TY - GEN
T1 - Detection of Illegal Parking Events Using Spatial-Temporal Features
AU - Jiang, Jiawei
AU - Chen, Yu Chen
AU - Hsieh, Hsun Ping
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/11/3
Y1 - 2020/11/3
N2 - In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a "next frame"prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.
AB - In this work, we propose a novel deep learning framework, called Attention-Based 2-layer Bi-ConvLSTM (denoted as Att-2BiConvLSTM) model, to predict the number of illegal-parking events in urban spaces. We model the research as a "next frame"prediction problem, which aims to improve urban transportation conditions and enhance the security and right-of-way for pedestrians. Various features in the prediction model are considered: some of them (e.g., hourly weather, traffic volumes) are dynamic every hour, while others (e.g., road network, point-of-interests) are static. To boost the effectiveness of static features, we propose a dynamic training process to transform the static features into dynamics. After that, all features can vary with time so that they are capable of handling a real-time prediction scenario. Moreover, we propose an attention mechanism for enhancing our bi-directional ConvLSTM model. With experimental verifications, we find that our proposed Att-2BiConvLSTM model can outperform other state-of-art and baseline methods. Besides, our model is useful for combining all features to make an accurate prediction.
UR - http://www.scopus.com/inward/record.url?scp=85097289870&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85097289870&partnerID=8YFLogxK
U2 - 10.1145/3397536.3428350
DO - 10.1145/3397536.3428350
M3 - Conference contribution
AN - SCOPUS:85097289870
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 667
EP - 668
BT - Proceedings of the 28th International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
A2 - Lu, Chang-Tien
A2 - Wang, Fusheng
A2 - Trajcevski, Goce
A2 - Huang, Yan
A2 - Newsam, Shawn
A2 - Xiong, Li
PB - Association for Computing Machinery
T2 - 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2020
Y2 - 3 November 2020 through 6 November 2020
ER -