Efficacy of Pedestrian Evacuation Time Estimate Using Agent Based Queuing Network Model

Bharat Kunwar (), Filippo Simini () and Anders Johansson ()
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Bharat Kunwar: University of Bristol, Faculty of Engineering
Filippo Simini: University of Bristol, Faculty of Engineering
Anders Johansson: University of Bristol, Faculty of Engineering

A chapter in Traffic and Granular Flow '15, 2016, pp 289-295 from Springer

Abstract: Abstract ThereKunwar, Bharat is an increasingSimini, Filippo risk of exposure to disastersJohansson, Anders due to rising instances of extreme events (Munich et al. Topics Geo: Natural Catastrophes 2013: Analyses, Assessments, Positions. Munchener Ruckversicherungs-Gesellschaft, Munich, 2014, [7]) and growing urban settlements (United Nationsin World economic and social survey 2013: sustainable development challenges, 2013, [9]). As such, it is important that we explore ways measure preparedness to such disasters. In a previous work (Kunwar et al. in Evacuation time estimate for a total pedestrian evacuation using queuing network model and volunteered geographic information, 2015, [5]), we used agent based modelling (ABM) to investigate 50 cities in the UK and draw a link between their attributes such as spatial size, population, exit width and their evacuation time estimates (ETE) for a full city evacuation, one of the most stressing mobility use cases for a city. In this work, we examine the efficacy of those results by looking at how sensitive they are to fundamental diagram parameters. We found the overall ETE to be most sensitive to density threshold for minimum velocity with variations as large as an order of magnitude. We observed that ETE is also sensitive to maximum density limit but the results keep within the same order of magnitude. We also saw an increasing gap in ETE for lowest and highest values of density threshold for minimum velocity with every doubling of population. We reached a conclusion that it is necessary to carefully establish the input parampAGNeters if a robust result is desired for a network-based ‘mesoscopic’ modelling.

Date: 2016
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DOI: 10.1007/978-3-319-33482-0_37

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