Parallel Multi-Graph Convolution Network For Metro Passenger Volume Prediction

Gao, Fuchen; Wang, Zhanquan; Liu, Zhenguang

doi:10.1109/dsaa53316.2021.9564196

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Parallel Bidirectional Gated Recurrent Unit (PB-GRU) [46]: A deep learning model composed of parallel multi-graph convolution and stacked bidirectional unidirectional gated recurrent unit for metro passenger volume prediction.…”

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confidence: 99%

A Spatial-Temporal Graph Convolutional Recurrent Network for Transportation Flow Estimation

Drosouli,

Voulodimos,

Mastorocostas

et al. 2023

Sensors

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Accurate estimation of transportation flow is a challenging task in Intelligent Transportation Systems (ITS). Transporting data with dynamic spatial-temporal dependencies elevates transportation flow forecasting to a significant issue for operational planning, managing passenger flow, and arranging for individual travel in a smart city. The task is challenging due to the composite spatial dependency on transportation networks and the non-linear temporal dynamics with mobility conditions changing over time. To address these challenges, we propose a Spatial-Temporal Graph Convolutional Recurrent Network (ST-GCRN) that learns from both the spatial stations network data and time series of historical mobility changes in order to estimate transportation flow at a future time. The model is based on Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) in order to further improve the accuracy of transportation flow estimation. Extensive experiments on two real-world datasets of transportation flow, New York bike-sharing system and Hangzhou metro system, prove the effectiveness of the proposed model. Compared to the current state-of-the-art baselines, it decreases the estimation error by 98% in the metro system and 63% in the bike-sharing system.

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confidence: 99%