TY  - JOUR
PY  - 2016//
TI  - Optimized structure of the traffic flow forecasting model with a deep learning approach
JO  - IEEE transactions on neural networks and learning systems
A1  - Yang, Hao-Fan
A1  - Dillon, Tharam S.
A1  - Chen, Yi-Ping Phoebe
SP  - 2371
EP  - 2381
VL  - 28
IS  - 10
N2  - Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.<p />  <p>Language: en</p>
LA  - en
SN  - 2162-237X
UR  - http://dx.doi.org/10.1109/TNNLS.2016.2574840
ID  - ref1
ER  -