Long short-term memory neural network for traffic speed prediction using remote microwave sensor data

By Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, Yunpeng Wang. 2015

2,059 citations recorded by the source corpus.

Subfields: Building and Construction, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/60a9bf1f11d16637297b8fa62318911df6e03bef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Building and Construction

In Control and Systems Engineering

In Signal Processing

Citations in the corpus

Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management (2019, background): “With the recent advancement in deep learning paradigm, the ITS research community has made efforts to extend the ITS capabilities with deep learned modeling [17], [27], [28].”
GMAN: A Graph Multi-Attention Network for Traffic Prediction (2019, background): “…networks (RNN) are the trafﬁc condition of sensor 3 at time step t + l + 1 may be more correlated to that of distant time steps (e.g., t − 1 ) rather than recent time steps (e.g., t + l ). widely applied to model such temporal correlations (Ma et al. 2015; Song, Kanasugi, and Shibasaki 2016).”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “In traffic flow area, deep learning model has become a popular tool to predict traffic flow density [83]–[86].”
Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction (2017, methodology): “[30] introduced a long short-term memory neural network (LSTM NN) into traffic prediction and demonstrated that LSTM NN outperformed other neural networks in both”
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting (2020, background): “It has been considered a standard forecasting model for several tasks such as traffic prediction [13], solar power forecasting [14], financial market predictions [15], and electricity price prediction [16].”
Spatio-Temporal Meta-Graph Learning for Traffic Forecasting (2022, positive): “First, LSTM (Hochre-iter and Schmidhuber 1997) and GRU (Chung et al. 2014) demonstrated superior performance in traffic modeling (Ma et al. 2015a; Lv et al. 2018; Li et al. 2018; Zhao et al. 2019; Wang et al. 2020; Bai et al. 2020; Ye et al. 2021; Shang, Chen, and Bi 2021; Lee et al. 2022) as well…”
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning (2019, methodology): “Existing predictive model research, in the field of traffic prediction, can be roughly divided into two categories: parametric models and nonparametric models (Ma et al. 2015).”
High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “capability of capturing complex non-linear relationships, machine learning methods, like support vector regression (SVR), tend to outperform the statistical methods, such as autoregressive integrated moving average (ARIMA) and its many variants [12], with respect to handling complex traffic forecast”
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction (2018, methodology): “Many classical baseline models used in traffic forecasting problems, like ARIMA [2, 23] Support Vector Regression (SVR) [37], Kalman filter [35].”
Stock closing price prediction based on sentiment analysis and LSTM (2019, methodology): “Ma [22] used LSTM to capture nonlinear traffic dynamics for short-term traffic prediction, achieving the best predictive performance in terms of accuracy and stability.”