2,020 research outputs found
Human Crowds Estimation based on Mobile Sensing
University of Tokyo(東京大学
Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)
This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio
empathi: An ontology for Emergency Managing and Planning about Hazard Crisis
In the domain of emergency management during hazard crises, having sufficient
situational awareness information is critical. It requires capturing and
integrating information from sources such as satellite images, local sensors
and social media content generated by local people. A bold obstacle to
capturing, representing and integrating such heterogeneous and diverse
information is lack of a proper ontology which properly conceptualizes this
domain, aggregates and unifies datasets. Thus, in this paper, we introduce
empathi ontology which conceptualizes the core concepts concerning with the
domain of emergency managing and planning of hazard crises. Although empathi
has a coarse-grained view, it considers the necessary concepts and relations
being essential in this domain. This ontology is available at
https://w3id.org/empathi/
Intelligent evacuation management systems: A review
Crowd and evacuation management have been active areas of research and study in the recent past. Various developments continue to take place in the process of efficient evacuation of crowds in mass gatherings. This article is intended to provide a review of intelligent evacuation management systems covering the aspects of crowd monitoring, crowd disaster prediction, evacuation modelling, and evacuation path guidelines. Soft computing approaches play a vital role in the design and deployment of intelligent evacuation applications pertaining to crowd control management. While the review deals with video and nonvideo based aspects of crowd monitoring and crowd disaster prediction, evacuation techniques are reviewed via the theme of soft computing, along with a brief review on the evacuation navigation path. We believe that this review will assist researchers in developing reliable automated evacuation systems that will help in ensuring the safety of the evacuees especially during emergency evacuation scenarios
Autonomous Accident Monitoring Using Cellular Network Data
Mobile communication networks constitute large-scale sensor networks that generate huge amounts of data that can be refined into collective mobility patterns. In this paper we propose a method for using these patterns to autonomously monitor and detect accidents and other critical events. The approach is to identify a measure that is approximately time-invariant on short time-scales under regular conditions, estimate the short and long-term dynamics of this measure using Bayesian inference, and identify sudden shifts in mobility patterns by monitoring the divergence between the short and long-term estimates. By estimating long-term dynamics, the method is also able to adapt to long-term trends in data. As a proof-of-concept, we apply this approach in a vehicular traffic scenario, where we demonstrate that the method can detect traffic accidents and distinguish these from regular events, such as traffic congestions
Advanced technologies for offering situational intelligence in flood warning and response systems : a literature review
Deaths and property damage from floods have increased drastically in the past two 9
decades due to various reasons such as increased population, unplanned development and climate 10
change. Such losses from floods can be reduced by issuing timely early warnings and through 11
effective response mechanisms, based on situational intelligence during emerging flood situations. 12
This paper presents the outcome of a literature review that was conducted to identify the types and 13
sources of intelligence required for flood warning and response processes as well as the technology 14
solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence 15
are presented, together with the technologies that can be used to extract such intelligence. 16
Furthermore, a conceptual architecture, that illustrates how relevant technology solutions can be 17
used to extract intelligence at various stages of a flood cycle for decision-making for issuing early 18
warnings and planning responses, is presented
Supporting Post-disaster Recovery with Agent-based Modeling in Multilayer Socio-physical Networks
The examination of post-disaster recovery (PDR) in a socio-physical system
enables us to elucidate the complex relationships between humans and
infrastructures. Although existing studies have identified many patterns in the
PDR process, they fall short of describing how individual recoveries contribute
to the overall recovery of the system. To enhance the understanding of
individual return behavior and the recovery of point-of-interests (POIs), we
propose an agent-based model (ABM), called PostDisasterSim. We apply the model
to analyze the recovery of five counties in Texas following Hurricane Harvey in
2017. Specifically, we construct a three-layer network comprising the human
layer, the social infrastructure layer, and the physical infrastructure layer,
using mobile phone location data and POI data. Based on prior studies and a
household survey, we develop the ABM to simulate how evacuated individuals
return to their homes, and social and physical infrastructures recover. By
implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms
of agent types, housing types, household income levels, and geographical
locations. Moreover, simulation results across nine scenarios quantitatively
demonstrate the positive effects of social and physical infrastructure
improvement plans. This study can assist disaster scientists in uncovering
nuanced recovery patterns and policymakers in translating policies like
resource allocation into practice.Comment: 28 pages, 10 figure
Together or Alone: Detecting Group Mobility with Wireless Fingerprints
This paper proposes a novel approach for detecting groups of people that walk
"together" (group mobility) as well as the people who walk "alone" (individual
movements) using wireless signals. We exploit multiple wireless sniffers to
pervasively collect human mobility data from people with mobile devices and
identify similarities and the group mobility based on the wireless
fingerprints. We propose a method which initially converts the wireless packets
collected by the sniffers into people's wireless fingerprints. The method then
determines group mobility by finding the statuses of people at certain times
(dynamic/static) and the space correlation of dynamic people. To evaluate the
feasibility of our approach, we conduct real world experiments by collecting
data from 10 participants carrying Bluetooth Low Energy (BLE) beacons in an
office environment for a two-week period. The proposed approach captures space
correlation with 95% and group mobility with 79% accuracies on average. With
the proposed approach we successfully 1) detect the groups and individual
movements and 2) generate social networks based on the group mobility
characteristics.Comment: This work has received funding from the European Union's Horizon 2020
research and innovation programme within the project "Worldwide
Interoperability for SEmantics IoT" under grant agreement Number 72315
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