Machine Learning Based Crowd Behaviour Analysis and Prediction

2021 ◽  
Vol 7 (1) ◽  
pp. 24-34
Author(s):  
Neha Srivastava ◽  
Mohammad Zunnun Khan
Keyword(s):  
Machine Learning ◽  
Behaviour Analysis ◽  
Crowd Behaviour

scholarly journals Crowd surveillance: The (in)securitization of the urban body

2018 ◽  
Vol 49 (3) ◽  
pp. 200-216 ◽  
Author(s):  
Hidefumi Nishiyama
Keyword(s):  
Urban Space ◽  
Border Control ◽  
Unique Form ◽  
Social Body ◽  
Behaviour Analysis ◽  
The Social ◽  
Crowd Behaviour ◽  
The Individual ◽  
Crowd Surveillance ◽  
Individual Body

The recent proliferation of the securitization of crowded places has led to a growth in the development of technologies of crowd behaviour analysis. However, despite the emerging prominence of crowd surveillance in emergency planning, its impacts on our understanding of security and surveillance have received little discussion. Using the case of crowd surveillance in Tokyo, this article examines the ways in which crowds are simulated, monitored and secured through the technology of crowd behaviour analysis, and discusses the implications on the politics of security. It argues that crowd surveillance constitutes a unique form of the biopolitics of security that targets not the individual body or the social body of population, but the urban body of crowd. The power of normalization in crowd surveillance operates in a preemptive manner through the codification of crowd behaviour that is spatially and temporarily specific. The article also interrogates the introduction of crowd surveillance in relation to racialized logics of suspicion and argues that, despite its appearance as non-discriminatory and ‘a-racial’, crowd surveillance entails the racial coding of crowd behaviour and urban space. The article concludes with the introduction of crowd surveillance as a border control technology, which reorients existing modalities of (in)securitization at airports.

scholarly journals Revisiting crowd behaviour analysis through deep learning: Taxonomy, anomaly detection, crowd emotions, datasets, opportunities and prospects

2020 ◽  
Vol 64 ◽  
pp. 318-335 ◽  
Author(s):  
Francisco Luque Sánchez ◽  
Isabelle Hupont ◽  
Siham Tabik ◽  
Francisco Herrera
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Behaviour Analysis ◽  
Crowd Behaviour

scholarly journals Generative Modelling and Machine Learning Methods for Travel Behaviour Analysis

2021 ◽  
Author(s):  
Melvin Wong
Keyword(s):  
Machine Learning ◽  
Discrete Choice ◽  
Learning Process ◽  
Travel Demand ◽  
Travel Behaviour ◽  
Generative Model ◽  
Choice Modelling ◽  
Behaviour Analysis ◽  
Discrete Choice Modelling ◽  
Generative Modelling

The dissertation outlines novel analytical and experimental methods for discrete choice modelling using generative modelling and information theory. It explores the influence of information heterogeneity on large scale datasets using generative modelling. The behaviorally subjective psychometric indicators are replaced with a learning process in an artificial neural network architecture. Part of the dissertation establishes new tools and techniques to model aspects of travel demand and behavioural analysis for the emerging transport and mobility markets. Specically, we consider: (i) What are the strengths, weaknesses and role of generative learning algorithms for behaviour analysis in travel demand modelling? (ii) How to monitor and analyze the identiability and validity of the generative model using Bayesian inference methods? (iii) How to ensure that the methodology is behaviourally consistent? (iv) What is the relationship between the generative learning process and realistic representation of decision making as well as its usefulness in choice modelling? and (v) What are the limitations and assumptions that have needed to develop the generative model systems? This thesis is based on four articles introduced in Chapters 3 to 6. Chapters 3 and 4 introduces a restricted Boltzmann machine learning algorithm for travel behaviour that includes an analysis of modelling discrete choice with and without psychometric indicators. Chapter 5 provides an analysis of information heterogeneity from the perspective of a generative model and how it can extract population taste variation using a Bayesian inference based learning process. One of the most promising applications for generative modelling is for modelling the multiple discretecontinuous data. In Chapter 6, a generative modelling framework is developed to show the process and methodology of capturing higher-order correlation in the data and deriving a process of sampling that can account for the interdependencies between multiple outputs and inputs. A brief background on machine learning principles for discrete choice modelling and newly developed mathematical models and equations related to generative modelling for travel behaviour analysis are provided in the appendices.

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