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Proceedings ArticleDOI

Graph neural network in traffic forecasting: a review

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TLDR
In this paper, a review of the related work and the applications of GNNs in different traffic forecasting problems, e.g., bike sharing, metro flow, road traffic flow prediction, etc.
Abstract
Traffic Forecasting is an important and challenging problem. The recent developed deep learning models are becoming dominant in this area. Especially, graph neural networks (GNNs) are being applied in traffic forecasting in recent years. In this paper, I give a review of the related work and the applications of GNNs in different traffic forecasting problems, e.g., bike sharing, metro flow, road traffic flow prediction, etc. I find that GNNs are only applied in recent years, and there is still a great research potential for this direction.

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Proceedings ArticleDOI

Origin-Destination Matrix Prediction via Graph Convolution: a New Perspective of Passenger Demand Modeling

TL;DR: A unified model, Grid-Embedding based Multi-task Learning (GEML) which consists of two components focusing on spatial and temporal information respectively, designed to model the spatial mobility patterns of passengers and neighboring relationships of different areas is proposed.
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Spatial Temporal Incidence Dynamic Graph Neural Networks for Traffic Flow Forecasting

TL;DR: The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods.
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A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data

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Modeling taxi services with smartphone-based e-hailing applications

TL;DR: Wang et al. as mentioned in this paper proposed a spatial equilibrium model that not only balances the supply and demand of taxi services but also captures both the taxi drivers' and customers' possible adoption of the newly emerging e-hailing applications in a well-regulated taxi market.
Journal ArticleDOI

Geospatial Data to Images: A Deep-Learning Framework for Traffic Forecasting

TL;DR: A deep-learning framework is proposed, which transforms geospatial data to images, and then utilizes the state-of-the-art deep- learning methodologies such as Convolutional Neural Network (CNN) and residual networks, and significantly outperforms traditional methods.
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