Elementary modelling and behavioural analysis for emergency evacuations using social media

Fry, John; Binner, Jane M.

doi:10.1016/j.ejor.2015.05.049

Cited by 35 publications

(11 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By using Markov models to characterise the dynamics and patterns of patient adherence behaviour, the authors develop a basis for designing cost-effective interventions. Fry and Binner (2015) provide insights into the increasingly important domain of managing evacuations and emergency situations, where the use of models is rapidly expanding. Specifically, the authors study several ways to measure the usefulness of information from social media in the context of emergency evacuations.…”

Section: Introduction To the Articles In The Special Issuementioning

confidence: 99%

Behavioural operational research: Returning to the roots of the OR profession

Franco

Hämäläinen

2016

European Journal of Operational Research

View full text Add to dashboard Cite

Section: Introduction To the Articles In The Special Issuementioning

confidence: 99%

Behavioural operational research: Returning to the roots of the OR profession

Franco

Hämäläinen

2016

European Journal of Operational Research

View full text Add to dashboard Cite

“…Kryvasheyeu et al (2015) proposed that social media users can be considered as early-warning sensors in detecting and locating disasters. Studies have also explored social media data to understand evacuation behavior (Fry & Binner, 2015;Martín, Li, & Cutter, 2017) and damage assessment (Deng, Liu, Zhang, Deng, & Ma, 2016;Guan & Chen, 2014;Kryvasheyeu et al, 2016;Yuan & Liu, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data

Roy

Hasan

Mozumder

2020

Computer aided Civil Eng

View full text Add to dashboard Cite

Rapid identification of infrastructure disruptions during a disaster plays an important role in restoration and recovery operations. Due to the limitations of using physical sensing technologies, such as the requirement to cover a large area in a short period of time, studies have investigated the potential of social sensing for damage/disruption assessment following a disaster. However, previous studies focused on identifying whether a social media post is damage related or not. Hence, advanced methods are needed to infer actual infrastructure disruptions and their locations from such data. In this paper, we present a multilabel classification approach to identify the cooccurrence of multiple types of infrastructure disruptions considering the sentiment toward a disruption-whether a post is reporting an actual disruption (negative), or a disruption in general (neutral), or not affected by a disruption (positive). In addition, we propose a dynamic mapping framework for visualizing infrastructure disruptions. We use a geo-parsing method that extracts location from the texts of a social media post. We test the proposed approach using Twitter data collected during hurricanes Irma and Michael. The proposed multilabel classification approach performs better than a baseline method (using simple keyword search and sentiment analysis). We also find that disruption-related tweets, based on specific keywords, do not necessarily indicate an actual disruption. Many tweets represent general conversations, concerns about a potential disruption, and positive emotion for not being affected by any disruption. In addition, a dynamic disruption map has potential in showing county and point/coordinate level disruptions. Identifying disruption types and their locations is vital for disaster recovery, response, and relief actions. By inferring the co-occurrence of multiple disruptions, the proposed approach may help coordinate among infrastructure service providers and disaster management organizations. 1 INTRODUCTION Cities and communities all over the world largely depend on critical infrastructure systems/services such as electrical power, water distribution, communication services, and transportation networks. The growing interconnectedness and © 2020 Computer-Aided Civil and Infrastructure Engineering interdependency among these systems have changed the organizational and operational factors and increased the vulnerability in the face of unwanted disruptions. These systems provide critical services to a large population, and thus when disrupted they affect our quality of life, local and regional economy, and the overall community wellbeing. The need to

show abstract

“…Sorensen and Mileti (1988) define three main sources through which warning information are disseminated: official channels (e.g., police officers), informal channels (e.g., friends, relatives), and media (e.g., television), where different warning dissemination channels affect the response to a warning signal (Sorensen 1991 Safety Check tool allows people to communicate their status (safe or not) if they are in a disasteraffected area. Fry and Binner (2016) address the role of social media in supporting emergency evacuation operations through a means of both mathematical modelling and Behavioural OR (BOR).…”

Section: Evacuee Behaviourmentioning

confidence: 99%