Smart City Gnosys

Smart city article details

Title	Forecasting Crowd Distribution In Smart Cities
ID_Doc	26831
Authors	Cecaj A.; Lippi M.; Mamei M.; Zambonelli F.
Year	2020
Published	Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks workshops, 2020-June
DOI	http://dx.doi.org/10.1109/SECONWorkshops50264.2020.9149774
Abstract	In this work we present a forecasting method that can be used to predict crowd distribution across the city. Specifically, we analyze and forecast cellular network traffic and estimate crowd on such basis. Our forecasting model is based on a neural network combined with time series decomposition techniques. Our analysis shows that this approach can give interesting results in two directions. First, it creates a forecasting solution that fits all the variability in our dataset without having to create specific features and without complex search procedures for optimal parameters. Second, the method performs well, showing to be robust even in the presence of spikes in the data thus enabling better applications such as event management and detection of crowd gathering. © 2020 IEEE.
Author Keywords	ARIMA; deep learning; deep neural networks; forecast; time series

Similar Articles

Id	Similarity	Authors	Title	Published
15086	0.901	Sardinha I.; Rocha A.P.; Rossetti R.J.F.	Comparing Aggregated And Separate Models For Crowd Density Forecasting	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 14967 LNAI (2025)
21252	0.875	Kanchana R.; Fernandez F.M.H.	Dynamic Crowd Modeling And Anomalous Behavior Prediction Using Gmm And Time Series Analysis In Real-Time Smart City Environment	4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings (2025)
26830	0.874	Cai Z.; Jiang R.; Lian X.; Yang C.; Wang Z.; Fan Z.; Tsubouchi K.; Kobayashi H.H.; Song X.; Shibasaki R.	Forecasting Citywide Crowd Transition Process Via Convolutional Recurrent Neural Networks	IEEE Transactions on Mobile Computing, 23, 5 (2024)
25360	0.871	Samarajeewa C.; De Silva D.; Manic M.; Mills N.; Rathnayaka P.; Jennings A.	Explainable Artificial Intelligence For Crowd Forecasting Using Global Ensemble Echo State Networks	IEEE Open Journal of the Industrial Electronics Society, 5 (2024)
41869	0.865	Wang C.; Liang Y.; Tan G.	Periodic Residual Learning For Crowd Flow Forecasting	GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems (2022)
42750	0.858	Bellodi E.; Zese R.; Petrovich C.; Frascella A.; Bertasi F.	Predicting The Impact Of Public Events And Mobility In Smart Cities	IET Smart Cities, 6, 4 (2024)
38702	0.855	Papastefanopoulos V.; Linardatos P.; Panagiotakopoulos T.; Kotsiantis S.	Multivariate Time-Series Forecasting: A Review Of Deep Learning Methods In Internet Of Things Applications To Smart Cities	Smart Cities, 6, 5 (2023)
42777	0.854	Violos J.; Pelekis S.; Berdelis A.; Tsanakas S.; Tserpes K.; Varvarigou T.	Predicting Visitor Distribution For Large Events In Smart Cities	2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings (2019)
40092	0.852	Zeng Y.; Zhou S.; Xiang K.	Online-Offline Interactive Urban Crowd Flow Prediction Toward Iot-Based Smart City	IEEE Transactions on Services Computing, 15, 6 (2022)