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DUST SIGNED

Data Assimilation for Agent-Based Models: Applications to Civil Emergencies

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DUST" data sheet

The following table provides information about the project.

Coordinator
UNIVERSITY OF LEEDS 

Organization address
address: WOODHOUSE LANE
city: LEEDS
postcode: LS2 9JT
website: www.leeds.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Total cost 1˙499˙840 €
 EC max contribution 1˙499˙840 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-01-01   to  2022-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITY OF LEEDS UK (LEEDS) coordinator 1˙499˙840.00

Map

 Project objective

Civil emergencies such as flooding, terrorist attacks, fire, etc., can have devastating impacts on people, infrastructure, and economies. Knowing how to best respond to an emergency can be extremely difficult because building a clear picture of the emerging situation is challenging with the limited data and modelling capabilities that are available. Agent-based modelling (ABM) is a field that excels in its ability to simulate human systems and has therefore become a popular tool for simulating disasters and for modelling strategies that are aimed at mitigating developing problems. However, the field suffers from a serious drawback: models are not able to incorporate up-to-date data (e.g. social media, mobile telephone use, public transport records, etc.). Instead they are initialised with historical data and therefore their forecasts diverge rapidly from reality.

To address this major shortcoming, this research will develop dynamic data assimilation methods for use in ABMs. These techniques have already revolutionised weather forecasts and could offer the same advantages for ABMs of social systems. There are serious methodological barriers that must be overcome, but this research has the potential to produce a step change in the ability of models to create accurate short-term forecasts of social systems. The project is largely methodological, and will evidence the efficacy of the new methods by developing a cutting-edge simulation of a city – entitled the Dynamic Urban Simulation Technique (DUST) – that can be dynamically optimised with streaming ‘big’ data. The model will ultimately be used in three areas of important policy impact: (1) as a tool for understanding and managing cities; (2) as a planning tool for exploring and preparing for potential emergency situations; and (3) as a real-time management tool, drawing on current data as they emerge to create the most reliable picture of the current situation.

 Publications

year authors and title journal last update
List of publications.
2019 Tomas Crols, Nick Malleson
Quantifying the ambient population using hourly population footfall data and an agent-based model of daily mobility
published pages: 201-220, ISSN: 1384-6175, DOI: 10.1007/s10707-019-00346-1
GeoInformatica 23/2 2019-08-30

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