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An exact second order cone programming approach for traffic assignment problems

Bayram, Vedat (2023) An exact second order cone programming approach for traffic assignment problems. RAIRO: Operations Research, . ISSN 0399-0559. (In press) (KAR id:103428)

Abstract

Demographic changes, urbanization and increasing vehicle ownership at unprecedented rates put a lot of strain on cities particularly on urban mobility and transportation and overwhelm transportation network infrastructures and current transportation systems, which are not built to cope with such a fast increasing demand. Traffic congestion is considered as the most difficult challenge to tackle for sustainable urban mobility and is aggravated by the increased freight activity due to e-commerce and on-demand delivery and the explosive growth in transportation network companies and ride-hailing services. There is a need to implement a combination of policies to ensure that increased urban traffic congestion does not lower the quality of life and threaten global climate and human health and to prevent further economic losses. This study aims to contribute to the United Nations (UN) climate action and sustainable development goals in tackling recurring traffic congestion problem in urban areas to achieve a sustainable urban mobility in that it offers a solution methodology for traffic assignment problem. We introduce an exact generalized solution methodology based on reformulation of existing traffic assignment problems as a second order cone programming (SOCP) problem and propose column generation (CG) and cutting plane (CP) algorithms to solve the problem effectively for large scale road network instances. We conduct numerical experiments to test the performance of the proposed algorithms on realistic road networks.

Item Type: Article
Uncontrolled keywords: Sustainable Development Goals, Traffic Congestion, Traffic Assignment, Column Generation, Cutting Plane, Second Order Cone Programming
Subjects: Q Science > Operations Research - Theory
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Vedat Bayram
Date Deposited: 25 Oct 2023 16:38 UTC
Last Modified: 09 Jan 2024 18:33 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/103428 (The current URI for this page, for reference purposes)

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