Crowd characterization for crowd management using social media data in city events

Gong, Vincent X.; Daamen, Winnie; Bozzon, Alessandro; Hoogendoorn, Serge P.

doi:10.1016/j.tbs.2020.03.011

Cited by 22 publications

(17 citation statements)

References 40 publications

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“…Moreover, the bias in social media usage in terms of users' age and gender may also affect crowd size estimation using social media. For instance, knowing that social media is more popular in younger generation (Yang et al 2016;Gong et al 2018a), city events with less younger participants may generate less social media images, which may not sufficient for training and improving the crowd size estimation methods, and also not sufficient for methods to estimate the crowd size during events. Therefore, it may affect crowd size estimation for crowd management.…”

Section: Discussionmentioning

confidence: 99%

“…To collect these social media images, we first select a set of events and activities during these events. Then, social media images taken during these events and activities are collected from Instagram, the most popular image based social network (Yang et al 2016;Gong et al 2018a). After collecting the data, we use these social media images to derive the ground truth (annotated dataset).…”

Section: Data Collection and Annotationmentioning

confidence: 99%

“…Instagram, the image based social media network, is widely used by people to share pictures (Yang et al 2016;Gong et al 2018a). Therefore, we collect images from Instagram to construct the social media dataset.…”

Section: Social Media Data Collectionmentioning

confidence: 99%

“…A higher level-of-service of the event area indicates lower density of people in that area, which is safer than the lower level-of-service that refers to high density of people. Using such information together with a set of other qualitative and quantitative interpretation of the crowd, such as sentiment (Gong et al 2019) and composition (Gong et al 2018a), stakeholders apply predefined measures to manage the crowd. The level-ofservice information can be inferred from density of people in that area, which can be further calculated using the number of people in the area and the area of the event place.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Counting people in the crowd using social media images for crowd management in city events

et al. 2021

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City events are getting popular and are attracting a large number of people. This increase needs for methods and tools to provide stakeholders with crowd size information for crowd management purposes. Previous works proposed a large number of methods to count the crowd using different data in various contexts, but no methods proposed using social media images in city events and no datasets exist to evaluate the effectiveness of these methods. In this study we investigate how social media images can be used to estimate the crowd size in city events. We construct a social media dataset, compare the effectiveness of face recognition, object recognition, and cascaded methods for crowd size estimation, and investigate the impact of image characteristics on the performance of selected methods. Results show that object recognition based methods, reach the highest accuracy in estimating the crowd size using social media images in city events. We also found that face recognition and object recognition methods are more suitable to estimate the crowd size for social media images which are taken in parallel view, with selfies covering people in full face and in which the persons in the background have the same distance to the camera. However, cascaded methods are more suitable for images taken from top view with gatherings distributed in gradient. The created social media dataset is essential for selecting image characteristics and evaluating the accuracy of people counting methods in an urban event context.

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Section: Discussionmentioning

confidence: 99%

Section: Data Collection and Annotationmentioning

confidence: 99%

Section: Social Media Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Counting people in the crowd using social media images for crowd management in city events

et al. 2021

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“…Digitalization is an important concept unique to Chinese, and comes up naturally under the influence of the terms such as electronation, informatization, computerization and networking. By collecting, analyzing, and integrating various social energy flows (ordered and disordered) [20], numerous individual-led social behavior big data, choice preference big data, and traffic information big data, etc., it is possible to see the relationship between urban social populations and changes in urban functional layout [21,22]. This is also an important scientific basis for large-scale urban design we do in cities today.…”

Section: Paradigm Of the Fourth Generation Urban Design: Human-computer Interactionmentioning

confidence: 99%

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Wang

2021

Front. Struct. Civ. Eng.

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The wave of “digital age” featuring digital information is coming. Digital technology is profoundly changing the societal development direction and evolution paths. It also has significant bearing on production modes, social interactions and lifestyles. With regard to urban design, a system of knowledge about the creation and adaptation of material space forms that integrate humanities, art, technology and materials, digital technology has provided it with a brand-new and revolutionary scientific impetus for its evolution. The result of this evolution is “digital urban design paradigm based on human-computer interaction”, i.e., the urban development is moving toward “pan-dimensionality” and “individual ubiquity”. The future of urban design will construct a new approach to urban research and engineering, which is more complex, capable of accommodating and compatible with multiple goals of “instrumental rationality” and “value rationality”. Such a new approach shall be led by the probabilistic theory of “gray scale thinking”, reflecting quaternary synergetic view of “scientific rationality, ecological rationality, cultural rationality and technical rationality” to realize the cognitive progress of “engineering for the benefit of mankind”.

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Research on the Behavior Pattern of Microblog “Tree Hole” Users with Their Temporal Characteristics

Jing

Lin

Huang

2020

Health Information Science

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Crowd characterization for crowd management using social media data in city events

Cited by 22 publications

References 40 publications

Counting people in the crowd using social media images for crowd management in city events

Counting people in the crowd using social media images for crowd management in city events

Development trend of urban design in “digital age”: Pan-dimensionality and individual-ubiquity

Research on the Behavior Pattern of Microblog “Tree Hole” Users with Their Temporal Characteristics

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