Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser, M. Shamim; Lwin, Khin T.; Mahmud, Mufti; Hajializadeh, Donya; Chaipimonplin, Tawee; Sarhan, A. E.; Hossain, M. A.

doi:10.1109/tits.2017.2771746

Cited by 93 publications

(49 citation statements)

References 101 publications

(181 reference statements)

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“…Analyzing the spatio-temporal distribution of urban crowd flows is a long-standing research focus. In metropolitan cities, crowd flows are influenced by the complex land uses and frequent mass gatherings, so it is more likely to form a crowded hotspot in a limited range of space and time [13]. Several studies have investigated this phenomenon by counting instant population via camera videos [14], telecommunications [15], social media footprints [16], and other ubiquitous sensing techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Two cells I t x,y and I t x ,y are further defined as reachable crowds if there exists a path I t x 1 ,y 1 , · · · , I t x n ,y n with I t x 1 ,y 1 = I t x,y and I t x n ,y n = I t x ,y , where each cell I t x i+1 ,y i+1 is directly reachable from cell I t x i ,y i according to Equations (12) and (13). Note that the so-called "directly reachable" defined here is fundamentally different from the density reachability defined in DBSCAN [55].…”

Section: Connected Componentmentioning

confidence: 99%

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Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Qin

Kang

et al. 2019

IJGI

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Metropolitan cities are facing many socio-economic problems (e.g., frequent traffic congestion, unexpected emergency events, and even human-made disasters) related to urban crowd flows, which can be described in terms of the gathering process of a flock of moving objects (e.g., vehicles, pedestrians) towards specific destinations during a given time period via different travel routes. Understanding the spatio-temporal characteristics of urban crowd flows is therefore of critical importance to traffic management and public safety, yet it is very challenging as it is affected by many complex factors, including spatial dependencies, temporal dependencies, and environmental conditions. In this research, we propose a novel matrix-computation-based method for modeling the morphological evolutionary patterns of urban crowd flows. The proposed methodology consists of four connected steps: (1) defining urban crowd levels, (2) deriving urban crowd regions, (3) quantifying their morphological changes, and (4) delineating the morphological evolution patterns. The proposed methodology integrates urban crowd visualization, identification, and correlation into a unified and efficient analytical framework. We validated the proposed methodology under both synthetic and real-world data scenarios using taxi mobility data in Wuhan, China as an example. Results confirm that the proposed methodology can enable city planners, municipal managers, and other stakeholders to identify and understand the gathering process of urban crowd flows in an informative and intuitive manner. Limitations and further directions with regard to data representativeness, data sparseness, pattern sensitivity, and spatial constraint are also discussed.

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Section: Related Workmentioning

confidence: 99%

Section: Connected Componentmentioning

confidence: 99%

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Qin

Kang

et al. 2019

IJGI

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“…The details such as the number of vehicles, position of the vehicle, number of people (a.k.a crowd), speed, and roadway play a crucial role in ITS decision making to improve smartness. Acquisition of these data could be historical or real-time and can be in the form of text, images and videos [4]. In short, the data is heterogeneous in nature.…”

Section: Data Collection and Creationmentioning

confidence: 99%

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

2019

IJRTE

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Intelligent Transportation System (ITS) has become the popular area of research since the last decade. Knowing the crowd density in every region of the city is of high importance for an ITS in delivering adequate transport facility to the public. Further, the occurrence of social events draw public crowd occasionally, in any specific region of the city. Thus, the combination of both the identification of crowd density and the social event detection lays a path to an interesting research in ITS. Having said, this paper proposes a methodology for the effective design of ITS with a primary focus on crowd sensing. The paper also presents a taxonomy of methods used to gather crowd density information through various sources. Furthermore, the research works that focused on event detection and crowd analysis are studied. Finally, the open challenges are identified and outlined which are promising research directions for ITS.

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“…Indeed, we aim to infer the user participation to not only the event before, but also after the event takes place. The early knowledge of the possible user attendance can be extremely useful for enabling innovative services and applications in the fields of transportation planning and crowd safety management (KAISER et al, 2017).…”

Section: Motivationmentioning

confidence: 99%

Mining Human Mobility Data and Social Media for Smart Ride Sharing

Lira

2019

2019 20th IEEE International Conference on Mobile Data Management (MDM)

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I would like to express my deep gratitude to Dr. Chiara Renso, Professor Valeria Times, and Professor Rossano Venturini, my research supervisors, for their patient guidance, enthusiastic encouragement and useful critiques of this research work. Their guidance helped me constantly during the time spent in research and writing this thesis. Without doubt, I had the luck to have a really great team of supervisors. I would also like to thank Dr. Raffaele Perego, for his valuable advice and the opportunity to undertake my Ph.D. research within the HPC lab of ISTI-CNR Pisa. It was fantastic to have the opportunity to work in his facilities. My grateful thanks are also extended to Professor Iadh Ounis and Professor Craig McDonald, from the University of Glasgow, for their collaboration in building the predictive models for user attendance using Social Media, and to Dr. Salvo Rinzivillo, from the KDD Lab ISTI-CNR Pisa, who helped me elaborate our novel ride-sharing matching algorithm. I would also like to extend my thanks to my colleagues of the HPC Lab for the fun and support. The years spent at the HPC Lab were anything but sharing deadlines, hard work, events and travels with such special people. Certainly, all these experiences shared with them made this achievement less hard to be earned. In particular, I would like to individually thank Dr. Patrizio Dazzi, Dr. Massimo Coppola and Dr. Emanuele Carlini for the opportunity to participate in the BASMATI Project developing my skills as researcher. Moreover, thanks to the external reviewers, Professor Stan Matwin and Professor Yannis Theodoridis, for taking the effort to contribute to this work by reviewing it: I am really honored of having their endorsement. I am also grateful to the UNIPI committee members Professor Anna Monreale and Professor Roberto Grossi for their priceless comments and suggestions to improve my Ph.D. research. I am especially grateful to my parents for their unconditional love and support in my life. I owe them all I am. I thank as well to all family, who supported me along the completion of the Ph.D. program. And finally, last but by no means least, I thank all my friends that somehow contributed to my growth during this period.

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Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Cited by 93 publications

References 101 publications

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Intelligent Transportation Cyber-Physical System: On Crowd through Event Detection for Public Urban Transportation

Mining Human Mobility Data and Social Media for Smart Ride Sharing

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