Transit Network Frequencies-Setting Problem Solved Using a New Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation

Ruano, Edgar; Cobos, Carlos; Torres-Jiménez, José

doi:10.1007/978-3-319-62428-0_27

Cited by 5 publications

(8 citation statements)

References 23 publications

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“…MOGBHS is an extension of the Global-Best Harmony Search (GBHS) which provides great results while considering multiple objectives simultaneously. For example, it has provided great results for the generation of schedules for an integrated transit system [32]. MOGBHS showed superior performance relative to NSGA-II.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

et al. 2020

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The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.

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Section: Introductionmentioning

confidence: 99%

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

et al. 2020

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“…Guihaire and Hao (2008) and Ibarra-Rojas et al (2015) review 26 contributions dealing with headway optimization, showing that early works assume fixed demand and are based on analytic models (Newell 1971;Salzborn 1972;Schéele 1980;Han and Wilson 1982). Six of the 26 works Yu et al 2011;Huang et al 2013;Li et al 2013;Wu et al 2015) and Ruano et al (2017) more recently, employ genetic algorithms for the headway optimization problem. Most of them also use non-linear models, though Li et al (2013) offer a simulation model.…”

Section: Introductionmentioning

confidence: 99%

“…Whilst the last two mentioned works as well as others (Ceder 1984(Ceder , 2001) focus on the optimization of a single line, we are attempting to optimize several intersecting lines (i.e., a whole network) at once (Yu et al 2011). Following the sparse contributions that apply simulation to a problem-specific context (Vázquez-Abad and Zubieta 2005; Mohaymany and Amiripour 2009;Ruano et al 2017), we turn to simulation-based optimization. This approach has proven successful in similar application contexts (Osorio and Bierlaire 2013;Osorio and Chong 2015;Chong and Osorio 2018).…”

Section: Introductionmentioning

confidence: 99%

Population-based simulation optimization for urban mass rapid transit networks

2019

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In this paper, we present a simulation-based headway optimization for urban mass rapid transit networks. The underlying discrete event simulation model contains several stochastic elements, including time-dependent demand and turning maneuver times as well as direction-dependent vehicle travel and passenger transfer times. Passenger creation is a Poisson process that uses hourly origin-destination-matrices based on anonymous mobile phone and infrared count data. The numbers of passengers on platforms and within vehicles are subject to capacity restrictions. As a microscopic element, passenger distribution along platforms and within vehicles is considered. The bi-objective problem, involving cost reduction and service level improvement, is transformed into a single-objective optimization problem by normalization and scalarization. Population-based evolutionary algorithms and different solution encoding variants are applied. Computational experience is gained from test instances based on real-world data (i.e., the Viennese subway network). A covariance matrix adaptation evolution strategy performs best in most cases, and a newly developed encoding helps accelerate the optimization process by producing better short-term results.

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“…For example, Guihaire and Hao (2008) and Ibarra-Rojas et al (2015) review 26 contributions dealing with headway optimization, showing that early works assume fixed demand and are based on analytic models (Newell 1971;Salzborn 1972;Schéele 1980;Han and Wilson 1982). Six of the 26 works Yu et al 2011;Huang et al 2013;Li et al 2013;Wu et al 2015), as well as Ruano et al (2017) more recently, employ genetic algorithms for the headway optimization problem. Most of them also use non-linear models, though Li et al (2013) offer a simulation model.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike studies that attempt to optimize a single line (e.g., the latter two and Ceder, 1984and Ceder, , 2001, we seek to optimize several lines (i.e., a whole network) at once (Yu et al 2011). Following sparse contributions (Vázquez-Abad and Zubieta 2005;Mohaymany and Amiripour 2009;Ruano et al 2017) that apply simulation to a problemspecific context, we turn to simulation optimization. This approach has proven successful in similar application contexts (Osorio and Bierlaire 2013;Osorio and Chong 2015;Chong and Osorio 2018).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective simulation optimization for complex urban mass rapid transit systems

2019

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In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a nondominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our twophase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.

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Transit Network Frequencies-Setting Problem Solved Using a New Multi-Objective Global-Best Harmony Search Algorithm and Discrete Event Simulation

Cited by 5 publications

References 23 publications

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

A Multi-Objective Approach for the Calibration of Microscopic Traffic Flow Simulation Models

Population-based simulation optimization for urban mass rapid transit networks

Multi-objective simulation optimization for complex urban mass rapid transit systems

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