Simulation-Based Optimization: Achieving Computational Efficiency Through the Use of Multiple Simulators

Abstract

Transportation agencies often resort to the use of traffic simulation models to evaluate the impacts of changes in network design or network operations. They often have multiple traffic simulation tools that cover the network area where changes are to be made. These multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration) as well as in their modeling scale (e.g., city-scale versus regional-scale). The choice of which simulation model to rely on, let alone of how to combine their use, is intricate. A larger-scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This paper proposes an optimization framework that enables multiple simulation models to be jointly and efficiently used to address continuous urban transportation optimization problems.We propose a simulation-based optimization algorithm that embeds information from both a high-accuracy low-efficiency simulator and a low-accuracy high-efficiency simulator. At every iteration, the algorithm decides which simulator to evaluate. This decision is based on an analytical approximation of the accuracy loss due to running the loweraccuracy model. We formulate an analytical expression that is based on a differentiable and computationally efficient to evaluate traffic assignment model.We evaluate the performance of the algorithm with a traffic signal control problem on both a small network and a city network. We show that the proposed algorithm identifies signal plans with excellent performance, and can do so at a significantly lower computational cost than when systematically running the high-accuracy simulator.The proposed methodology contributes to enable large-scale high-resolution traffic simulation models to be used efficiently for simulation-based optimization. More broadly, it * osorioc@mit.edu, Tel: 617.452.3063 † kkselvam@mit.edu 1 enables the use of multiple simulation models that may differ, for instance, in their scale, their resolution or their computational costs, to be used jointly for optimization.

Simulation-Based Travel Time Reliable Signal Control

“…One extension of this work is to use a macroscopic model with time-dependent and endogenous assignement. Computationally efficient analytical formulations with endogenous assignement suitable for SO and for large-scale networks have been recently proposed for both traffic management problems (Osorio and Selvam 2017), as well as for calibration problems for the Berlin metropolitan area (Zhang et al 2017). The extension of such formulations for reliable SO problems is of interest.…”

“…For details regarding its formulation, we refer the reader to Osorio and Bierlaire (2013). The formulation of the method has been extended to successfully address difficult constrained simulation-based problems in a computationally efficient manner (Osorio and Nanduri 2015a,b, Osorio and Chong 2015, Osorio and Selvam 2017, Zhang et al 2017).…”

“…The computational inefficiency of microscopic simulators has mostly limited their use to what-if (i.e., scenario-based) analysis (as in, for instance, Bullock et al (2004), Ben-Akiva et al (2003)). Their use within simulation-based optimization (SO) algorithms is rare, and is limited to the use of first-order distributional information (Osorio and Selvam 2017, Osorio and Nanduri 2015a,b, Osorio and Chong 2015, Li, Abbas, Pasupathy and Head 2010, Stevanovic et al 2009, 2008, Branke et al 2007, Yun and Park 2006, Hale 2005, Joshi et al 1995. This paper proposes a methodology that enables the use of highresolution stochastic traffic simulators to efficiently address higher-order SO problems.…”

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

“…In particular, in that work the decision variables are coefficients of a route choice model. Second, metamodels have been formulated to design multi-model SO algorithms, where multiple simulators with different computational runtime costs are jointly used (Osorio and Selvam 2015).…”

Memory-Based Evolutionary Algorithms for Nonlinear and Stochastic Programming Problems

“…In the real world, there are many challenging applications which naturally require involving optimization techniques to have the best configurations, and find the optimal resources [1]. In particular, these complex application problems are computationally expensive in a sense of resources and time and most of them contain noise.…”