propose a novel neural network module termed Heterogeneous Information Aggregation Machine (HIAM) to jointly learn the evolutionary patterns of OD and DO ridership. An OD modeling branch estimates the potential destinations of unfinished orders explicitly to complement the information of incomplete OD matrices. A DO modeling branch takes DO matrices as input to capture the spatial-temporal distribution of DO ridership. Based on the proposed HIAM, we develop a unified Seq2Seq network to forecast the future OD and DO ridership simultaneously.
  arXiv  Detail & Related papers  (2021-07-02T10:11:51Z)
• SGCN:Sparse Graph Convolution Network for Pedestrian Trajectory Prediction [64.16212996247943]
  We present a Sparse Graph Convolution Network(SGCN) for pedestrian trajectory prediction. Specifically, the SGCN explicitly models the sparse directed interaction with a sparse directed spatial graph to capture adaptive interaction pedestrians. visualizations indicate that our method can capture adaptive interactions between pedestrians and their effective motion tendencies.
  arXiv  Detail & Related papers  (2021-04-04T03:17:42Z)
• Coupled Layer-wise Graph Convolution for Transportation Demand Prediction [43.23120462553671]
  Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction. This paper provides a novel graph convolutional network for transportation demand prediction. Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi.
  arXiv  Detail & Related papers  (2020-12-15T04:10:09Z)
• Physical-Virtual Collaboration Modeling for Intra-and Inter-Station Metro Ridership Prediction [116.66657468425645]
  We propose a unified Physical-Virtual Collaboration Graph Network (PVCGN), which can effectively learn the complex ridership patterns from the tailor-designed graphs. Specifically, a physical graph is directly built based on the realistic topology of the studied metro system. A similarity graph and a correlation graph are built with virtual topologies under the guidance of the inter-station passenger flow similarity and correlation.
  arXiv  Detail & Related papers  (2020-01-14T16:47:54Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.