Joint Calibration for DTA Model Using Islands-GA and PC-SPSA
| Autor: | Tao Ma, Constantinos Antoniou, Moeid Qurashi, Yijiong Zhu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
050210 logistics & transportation
Mathematical optimization Optimization problem Heuristic (computer science) Computer science Reliability (computer networking) 05 social sciences 0211 other engineering and technologies 02 engineering and technology Simultaneous perturbation stochastic approximation Component (UML) 021105 building & construction 0502 economics and business Genetic algorithm Convergence (routing) Calibration |
| Zdroj: | Transportation Research Procedia. 52:716-723 |
| ISSN: | 2352-1465 |
| DOI: | 10.1016/j.trpro.2021.01.086 |
| Popis: | Dynamic Traffic Assignment (DTA) models are widely used in transportation system management. Calibration is a crucial step to improve the reliability and the accuracy of DTA models. We present a systematic framework to offline calibrate the supply and demand component of a DTA model. The essence of model calibration is an optimization problem, aiming to minimize the discrepancy between field conditions and simulated traffic measurements. To overcome limitations of a single optimization algorithm, a joint approach is developed for the calibration of supply and demand component respectively with different traffic measurements. As the calibration process is a nonlinear and stochastic problem, heuristic algorithms: the Genetic Algorithm (GA) and the Simultaneous Perturbation Stochastic Approximation (SPSA) Algorithm, are implemented as a complement solution. Instead of using the standard GA, to expedite searching efficiency, we introduce the Islands Genetic Algorithm (IGA) and SPSA with Principal Component Analysis (PC-SPSA) to solve the calibration problem. A case study on a network of Munich, Germany, is used to validate the proposed methodology. The promising results indicate that calibration of the supply and demand component of a DTA model with the proposed joint approach improves modelling accuracy. In comparison, IGA outperforms standard GA in terms of convergence speed and solution quality. |
| Databáze: | OpenAIRE |
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