A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data
By Toon Bogaerts, A. Masegosa, Juan S. Angarita-Zapata, Enrique Onieva, P. Hellinckx. 2020
331 citations recorded by the source corpus.
Abstract Traffic forecasting is an important research area in Intelligent Transportation Systems that is focused on anticipating traffic in order to mitigate congestion. In this work we propose a deep neural network that simultaneously extracts the spatial features of traffic, using graph convolution, and its temporal features by means of Long Short Term Memory (LSTM) cells to make both short-term and long-term predictions. The model is trained and tested using sparse trajectory (GPS) data coming from the ride-hailing service of DiDi in the cities of Xi'an and Chengdu in China. Besides, presenting the deep neural network, we also propose a data-reduction technique based on temporal correlation to select the most relevant road links to be used as input. Combining the suggested approaches, our model obtains better results compared to high-performance algorithms for traffic forecasting, such as LSTM or the algorithms presented in the TRANSFOR19 forecasting competition. The model is capable of maintaining its performance over different time-horizons from 5 min to up to 4 h with multi-step predictions.
Subfields: Building and Construction, Transportation
View Paper: https://www.semanticscholar.org/paper/273cb1aa411c35ff9fcdb59cfef29df1e1724dfa
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In Transportation
Citations in the corpus
- BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation (2021, background): “Graph neural networks (GNNs) have received extensive attention from researchers due to their excellent performance on various graph learning tasks such as social analysis [24, 17, 29], drug discovery [12, 25], traffic forecasting [18, 3, 6], recommendation system [38, 32] and computer vision [39, 4]”
- Graph Neural Network for Traffic Forecasting: A Survey (2021, neutral): “Dataset Name Relevant Studies METR-LA [3], [4], [22], [25], [26], [27], [35], [38], [43], [44], [55], [57], [71], [72], [80], [105], [110], [117], [125], [129], [132], [137], [138], [141], [142], [143], [144], [145], [146], [148], [149], [153], [155], [159], [160], [172] PeMS all [28], [147] PeMS-BA”