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Calibration of a rule-based intelligent network simulation model

Calibration of a rule-based intelligent network simulation model

Memon, A. A., Meng, M. ORCID: 0000-0001-7240-6454, Wong, Y. D. and Lam, S. H. (2016) Calibration of a rule-based intelligent network simulation model. Journal of Modern Transportation, 24 (1). pp. 48-61. ISSN 2095-087X (Print), 2196-0577 (Online) (doi:https://doi.org/10.1007/s40534-015-0091-1)

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Abstract

This paper is focused on calibration of an intelligent network simulation model (INSIM) with real-life transportation network to analyse the INSIM’s feasibility in simulating commuters’ travel choice behaviour under the influence of real-time integrated multimodal traveller information (IMTI). A transportation network model for the central and western areas of Singapore was simulated in PARAMICS and integrated with INSIM expert system by means of an application programming interface to form the INSIM. Upon calibration, INSIM was able to realistically present complicated scenarios in which real-time IMTI was provided to commuters and the network performance measures being recorded.

Item Type: Article
Additional Information: © The Author(s) 2016. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords: Traffic simulation; Integrated traveller information; Calibration; Mode choice
Subjects: H Social Sciences > HE Transportation and Communications
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Connected Cities Research Group
Faculty of Business > Department of Systems Management & Strategy
Last Modified: 12 Feb 2019 12:08
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/22706

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