Neural-Network-Based Models for Short-Term Traffic Flow Forecasting Using a Hybrid Exponential Smoothing and Levenberg–Marquardt Algorithm
By Kit Yan Chan, Tharam Singh Dillon, Jaipal Singh, Elizabeth J. Chang. IEEE transactions on intelligent transportation systems (Print), 2012
371 citations recorded by the source corpus.
Subfields: Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/c663bbbefed5d9a89ef8d66e56f32576cee37fe6
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
- Yunpeng Wang
- Xiaolei Ma
- Guojie Song
- Kunqing Xie
- Manoel Castro-Neto
- Youngseon Jeong
- A. K. Qin
- Farhana Murtaza Choudhury
In Civil and Structural Engineering
Citations in the corpus
- Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning (2014, methodology): “Some representative methods are artificial neural networks (NNs) [3], [24], support vector regression (SVR) [2], and local weighted learning (LWL) [23].”
- A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (2020, methodology): “[30] imbued a neural network model with the hybrid exponential smoothing method to preprocess training data and the Levenberg-Marquardt algorithm to train the network weights.”
- Supervised Weighting-Online Learning Algorithm for Short-Term Traffic Flow Prediction (2013, methodology): “[3] have proposed a hybrid exponential smoothing method and the Levenberg–Marquardt algorithm to train NN for short-term traffic flow forecasting.”
- Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks (2017, methodology): “Chan employed a smoothing technique to pre-process traffic data before inputting the data into NNs for prediction, which achieved more than a 6% accuracy [21,22].”
- Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach (2017, methodology): “…forecasting problems, a broad range of approaches have been proposed, including the ARIMA family (Zhang et al., 2011; Khashei et al., 2012), local regression model (Antoniou et al., 2013), neural network based algorithms (Chan et al., 2012), and Bayesian inferring approaches (Fei et al., 2011).”