Deep Learning: A Generic Approach for Extreme Condition Traffic Forecasting

By Rose Yu, Yaguang Li, Cyrus Shahabi, Ugur Demiryurek, Yan Liu. 2017

395 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Environmental Engineering

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Citations in the corpus

Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018, methodology): “Yu et al. (2017) proposes to use Long Short Term Memory networks (LSTM) to predict loop sensor readings.”
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Ever since the precursory study of utilizing NN into the traffic prediction problem was proposed [39], many NN-based methods, like feed forward NN [41], fuzzy NN [40], recurrent NN (RNN) [42], and hybrid NN [25], are adopted for traffic forecasting problems.”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “Although some studies Chen et al. (2016); Wu and Tan (2016); Yu et al. (2017b) utilized more than one LSTM layers, the inﬂuence of the number of LSTM layers needs to be further evaluated.”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, background): “[112] outline a unique application where DL is used to model traffic flow after a (hopefully rare) accident has occurred.”
Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting (2019, methodology): “With convolutional neural network and recurrent neural network, state-of-the-art results are achieved in (Shi et al. 2015; Yu et al. 2017; Shi et al. 2017; Zhang, Zheng, and Qi 2017; Zhang et al. 2018a; Ma et al. 2017; Yao et al. 2018b; 2018a).”
A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “Another example is work [55] which used a combination of a Stacked Autoencoder to encode traffic accidents data and an LSTM to capture the temporal aspect of the data.”
Traffic Flow Prediction via Spatial Temporal Graph Neural Network (2020, background): “Recurrent Unit (GRU) networks [8] have been extensively applied in learning complicated schemes from a huge number of sequence information [11, 29, 38, 45].”
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks (2017, background): “Deep neural networks have been studied for time series forecasting [8, 27, 34, 37], i.”
Deep Learning for Spatio-Temporal Data Mining: A Survey (2019, methodology): “Application domains Related works Transportation [177], [187], [116], [31], [29], [6], [32], [127], [53], [112], [26], [105], [166], [82], [23], [66], [108], [126], [81], [139], [78], [128], [146], [148], [72], [98], [189], [22], [24], [99], [96], [16], [97], [85], [15], [164], [165], [95], [140], [”
High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, background): “RNN and its variants (long short-term memory (LSTM) recurrent neural network and gated recurrent unit (GRU)) show excellent potential for traffic forecasting [4, 15, 24].”
Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting (2019, background): “With the explosive growth of Internet of Things (IoT) applications and big data, multivariate time series is becoming ubiquitous in many ﬁelds, e.g., aerology [1], meteorology [2], environment [3], multimedia [4], power energy [5], ﬁnance [6], and transportation [7].”
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting (2017, background): “Most recently, deep learning models for trafﬁc forecasting have been developed in Lv et al. (2015); Yu et al. (2017b), but without considering the spatial structure.”
Spatio-Temporal Graph Structure Learning for Traffic Forecasting (2020, methodology): “Deep Recurrent Neural Networks were applied to trafﬁc forecasting (Yu et al. 2017; Laptev et al. 2017).”
Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2018, methodology): “To model non-linear temporal dependency, researchers propose to use recurrent neural network (RNN)-based framework (Yu et al. 2017; Cui, Ke, and Wang 2016).”