TY - GEN
T1 - Using a separable convolutional neural network for large-scale transportation network speed prediction
AU - Loaiza, F. A.
AU - Herrera, José
AU - Mantilla Sc, Luis
N1 - Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/1/8
Y1 - 2018/1/8
N2 - This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.
AB - This paper proposes the reduction of the convergence time on a Convolutional Neural Network (CNN) method for traffic speed prediction, without reducing the performance of speed prediction method. The proposed method contains two procedures: The first one is to convert the traffic network data to images; in this case the speed variable will be transformed. The second step of the procedure presents a modification of the CNN method for speed prediction in which a separable convolution is used to reduce the number of parameters. This separable convolution helps to reducing the convergence time of speed predictions for large-scale transportation network. The proposal is evaluated with real data from the Caltrans Performance Measurement System (PeMS), obtained through sensors. The results show that Separable Convolutional Neural Network (SCNN) reduces convergence time of CNN method without losing the performance of the predictions of traffic speed in a large-scale transportation network.
KW - Convolutional neural network
KW - Deep learning
KW - Separable convolution
KW - Spatiotemporal features
KW - Traffic speed prediction
KW - Transportation network
UR - http://www.scopus.com/inward/record.url?scp=85049835209&partnerID=8YFLogxK
U2 - 10.1145/3177457.3177464
DO - 10.1145/3177457.3177464
M3 - Articulo (Contribución a conferencia)
AN - SCOPUS:85049835209
T3 - ACM International Conference Proceeding Series
SP - 157
EP - 161
BT - Proceedings of the 10th International Conference on Computer Modeling and Simulation, ICCMS 2018
PB - Association for Computing Machinery
Y2 - 8 January 2018 through 10 January 2018
ER -