A Caltech Library Service

Parameter estimation for macroscopic pedestrian dynamics models from microscopic data

Gomes, Susana N. and Stuart, Andrew M. and Wolfram, Marie-Therese (2018) Parameter estimation for macroscopic pedestrian dynamics models from microscopic data. . (Unpublished)

[img] PDF - Submitted Version
See Usage Policy.


Use this Persistent URL to link to this item:


In this paper we develop a framework for parameter estimation in macroscopic pedestrian models using individual trajectories -- microscopic data. We consider a unidirectional flow of pedestrians in a corridor and assume that the velocity decreases with the average density according to the fundamental diagram. Our model is formed from a coupling between a density dependent stochastic differential equation and a nonlinear partial differential equation for the density, and is hence of McKean--Vlasov type. We discuss identifiability of the parameters appearing in the fundamental diagram from trajectories of individuals, and we introduce optimization and Bayesian methods to perform the identification. We analyze the performance of the developed methodologies in various situations, such as for different in- and outflow conditions, for varying numbers of individual trajectories and for differing channel geometries.

Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription Paper
Additional Information:The work of S.G. and A.S. was supported by the EPSRC Programme Grant EQUIP. The work of M.-T.W. and AS was supported by a Royal Society international collaboration grant. M.-T. W. acknowledges partial support from the Austrian Academy of Sciences via the New Frontier's grant NST-001. SG is grateful to Imperial College London for use of computer facilities. The authors are grateful to Grigorios Pavliotis for helpful discussions.
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)UNSPECIFIED
Österreichische Akademie der WissenschaftenNST-001
Subject Keywords:Macroscopic pedestrian models, generalized McKean-Vlasov equations, parameter estimation, optimization-based and Bayesian inversion
Record Number:CaltechAUTHORS:20190719-112058516
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97297
Deposited By: Tony Diaz
Deposited On:19 Jul 2019 20:04
Last Modified:19 Jul 2019 20:04

Repository Staff Only: item control page