TY - GEN
T1 - Traffic congestion prediction by spatiotemporal propagation patterns
AU - Di, Xiaolei
AU - Xiao, Yu
AU - Zhu, Chao
AU - Deng, Yang
AU - Zhao, Qinpei
AU - Rao, Weixiong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/6
Y1 - 2019/6
N2 - Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-Term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.
AB - Accurate prediction of traffic congestion at the granularity of road segment is important for planning travel routes and optimizing traffic control in urban areas. Previous works often calculated only the average congestion levels of a large region covering many road segments and did not take into account spatial correlation between road segments, resulting in inaccurate and coarse-grained prediction. To overcome these issues, we propose in this paper CPM-ConvLSTM, a spatiotemporal model for short-Term prediction of congestion level in each road segment. Our model is built on a spatial matrix which incorporates both the congestion propagation pattern and the spatial correlation between road segments. The preliminary experiments on the traffic data set collected from Helsinki, Finland prove that CPM-ConvLSTM greatly outperforms 6 counterparts in terms of prediction accuracy.
KW - Short term Prediction
KW - Spatiotemporal Deep Learning Model
KW - Traffic congestion
UR - http://www.scopus.com/inward/record.url?scp=85071007755&partnerID=8YFLogxK
U2 - 10.1109/MDM.2019.00-45
DO - 10.1109/MDM.2019.00-45
M3 - Conference contribution
AN - SCOPUS:85071007755
T3 - Proceedings - IEEE International Conference on Mobile Data Management
SP - 298
EP - 303
BT - Proceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th International Conference on Mobile Data Management, MDM 2019
Y2 - 10 June 2019 through 13 June 2019
ER -
