TY - GEN
T1 - Large-Scale Spatiotemporal Prediction Method of Traffic Speed Based on 3D Convolutional Neural Network
AU - Niu, Yuxin
AU - Yu, Haiyang
AU - Ren, Yilong
N1 - Publisher Copyright:
© 2020 ASCE.
PY - 2020
Y1 - 2020
N2 - In recent years, the development of big data acquisition and storage, computer technology and communication technology has provided new momentum for ITS, while traffic speed prediction is a core link of ITS. In order to achieve large-scale traffic forecasting in urban road network and extract the time series feature and spatial feature of road network speed evolution, a spatiotemporal prediction method based on 3D convolution neural network is proposed in this paper, using gridded historical traffic data and corresponding road network traffic speed for training. Finally, in the empirical analysis stage, 3D CNN is evaluated and compared with the prediction results of 2D CNN, LSTM, and BPNN models on the whole, midweek and weekend. Experimental results show that the MAE, MAPE, and RMSE indices of the test set are at least 10% better than other models. It has a good performance in the actual road network traffic speed prediction.
AB - In recent years, the development of big data acquisition and storage, computer technology and communication technology has provided new momentum for ITS, while traffic speed prediction is a core link of ITS. In order to achieve large-scale traffic forecasting in urban road network and extract the time series feature and spatial feature of road network speed evolution, a spatiotemporal prediction method based on 3D convolution neural network is proposed in this paper, using gridded historical traffic data and corresponding road network traffic speed for training. Finally, in the empirical analysis stage, 3D CNN is evaluated and compared with the prediction results of 2D CNN, LSTM, and BPNN models on the whole, midweek and weekend. Experimental results show that the MAE, MAPE, and RMSE indices of the test set are at least 10% better than other models. It has a good performance in the actual road network traffic speed prediction.
UR - https://www.scopus.com/pages/publications/85098277006
M3 - 会议稿件
AN - SCOPUS:85098277006
T3 - CICTP 2020: Transportation Evolution Impacting Future Mobility - Selected Papers from the 20th COTA International Conference of Transportation Professionals
SP - 163
EP - 172
BT - CICTP 2020
A2 - Wei, Heng
A2 - Wang, Haizhong
A2 - Zhang, Lei
A2 - An, Yisheng
A2 - Zhao, Xiangmo
PB - American Society of Civil Engineers (ASCE)
T2 - 20th COTA International Conference of Transportation Professionals: Transportation Evolution Impacting Future Mobility, CICTP 2020
Y2 - 14 August 2020 through 16 August 2020
ER -