A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems
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Author(s)
Chong, Linsen
Osorio Pizano, Carolina
Date Issued
July 2017
Journal
Transportation Science
Publisher
Institute for Operations Research and the Management Sciences (INFORMS)
Citation
Chong, Linsen, and Carolina Osorio. “A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems.” Transportation Science 52, 3 (June 2018): 637–656 © 2017 INFORMS
Version
Author's final manuscript
Abstract
This paper addresses large-scale urban transportation optimization problems with time-dependent continuous decision variables, a stochastic simulation-based objective function, and general analytical differentiable constraints. We propose a metamodel approach to address, in a computationally efficient way, these large-scale dynamic simulation-based optimization problems. We formulate an analytical dynamic network model that is used as part of the metamodel. The network model formulation combines ideas from transient queueing theory and traffic flow theory. The model is formulated as a system of equations. The model complexity is linear in the number of road links and is independent of the link space capacities. This makes it a scalable model suitable for the analysis of large-scale problems. The proposed dynamic metamodel approach is used to address a time-dependent large-scale traffic signal control problem for the city of Lausanne. Its performance is compared to that of a stationary metamodel approach. The proposed approach outperforms the stationary approach. This comparison illustrates the added value of providing the algorithm with analytical dynamic problem-specific structural information. The performance of a signal plan derived by the proposed approach is also compared to that of an existing signal plan for the city of Lausanne, and to that of a signal plan derived by a mainstream commercial signal control software. The proposed method can systematically identify signal plans with good performance. Keywords: simulation-based optimization; transient queuing theory; metamodel
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1287/TRSC.2016.0717
