Title: A big data and cloud computing model architecture for a multi-class travel demand estimation through traffic measures: a real case application in Italy

Authors: Armando Cartenì; Ilaria Henke; Assunta Errico; Maria Ida Di Bartolomeo

Addresses: Department of Engineering, University of Campania 'Luigi Vanvitelli', Aversa, 81031, Italy ' Department of Civil, Construction and Environmental Engineering, University of Naples 'Federico II', Naples, 80125, Italy ' Department of Agriculture, University of Naples 'Federico II', Naples, 80055, Italy ' Department of Engineering, University of Campania 'Luigi Vanvitelli', Aversa, 81031, Italy

Abstract: The big data and cloud computing are an extraordinary opportunity to implement multipurpose smart applications for the management and the control of transport systems. The aim of the paper was to propose a big data and cloud computing model architecture for a multi-class origin-destination demand estimation based on the application of a bi-level transport algorithm using traffic counts on congested network, also for proposing sustainable policies at urban scale. The proposed methodology has been applied to a real case study in terms of travel demand estimation within the city of Naples (Italy), also aiming to verify the effectiveness of a sustainable policy in terms of reducing traffic congestion of about 20% through en-route travel information. The obtained results, although preliminary, suggest the usefulness of the proposed methodology in terms of ability in real-time/pre-fixed time periods traffic demand estimation.

Keywords: cloud computing; big data; virtualisation; smart city; smart road; internet of things; transportation planning; transport service; demand estimation; sustainable mobility; simulation model; intelligent transport system; ITS; Italy.

DOI: 10.1504/IJCSE.2023.133672

International Journal of Computational Science and Engineering, 2023 Vol.26 No.5, pp.482 - 493

Received: 15 Nov 2020
Accepted: 06 Mar 2021
Published online: 29 Sep 2023 *

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