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

Cecaj, Alket; Lippi, Marco; Mamei, Marco; Zambonelli, Franco

doi:10.3390/iot2010003

Cited by 18 publications

(8 citation statements)

References 104 publications

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“…We illustrate the performance of CrowdNet on an irregular tessellation defined by an administrative tessellation defined by 29 neighbourhoods in Manhattan. Data of the administrative tiles are taken from an official tool of the municipality of New York City 3 .…”

Section: Figure 3b Makes the Comparison For A Larger Time Interval (6...mentioning

confidence: 99%

“…Existing solutions to crowd flow prediction are not very meaningful to policymakers: as predictions are produced in form of a single value per location representing the aggregated inflow or outflow, no information is provided about the origin and the destination of these flows. However, this networked information is crucial to understand the density of movements throughout the city and its evolution in time, manage public events and emergency situations, maintain an efficient public transport system, and forecast the direction in which a viral disease may spread out [3].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing crowd flow prediction in various spatial and temporal granularities

Cardia¹,

Luca²,

Pappalardo³

2022

Preprint

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Thanks to the diffusion of the Internet of Things, nowadays it is possible to sense human mobility almost in real time using unconventional methods (e.g., number of bikes in a bike station). Due to the diffusion of such technologies, the last years have witnessed a significant growth of human mobility studies, motivated by their importance in a wide range of applications, from traffic management to public security and computational epidemiology. A mobility task that is becoming prominent is crowd flow prediction, i.e., forecasting aggregated incoming and outgoing flows in the locations of a geographic region. Although several deep learning approaches have been proposed to solve this problem, their usage is limited to specific types of spatial tessellations and cannot provide sufficient explanations of their predictions. We propose CrowdNet, a solution to crowd flow prediction based on graph convolutional networks. Compared with state-of-the-art solutions, CrowdNet can be used with regions of irregular shapes and provide meaningful explanations of the predicted crowd flows. We conduct experiments on public data varying the spatio-temporal granularity of crowd flows to show the superiority of our model with respect to existing methods, and we investigate CrowdNet's reliability to missing or noisy input data. Our model is a step forward in the design of reliable deep learning models to predict and explain human displacements in urban environments.

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Section: Figure 3b Makes the Comparison For A Larger Time Interval (6...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing crowd flow prediction in various spatial and temporal granularities

Cardia¹,

Luca²,

Pappalardo³

2022

Preprint

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show abstract

“…Estimating and predicting (in space and/or time) some features of a human "crowd" in indoor and outdoor locations is an active research topic, with many applications, including surveillance and security, situation awareness, emergencies, and crowd management [55]. In transport applications, the feature of interest is the number of components of the crowd and/or its density.…”

Section: A Crowd Predictionmentioning

confidence: 99%

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Darsena¹,

Gelli²,

Iudice³

et al. 2022

Preprint

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<p>Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with COVID-19 limitations. This paper presents a taxonomy and review of sensing technologies based on Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICT) in order to: (i) monitor and predict crowding events; (ii) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs); (iii) inform in real-time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus stops/stations, and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as, e.g., on-line ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning.</p>

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“…Its challenge is to predict in a geographic region the incoming and outgoing flows [50]. It may help mitigate or prevent massive crowds of people streaming into a strip area and other dangerous situations [51,52]. However, CFP presents a problem because it deals with spatio-temporal dependencies.…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

2021

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The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches.

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

Cited by 18 publications

References 104 publications

Enhancing crowd flow prediction in various spatial and temporal granularities

Enhancing crowd flow prediction in various spatial and temporal granularities

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility

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