Using space syntax and agent-based approaches for modeling pedestrian volume at the urban scale

Omer, Itzhak; Kaplan, Nir

doi:10.1016/j.compenvurbsys.2017.01.007

Cited by 46 publications

(47 citation statements)

References 30 publications

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“…Many researchers have noted that the indicators extracted from space syntax have important effects on urban vibrancy [16,[41][42][43][44] and are able to promote urban vibrancy and improve living environments. By physically and functionally representing pedestrian connections and accessibility [45], space syntax also contributes greatly to the design of urban streets and neighbourhoods; thus, space syntax also has profound effects on urban vibrancy and intra-city migration [46,47].…”

Section: System Of Influencing Factorsmentioning

confidence: 99%

Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

Lingjun

Lin

et al. 2018

Sustainability

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Urban vibrancy is an important indicator of the attractiveness of a city and its potential for comprehensive, healthy and sustainable development in all aspects. With the development of big data, an increasing number of datasets can be used to analyse urban vibrancy on fine spatial and temporal scales from the perspective of human perception. In this study, we applied mobile phone data as a proxy for local vibrancy in Shenzhen and constructed a comprehensive framework for the factors that influence urban vibrancy, especially in terms of urban morphology and space syntax. In addition, the popular geographically and temporally weighted regression (GTWR) method was used to explore the spatiotemporal relationships between vibrancy and its influencing factors. The spatial and temporal coefficients are presented through maps. The conclusions of this attempt to study urban vibrancy with urban big data have significant implications for helping urban planners and policy makers optimize the spatial layouts of urban functional zones and perform high-quality city planning.

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Section: System Of Influencing Factorsmentioning

confidence: 99%

Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

Lingjun

Lin

et al. 2018

Sustainability

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“…Given the growing city sizes and spatial and temporal scarcity of human mobility data, pedestrian volumes have been estimated by simulations, often using agent-based models (ABM) [53]. Given the well-known challenges with calibrating and validating ABMs [40,53,54], deriving pedestrian densities from webcams could replace labor-intensive manual survey methods, especially in open spaces, such as plazas, where "gate count" methods [55] would be ineffective.…”

Section: Discussionmentioning

confidence: 99%

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

Petrášová

Hipp

Mitášová

2019

IJGI

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Accurate information on the number and distribution of pedestrians in space and time helps urban planners maintain current city infrastructure and design better public spaces for local residents and visitors. Previous studies have demonstrated that using webcams together with crowdsourcing platforms to locate pedestrians in the captured images is a promising technique for analyzing pedestrian activity. However, it is challenging to efficiently transform the time series of pedestrian locations in the images to information suitable for geospatial analytics, as well as visualize data in a meaningful way to inform urban design or decision making. In this study, we propose to use a space-time cube (STC) representation of pedestrian data to analyze the spatio-temporal patterns of pedestrians in public spaces. We take advantage of AMOS (The Archive of Many Outdoor Scenes), a large database of images captured by thousands of publicly available, outdoor webcams. We developed a method to obtain georeferenced spatio-temporal data from webcams and to transform them into high-resolution continuous representation of pedestrian densities by combining bivariate kernel density estimation with trivariate, spatio-temporal spline interpolation. We demonstrate our method on two case studies analyzing pedestrian activity of two city plazas. The first case study explores daily and weekly spatio-temporal patterns of pedestrian activity while the second one highlights the differences in pattern before and after plaza's redevelopment. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time series of pedestrian occurrences captured by webcam images. and privacy issues they raise. Labor-intensive, manual observation methods are often replaced by automated data collection using a variety of technological approaches, such as counting gates, GPS receivers and accelerometers in smart phones [5,6] and more recently also using call detail records (CDR) [7,8] and WiFi probe request data [9]. The challenges of asking people to actively wear the different sensors and privacy issues associated with telecommunication data, together with data and participant inaccessibility for research has led researchers to take advantage of crowdsourced, often publicly available, big data coming from social media networks, such as Twitter, Flickr, and Instagram [10]. Geolocated tweets and images have been used recently to study, e.g., urban parks visitation and access [11,12] and to gain insight about the paths of tourists through cities [13,14]. Strava-a network for tracking athletic activity-provides even more geospatially rich data which has been used to investigate cycling behavior [15], cycling infrastructure [16], and air pollution exposure of commuting cyclists [17].One of the increasingly popular methods for public space monitoring uses closed-circuit-television (CCTV) footage and webcams to capture and interpret images for a variety of purposes including se...

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“…Equally, characteristics of the urban networks in responding to changing demand can also be modeled as well as disruptions impacting individual agent paths and travel times. ABM also compares well with statistical prediction techniques for pedestrian flows that have gained popularity in recent years, such as multiple regression analysis (MRA) [90]. This type of analysis relies on known parameters such as length of pedestrian routes and visual connectivity between points to estimate e.g.…”

Section: Agent-based Modeling 41 Advantages and Scopementioning

confidence: 95%

Understanding Urban Mobility and Pedestrian Movement

Bezbradica¹,

Ruskin²

2020

Smart Urban Development

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Urban environments continue to expand and mutate, both in terms of size of urban area and number of people commuting daily as well as the number of options for personal mobility. City layouts and infrastructure also change constantly, subject to both short-term and long-term imperatives. Transportation networks have attracted particular attention in recent years, due to efforts to incorporate "green" options, enabling positive lifestyle choices such as walking or cycling commutes. In this chapter we explore the pedestrian viewpoint, aids to familiarity with and ease of navigation in the urban environment, and the impact of novel modes of individual transport (as options such as smart urban bicycles and electric scooters increasingly become the norm). We discuss principal factors influencing rapid transit to daily and leisure destinations, such as schools, offices, parks, and entertainment venues, but also those which facilitate rapid evacuation and movement of large crowds from these locations, characterized by high occupation density or throughput. The focus of the chapter is on understanding and representing pedestrian behavior through the agent-based modeling paradigm, allowing both large numbers of individual actions with active awareness of the environment to be simulated and pedestrian group movements to be modeled on real urban networks, together with congestion and evacuation pattern visualization.

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Using space syntax and agent-based approaches for modeling pedestrian volume at the urban scale

Cited by 46 publications

References 30 publications

Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

Exploring the Influence of Urban Form on Urban Vibrancy in Shenzhen Based on Mobile Phone Data

Visualization of Pedestrian Density Dynamics Using Data Extracted from Public Webcams

Understanding Urban Mobility and Pedestrian Movement

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