A Simulation-Based Optimization Framework for Urban Transportation Problems
This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. Th...
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| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2014
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| Online Access: | http://hdl.handle.net/1721.1/89831 https://orcid.org/0000-0003-0979-6052 |
| _version_ | 1826194227842777088 |
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| author | Bierlaire, Michel Osorio Pizano, Carolina |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Bierlaire, Michel Osorio Pizano, Carolina |
| author_sort | Bierlaire, Michel |
| collection | MIT |
| description | This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets.
We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios, and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times. |
| first_indexed | 2024-09-23T09:52:34Z |
| format | Article |
| id | mit-1721.1/89831 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:52:34Z |
| publishDate | 2014 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/898312022-09-26T14:14:53Z A Simulation-Based Optimization Framework for Urban Transportation Problems Bierlaire, Michel Osorio Pizano, Carolina Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Osorio Pizano, Carolina This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets. We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios, and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times. 2014-09-19T13:36:21Z 2014-09-19T13:36:21Z 2013-12 2013-06 Article http://purl.org/eprint/type/JournalArticle 0030-364X 1526-5463 http://hdl.handle.net/1721.1/89831 Osorio, Carolina, and Michel Bierlaire. “A Simulation-Based Optimization Framework for Urban Transportation Problems.” Operations Research 61, no. 6 (December 2013): 1333–1345. https://orcid.org/0000-0003-0979-6052 en_US http://dx.doi.org/10.1287/opre.2013.1226 Operations Research Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Bierlaire, Michel Osorio Pizano, Carolina A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title | A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title_full | A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title_fullStr | A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title_full_unstemmed | A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title_short | A Simulation-Based Optimization Framework for Urban Transportation Problems |
| title_sort | simulation based optimization framework for urban transportation problems |
| url | http://hdl.handle.net/1721.1/89831 https://orcid.org/0000-0003-0979-6052 |
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