Smart City Gnosys

Smart city article details

Title	An Attention-Mechanism-Based Traffic Flow Prediction Scheme For Smart City
ID_Doc	7670
Authors	Hu X.; Wei X.; Gao Y.; Zhuang W.; Chen M.; Lv H.
Year	2019
Published	2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019
DOI	http://dx.doi.org/10.1109/IWCMC.2019.8766639
Abstract	With the continuous development of smart city, the task of base station traffic flow prediction has become an urgent problem to be solved. In the traffic flow prediction, historical traffic data can provide valuable and important information. However, traditional algorithms cannot allocate reasonable attention to historical traffic during model fitting and prediction. In other words, the attention capability of models is not considered. In order to handle this issue, this paper proposes a base station traffic flow prediction scheme based on Long Short-Term Memory with attention mechanism (A-LSTM). The designed A-LSTM scheme contains three steps. Firstly, the collected base station data should be cleaned up. Subsequently, feature engineering is performed. Finally, the traffic flow prediction algorithm based on the A-LSTM algorithm is realized. It is noted that missing values padding can also be performed under the framework of the A-LSTM. Experiments on real dataset show that the adopted A-LSTM can more effectively model the long term sequential relationship in the data, comparing with the LSTM and the other related algorithms, and achieve the best performance in base station traffic flow prediction for the dataset.
Author Keywords	Attention mechanism; Base station; LSTM; Smart city; Traffic flow prediction

Similar Articles

Id	Similarity	Authors	Title	Published
2156	0.912	Yang S.	A Hybrid Deep Learning Model Combining Multi-Scale Self-Attention And Lstm For Accurate Traffic Flow Prediction	IET Conference Proceedings, 2025, 2 (2025)
35799	0.907	Tran-Van N.-Y.; Pham N.-T.; Le K.-H.	Ls-Tfp: A Lstm-Based Traffic Flow Prediction Method In Intelligent Internet Of Things	Cognitive Science and Technology (2022)
60175	0.906	Zhang X.; Huang K.; Liu C.; Xu X.	Urban Short - Term Traffic Flow Prediction Algorithm Based On Cnn-Lstm Model	2023 3rd International Conference on Consumer Electronics and Computer Engineering, ICCECE 2023 (2023)
58581	0.905	Yao T.; Yang C.	Traffic Flow Prediction Based On Improved Lstm And Mobile Big Data In Smart Cities	Advances in Transportation Studies, 64 (2024)
8075	0.902	Zheng G.; Chai W.K.; Katos V.	An Ensemble Model For Short-Term Traffic Prediction In Smart City Transportation System	Proceedings - IEEE Global Communications Conference, GLOBECOM (2019)
7345	0.902	Sawah M.S.; Taie S.A.; Ibrahim M.H.; Hussein S.A.	An Accurate Traffic Flow Prediction Using Long-Short Term Memory And Gated Recurrent Unit Networks	Bulletin of Electrical Engineering and Informatics, 12, 3 (2023)
58576	0.899	Zheng L.; Pu Y.; Sun W.	Traffic Flow Prediction Algorithm Based On Attention Spatiotemporal Graph Convolution Mechanism	IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (2024)
38701	0.896	Praveen Kumar B.; Hariharan K.	Multivariate Time Series Traffic Forecast With Long Short Term Memory Based Deep Learning Model	Proceedings of 2020 IEEE International Conference on Power, Instrumentation, Control and Computing, PICC 2020 (2020)
33152	0.89	Huang X.; Zhang B.; Feng S.; Ye Y.; Li X.	Interpretable Local Flow Attention For Multi-Step Traffic Flow Prediction	Neural Networks, 161 (2023)
46740	0.886	Wang J.; Yang S.; Gao Y.; Wang J.; Alfarraj O.	Road Network Traffic Flow Prediction Method Based On Graph Attention Networks	Journal of Circuits, Systems and Computers, 33, 15 (2024)