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Article

Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches

by
Alket Cecaj
1,2,*,
Marco Lippi
1,2,
Marco Mamei
1,2 and
Franco Zambonelli
1,2
1
Dipartimento di Scienze e Metodi dell’Ingegneria, University of Modena and Reggio Emilia, 42122 Reggio Emilia, Italy
2
Artificial Intelligence Research and Innovation Center (AIRI), University of Modena and Reggio Emilia, 41125 Modena, Italy
*
Author to whom correspondence should be addressed.
IoT 2021, 2(1), 33-49; https://doi.org/10.3390/iot2010003
Submission received: 2 December 2020 / Revised: 1 January 2021 / Accepted: 5 January 2021 / Published: 8 January 2021
(This article belongs to the Special Issue Internet of Things Technologies for Smart Cities)
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Abstract

The possibility of sensing and predicting the movements of crowds in modern cities is of fundamental importance for improving urban planning, urban mobility, urban safety, and tourism activities. However, it also introduces several challenges at the level of sensing technologies and data analysis. The objective of this survey is to overview: (i) the many potential application areas of crowd sensing and prediction; (ii) the technologies that can be exploited to sense crowd along with their potentials and limitations; (iii) the data analysis techniques that can be effectively used to forecast crowd distribution. Finally, the article tries to identify open and promising research challenges.
Keywords: crowd; forecasting; sensing; crowd-forecasting; predicting-methods; approaches crowd; forecasting; sensing; crowd-forecasting; predicting-methods; approaches
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MDPI and ACS Style

Cecaj, A.; Lippi, M.; Mamei, M.; Zambonelli, F. Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches. IoT 2021, 2, 33-49. https://doi.org/10.3390/iot2010003

AMA Style

Cecaj A, Lippi M, Mamei M, Zambonelli F. Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches. IoT. 2021; 2(1):33-49. https://doi.org/10.3390/iot2010003

Chicago/Turabian Style

Cecaj, Alket, Marco Lippi, Marco Mamei, and Franco Zambonelli. 2021. "Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches" IoT 2, no. 1: 33-49. https://doi.org/10.3390/iot2010003

APA Style

Cecaj, A., Lippi, M., Mamei, M., & Zambonelli, F. (2021). Sensing and Forecasting Crowd Distribution in Smart Cities: Potentials and Approaches. IoT, 2(1), 33-49. https://doi.org/10.3390/iot2010003

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