Urban traffic flow prediction using a fuzzy-neural approach

By Hong-ying Yin, S. Wong, Jian-min Xu, C. Wong. 2002

438 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Building and Construction, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/d1955f71f9d1d6920345007cfcd39a47cbc1651b

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Citations in the corpus

High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, methodology): “Ever since the precursory study [30] using the feed-forward NN for vehicle travel time estimation was proposed, many other NN-based models, including fuzzy NN [31], recurrent NN [9], convolution NN [5][18], deep belief networks [10][32], auto-encoders [11][33], generative adversarial networks [34][3”
Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Forecasting Network-wide Traffic State with Missing Values (2020, methodology): “…of utilizing NN into the traffic prediction problem was proposed Hua and Faghri (1994), many NN-based methods, like feed-forward NN Gers et al. (1999), fuzzy NN Yin et al. (2002), and recurrent NN (RNN) Van Lint et al. (2002) are adopted for traffic forecasting problems more than ten years ago.”
Link Prediction in Complex Networks: A Survey (2010, background): “A harder problem is to predict the weights of links, which is relevant to the traffic prediction for urban transportation and air transportation systems [160].”
Short‐Term Traffic Volume Forecasting Using Kalman Filter with Discrete Wavelet Decomposition (2007, methodology): “MAPE = 1 N N∑ k=1 ∣∣∣∣∣∣ ∧ vol(k) − vol(k) vol(k) ∣∣∣∣∣∣ × 100% (9) The second performance index used is root mean square error (RMSE) (Smith and Demetsky, 1994; Wild, 1997; Yin et al., 2002).”
Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm (2012, methodology): “A commonly used method for time series forecasting, i.e., TakagiSugeno fuzzy NNs [24], [25], [34], [36], which combine the mechanisms of fuzzy logic and feedforward NNs, has been proposed for short-term traffic flow forecasting [20], [39], [48].”
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.”
Road Traffic Forecasting: Recent Advances and New Challenges (2018, background): “Again, simulation models are the best suited to address this issue, parametrizing preceding inputs; notwithstanding, some researchers have concentrated on this matter by incorporating spatial information to time series [28], neural networks [37]–[39] or other non-parametric approaches [14], [40].”
An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic (2017, methodology): “Existing traffic prediction algorithms ranges from statistical prediction methods [6]–[11], artificial neural networks [12]–[15], fuzzy-neural networks [16]–[18], support”
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting (2020, methodology): “Common non-parametric models include k-nearest [2], support vector regression (SVR) [11, 30], fuzzy logic [34], Bayesian network[28], and neural network models.”
Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models (2009, methodology): “They can be combined with other pattern recognition techniques and optimization schemes like Fuzzy logic and Genetic Algorithms (GAs) to improve their prediction power ( Van Der Voort et al, 1996 ; Yin et al, 2002 ); however, they function like black boxes and, therefore, it is very difficult to is”
Short-term traffic flow prediction using seasonal ARIMA model with limited input data (2015, methodology): “The nonparametric techniques include nonparametric regression [4] and neural network [5–16].”
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting (2018, background): “proposed, many other NN-based models, including fuzzy NN [30], recurrent NN [8], convolution NN [5], [17], deep belief networks [9], [31], auto-encoders [10], [32], generative adversarial networks [33], [34], and combinations of these models have been applied to forecast traffic states.”