Modelling Crowds in Urban Spaces

Rudomín, Isaac; Vargas-Solar, Genoveva; Espinosa-Oviedo, Javier A.; Pérez, Hugo; Zechinelli-Martini, José-Luis

doi:10.13053/cys-21-1-2584

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…The previous figure is a proposal of a roof slab with roof garden and Smart farming, whose area = 400 m 2 , which can hold up to 463 people, which in essence is the optimal point that was sought [12].…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Modeling a Roof Garden to Buildings in a Smart City using Equation Weight for Calculation of Distribution of Load Live and Weight Maximum on a Roof Top

Torres¹,

Ochoa-Zezzatti²,

Aguilar³

et al. 2019

RCS

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This research presents an intelligent model related with the modeling of a roof garden in buildings in the center of the country of Mexico and, in general, the buildings are located in Mexican national territory, taking in consideration the legislation in such delimitation. We analyze the behavior and features of a roof slab from the point of view of the constructive conception that was designed to complete the building and that does not have the specifications of a slab of mezzanine that from the beginning are established in the calculation memory of the construction. For that reason, it is important to determine the optimal parameters for the development of the roof garden and thus begin to transform the cities with the characteristics and conditions to be a smart city. The intent of the present research is to apply mathematical tools, computational as well as artificial intelligence software for roof garden modeling, based on a mathematical model that allows to integrate the dead load to live load, and the specific weights of the dry stratum and the wet stratum since the use of smart farming is incorporated in the roof top design.

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Section: Analysis Of Resultsmentioning

confidence: 99%

Modeling a Roof Garden to Buildings in a Smart City using Equation Weight for Calculation of Distribution of Load Live and Weight Maximum on a Roof Top

Torres¹,

Ochoa-Zezzatti²,

Aguilar³

et al. 2019

RCS

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“…For instance, adjacent crowded spots have strong interactions with each other, and a crowded spot remains crowded in consecutive time periods [38,39]. A great deal of research has formed a macroscopic description of urban crowd flows and their propagation in time and space based on crowd simulation [40] and traffic flow theories [41]. Particularly, the emerging multiple sources of data enable urban crowd correlation to be captured, mined, and analyzed in very fine spatial and temporal granularities (e.g., road segments, street blocks) [42,43].…”

Section: Related Workmentioning

confidence: 99%

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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“…However, there were few visualization systems proposed to communicate those information. A typical visualization approach is the heat map [3,8]. Other systems display crowd sizes at different locations using circles on a 2D map [1].…”

Section: Related Workmentioning

confidence: 99%

Pulse: Toward a Smart Campus by Communicating Real-time Wi-Fi Access Data

Wu¹,

Ku²,

Cheng³

et al. 2018

Preprint

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Figure 1: The Pulse interface communicates the campus crowd information to the lay public through a projection screen: (A) The Map View provides a holistic view of the crowd distribution on the satellite map (A 2 ), with a pop-up window (A 1 ) displaying the derived information of campus facilities in a loop; (B) The Chart View presents the statistical information including the crowd amount ranking of functional zones (B 1 ), the historical and predicted number (B 2 ), the movement of crowd among functional zones (B 3 ), and the buildings with top incoming and outgoing crowds (B 4 ).

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Modelling Crowds in Urban Spaces

Cited by 4 publications

References 11 publications

Modeling a Roof Garden to Buildings in a Smart City using Equation Weight for Calculation of Distribution of Load Live and Weight Maximum on a Roof Top

Modeling a Roof Garden to Buildings in a Smart City using Equation Weight for Calculation of Distribution of Load Live and Weight Maximum on a Roof Top

Modeling Spatio-Temporal Evolution of Urban Crowd Flows

Pulse: Toward a Smart Campus by Communicating Real-time Wi-Fi Access Data

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