In this paper we investigate the sensitivity of the LWR model on network to its parameters and to the network itself. The quantification of sensitivity is obtained by measuring the Wasserstein distance between two LWR solutions corresponding to different inputs. To this end, we propose a numerical method to approximate the Wasserstein distance between two density distributions defined on a network. We found a large sensitivity to the traffic distribution at junctions, the network size, and the network topology.

Sensitivity analysis of the LWR model for traffic forecast on large networks using Wasserstein distance / Briani, M.; Cristiani, E.; Iacomini, E.. - In: COMMUNICATIONS IN MATHEMATICAL SCIENCES. - ISSN 1539-6746. - 16:1(2018), pp. 123-144. [10.4310/CMS.2018.v16.n1.a6]

Sensitivity analysis of the LWR model for traffic forecast on large networks using Wasserstein distance

Briani M.;Iacomini E.
2018

Abstract

In this paper we investigate the sensitivity of the LWR model on network to its parameters and to the network itself. The quantification of sensitivity is obtained by measuring the Wasserstein distance between two LWR solutions corresponding to different inputs. To this end, we propose a numerical method to approximate the Wasserstein distance between two density distributions defined on a network. We found a large sensitivity to the traffic distribution at junctions, the network size, and the network topology.
Earth mover's distanct, Godunov scheme, linear programming, LWR model, multi-path model, networks, traffic, uncertainty quantification, wasserstein distance
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Sensitivity analysis of the LWR model for traffic forecast on large networks using Wasserstein distance / Briani, M.; Cristiani, E.; Iacomini, E.. - In: COMMUNICATIONS IN MATHEMATICAL SCIENCES. - ISSN 1539-6746. - 16:1(2018), pp. 123-144. [10.4310/CMS.2018.v16.n1.a6]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1340665
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