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Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization

Lovreglio, Ruggiero; Ronchi, Enrico LU and Nilsson, Daniel LU (2015) In Physica A: Statistical Mechanics and its Applications 438. p.308-320
Abstract
The formulation of pedestrian floor field cellular automaton models is generally based on hypothetical assumptions to represent reality. This paper proposes a novel methodology to calibrate these models using experimental trajectories. The methodology is based on likelihood function optimization and allows verifying whether the parameters defining a model statistically affect pedestrian navigation. Moreover, it allows comparing different model specifications or the parameters of the same model estimated using different data collection techniques, e.g. virtual reality experiment, real data, etc. The methodology is here implemented using navigation data collected in a Virtual Reality tunnel evacuation experiment including 96 participants. A... (More)
The formulation of pedestrian floor field cellular automaton models is generally based on hypothetical assumptions to represent reality. This paper proposes a novel methodology to calibrate these models using experimental trajectories. The methodology is based on likelihood function optimization and allows verifying whether the parameters defining a model statistically affect pedestrian navigation. Moreover, it allows comparing different model specifications or the parameters of the same model estimated using different data collection techniques, e.g. virtual reality experiment, real data, etc. The methodology is here implemented using navigation data collected in a Virtual Reality tunnel evacuation experiment including 96 participants. A trajectory dataset in the proximity of an emergency exit is used to test and compare different metrics, i.e. Euclidean and modified Euclidean distance, for the static floor field. In the present case study, modified Euclidean metrics provide better fitting with the data. A new formulation using random parameters for pedestrian cellular automaton models is also defined and tested. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Pedestrian navigation, Path-finding, Model calibration, Cellular automaton model, Maximum likelihood, Virtual reality, tunnel evacuation
in
Physica A: Statistical Mechanics and its Applications
volume
438
pages
308 - 320
publisher
Elsevier
external identifiers
  • wos:000360871200029
  • scopus:84937786274
ISSN
0378-4371
DOI
10.1016/j.physa.2015.06.040
language
English
LU publication?
yes
id
2c522041-15fa-41e0-90c4-4174192e9762 (old id 7696015)
date added to LUP
2015-08-10 10:02:05
date last changed
2017-09-10 03:59:31
@article{2c522041-15fa-41e0-90c4-4174192e9762,
  abstract     = {The formulation of pedestrian floor field cellular automaton models is generally based on hypothetical assumptions to represent reality. This paper proposes a novel methodology to calibrate these models using experimental trajectories. The methodology is based on likelihood function optimization and allows verifying whether the parameters defining a model statistically affect pedestrian navigation. Moreover, it allows comparing different model specifications or the parameters of the same model estimated using different data collection techniques, e.g. virtual reality experiment, real data, etc. The methodology is here implemented using navigation data collected in a Virtual Reality tunnel evacuation experiment including 96 participants. A trajectory dataset in the proximity of an emergency exit is used to test and compare different metrics, i.e. Euclidean and modified Euclidean distance, for the static floor field. In the present case study, modified Euclidean metrics provide better fitting with the data. A new formulation using random parameters for pedestrian cellular automaton models is also defined and tested.},
  author       = {Lovreglio, Ruggiero and Ronchi, Enrico and Nilsson, Daniel},
  issn         = {0378-4371},
  keyword      = {Pedestrian navigation,Path-finding,Model calibration,Cellular automaton model,Maximum likelihood,Virtual reality,tunnel evacuation},
  language     = {eng},
  pages        = {308--320},
  publisher    = {Elsevier},
  series       = {Physica A: Statistical Mechanics and its Applications},
  title        = {Calibrating floor field cellular automaton models for pedestrian dynamics by using likelihood function optimization},
  url          = {http://dx.doi.org/10.1016/j.physa.2015.06.040},
  volume       = {438},
  year         = {2015},
}