Improving the calibration time of traffic simulation models using parallel computing technique
Yükleniyor...
Dosyalar
Tarih
2019-06
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The calibration procedure for traffic simulation models can be a very time-consuming process in the case of a large-scale and complex network. In the application of Evolutionary Algorithms (EA) such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for calibration of traffic simulation models, objective function evaluation is the most time-consuming step in such calibration problems, because EA has to run a traffic simulation and calculate its corresponding objective function value once for each set of parameters. The main contribution of this study has been to develop a quick calibration procedure for the parameters of driving behavior models using EA and parallel computing techniques (PCTs). The proposed method was coded and implemented in a microscopic traffic simulation software. Two scenarios with/without PCT were analyzed using the developed methodology. The results of scenario analysis show that using an integrated calibration and PCT can reduce the total computational time of the optimization process significantly-in our experiments by 50%-and improve the optimization algorithm's performance in a complex optimization problem. The proposed method is useful for overcoming the limitation of computational time of the existing calibration methods and can be applied to various EAs and traffic simulation software.
Açıklama
Anahtar Kelimeler
Behavior models, Bus priority, Buses, Calibration, Calibration problems, Calibration time, Complex networks, Complex optimization problems, Computer software, Corresponding objective function value, EA, Evolutionary computation, Genetic algorithm, Genetic algorithms, Highway traffic control, Integrated calibration, Intelligent systems, Intelligent vehicle highway systems, Intersections, Linear programming, Microscopic traffic simulation software, Microscopic traffic simulation, Objective function evaluation, Objective function values, Optimization, Optimization algorithm, Optimization algorithms, Parallel computing technique, Parallel processing, Parallel computing, Parallel computing techniques, Parallel Hybrid GAPSO, Parallel hybrid PSOGA, Parallel hybrids, Parallel processing systems, Parameter estimation, Particle swarm optimisation, Particle swarm optimization, Particle swarm optimization (PSO), Quick calibration procedure, Sociology, Statistics, Time-consuming process, Time-consuming step, Total computational time, Traffic control, Traffic engineering computing, Traffic simulation models, Traffic signals, Traffic simulation model, VISSIM
Kaynak
2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Dadashzadeh, N., Ergün, M., Kesten, A. S. & Zura, M. (2019). Improving the calibration time of traffic simulation models using parallel computing technique. Paper presented at the 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), 1-7. doi:10.1109/MTITS.2019.8883322
