A Simulation-Based Optimization Framework for Urban Transportation Problems

Osorio, Carolina; Bierlaire, Michel

doi:10.1287/opre.2013.1226

Cited by 167 publications

(129 citation statements)

References 29 publications

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“…This new formulation is then embedded within the SO algorithm of Osorio and Bierlaire (2013) and used to address a signal control problem. In Section 2.1, we summarize the main ideas of a metamodel SO algorithm.…”

Section: Methodsmentioning

confidence: 99%

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A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Chong

Osorio

2018

Transportation Science

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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. The online appendix is available at https://doi.org/10.1287/trsc.2016.0717 .

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Section: Methodsmentioning

confidence: 99%

“…Hence, it can be solved with a variety of mainstream solvers. Additionally, the algorithm we use in this paper (Osorio and Bierlaire 2013) is a derivative-free algorithm. Hence, it does not require the estimation of first-or second-order derivatives of the SO objective function, f (x).…”

Section: Metamodel Framework Metamodel So Algorithmsmentioning

confidence: 99%

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

Chong

Osorio

2018

Transportation Science

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“…A multi-objective optimization function with respect to the control parameters was formulated to find a balanced trade-off among the mobility, safety and environmental benefits. Osorio and Bierlaire (2013) proposed a simulation-based optimization framework for solving the traffic light control problems in large-scale urban areas.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Jin

Kosonen

2017

Advances in Engineering Software

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A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering SoftwareAccess to the published version may require subscription. N.B. When citing this work, cite the original published paper. AbstractTraffic flow is considered as a stochastic process in road traffic modeling. Computer simulation is a widely used tool to represent traffic system in engineering applications. The increased traffic congestion in urban areas and their impacts require more efficient controls and management. While the effectiveness of control schemes highly depends on accurate traffic model and appropriate control settings, optimization techniques play a central role for determining the control parameters in traffic planning and management applications. However, there is still a lack of research effort on the scientific computing framework for optimizing traffic control and operations and facilitating real planning and management applications. To this end, the present study proposes a model-based optimization framework to integrate essential components for solving road traffic control problems in general. In particular, the framework is based on traffic simulation models, while the solution needs extensive computation during the engineering optimization process. In this study, an advanced genetic algorithm, extended by an external archive for storing globally elite genes, governs the computing framework, and it shows superior performance than the ordinary genetic algorithm because of the reduced number of fitness function evaluations in engineering applications. To evaluate the optimization algorithm and validate the whole software framework, this paper illustrates a detailed application for optimization of traffic light controls. The study optimizes a simple road network of two intersections in Stockholm to demonstrate the model-based optimization processes as well as to evaluate the presented algorithm and software performance.

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“…A rich literature in DOvS has developed over the last 50 years, and the specific methods [125], wireless sensor networks [49], circuit design [130], network reliability [123] Transportation and logistics Traffic control and simulation [211,17,151], metro/transit travel times [83,148], air traffic control [119,96] …”

Section: Discrete Optimization Via Simulationmentioning

confidence: 99%

“…Trust-region algorithms have been used, for example, to optimize simulations of urban traffic networks [151].…”

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confidence: 99%

Simulation optimization: a review of algorithms and applications

et al. 2014

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Simulation Optimization (SO) refers to the optimization of an objective function subject to constraints, both of which can be evaluated through a stochastic simulation. To address specific features of a particular simulation-discrete or continuous decisions, expensive or cheap simulations, single or multiple outputs, homogeneous or heterogeneous noise-various algorithms have been proposed in the literature. As one can imagine, there exist several competing algorithms for each of these classes of problems. This document emphasizes the difficulties in simulation optimization as compared to mathematical programming, makes reference to state-of-the-art algorithms in the field, examines and contrasts the different approaches used, reviews some of the diverse applications that have been tackled by these methods, and speculates on future directions in the field.

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A Simulation-Based Optimization Framework for Urban Transportation Problems

Cited by 167 publications

References 29 publications

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems

A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation

Simulation optimization: a review of algorithms and applications

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