Modeling pedestrian mobility in disaster areas

Solmaz, Gürkan; Turgut, Damla

doi:10.1016/j.pmcj.2017.05.005

Cited by 23 publications

(4 citation statements)

References 42 publications

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“…For a link-based metric, those mentioned by Solmaz et al [16] such as node density and intercontact time were difficult to apply in our dataset. We then considered Origin-Destination (OD) matrices, which have been commonly used in literature for analyzing flows between locations.…”

Section: Existing Mobility Metricsmentioning

confidence: 94%

“…GPS datasets may also provide an avenue for modelling human mobility if they are large enough. Alessandretti et al [14] presented an extensive characterization of the statistical properties of GPS trajectories using a dataset collected from around 850 people lasting around 25 months, while Solmaz et al [16] used GPS traces to model and simulate pedestrian mobility in disaster areas.…”

Section: A Work Using Gps Datamentioning

confidence: 99%

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Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

et al. 2022

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The proliferation of smartphones has accelerated mobility studies by largely increasing the type and volume of mobility data available. One such source of mobility data is from GPS technology, which is becoming increasingly common and helps the research community understand mobility patterns of people. However, there lacks a standardized framework for studying the different mobility patterns created by the non-Work, non-Home locations of Working and Nonworking users on Workdays and Offdays using machine learning methods. We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features. We then use those features with an unsupervised machine learning method, k-means clustering, and obtain three clusters of users for each type of day (Workday and Offday). Finally, we propose two new metrics for the analysis of the clustering results, namely User Commonality and Average Frequency. By using the proposed metrics, interesting user behaviors can be discerned and it helps us to better understand the mobility patterns of the users.

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Section: Existing Mobility Metricsmentioning

confidence: 94%

Section: A Work Using Gps Datamentioning

confidence: 99%

Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

et al. 2022

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“…This series of studies examined mobility as a source of improving potential urban practices, and a majority of them have discussed equity issues in their research contexts. For instance, human mobility data serve as the key input in understanding evacuation movements after natural disasters (Solmaz & Turgut, 2017), post-disaster recovery in cities (Yabe et al, 2020), and vulnerability across different social groups (Martin et al, 2020). Similarly, a series of studies have been conducted in the context of Covid-19 to uncover the heterogeneous adaptions and responses of various social atoms and groups (Loo et al, 2021;Pan & He, 2022;Zhou et al, 2021), and contribute to more efficient disease containment strategies and more inclusive policies.…”

Section: Big Data As Equity Lensmentioning

confidence: 99%

Revealing social dimensions of urban mobility with big data: A timely dialogue

Wu,

Zhou

2023

JTLU

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Considered a total social phenomenon, mobility is at the center of intricate social dynamics in cities and serves as a reading lens to understand the whole society. With the advent of big data, the potential for using mobility as a key social analyzer was unleashed in the past decade. The purpose of this research is to systematically review the evolution of big data's role in revealing social dimensions of urban mobility and discuss how they have contributed to various research domains from early 2010s to now. Six major research topics are detected from the selected online academic corpuses by conducting keywords-driven topic modeling techniques, reflecting diverse research interests in networked mobilities, human dynamics in spaces, event modeling, spatial underpinnings, travel behaviors and mobility patterns, and sociodemographic heterogeneity. The six topics reveal a comprehensive, research-interests, evolution pattern, and present current trends on using big data to uncover social dimensions of human mobility activities. Given these observations, we contend that big data has two contributions to revealing social dimensions of urban mobility: as an efficiency advancement and as an equity lens. Furthermore, the possible limitations and potential opportunities of big data applications in the existing scholarship are discussed. The review is intended to serve as a timely retrospective of societal-focused mobility studies, as well as a starting point for various stakeholders to collectively contribute to a desirable future in terms of mobility.

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“…At the same time, the mass diffusion and availability of online social networks (OSNs) has appointed them as the preferred supplier of information and communication services, especially during fast-paced, unfolding events that impose stringent time requirements [4]. Crises and disasters are among such events and, indeed, many people involved in disasters publish informationrich textual and multimedia messages in OSNs, such as Facebook and Twitter, often live and in situ [5,6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

Avvenuti,

Bellomo,

Cresci

et al. 2019

Preprint

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People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7×) and the variety (up to 18×) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity.

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Modeling pedestrian mobility in disaster areas

Cited by 23 publications

References 42 publications

Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

Clustering and Analysis of GPS Trajectory Data Using Distance-Based Features

Revealing social dimensions of urban mobility with big data: A timely dialogue

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

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