An integrated approach to system dynamics and data envelopment analysis for determining efficient policies and forecasting travel demand in an urban transport system
Easing transport congestion within a crowded urban environment requires management of travel demand. Managing traffic congestion in urban areas requires a set of effective policies and knowing when to apply them. In this paper, the authors focus on the managers’ need to determine the most effective policies to respond to changing travel demands. The authors propose a new framework to achieve this aim by applying a combination of a System Dynamics approach and a Data Envelopment Analysis technique. Despite previous studies that integrate discrete-event simulation with multi-criteria decision-making approaches, the combination of a system dynamics approach with a data envelopment analysis technique proposed in this paper shows an improvement both in forecasting the travel demand and managing it through effective policies.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/19427867
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Supplemental Notes:
- © 2020 Informa UK Limited, trading as Taylor & Francis Group. Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Norouzian-Maleki, Pegah
- Izadbakhsh, Hamidreza
- Saberi, Morteza
- Hussain, Omar
- Jahangoshai Rezaee, Mustafa
- GhanbarTehrani, Nasim
- Publication Date: 2022-2
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 157-173
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Serial:
- Transportation Letters: The International Journal of Transportation Research
- Volume: 14
- Issue Number: 2
- Publisher: Taylor & Francis
- ISSN: 1942-7867
- EISSN: 1942-7875
- Serial URL: http://www.tandfonline.com/toc/ytrl20/current
Subject/Index Terms
- TRT Terms: Complex systems; Multiple criteria decision making; Nonparametric analysis; Travel demand management
- Subject Areas: Planning and Forecasting; Policy; Transportation (General);
Filing Info
- Accession Number: 01839940
- Record Type: Publication
- Files: TRIS
- Created Date: Mar 24 2022 5:26PM