A Novel LUTI Model Calibration Using Differential Evolution Algorithm

Abstract

LUTI (Land-Use and Transportation Interaction) models are decision-making aid tools that simulate complex dynamic bilateral feedback between transportation and land-use models within a territory. Although calibration (parameter estimation) is a crucial requirement of LUTI models, fully automated approaches with the usage of multi-objective functions have not been fully addressed. To address this limitation, a generic calibration approach is proposed for the parameters of the land-use model using a differential evolution algorithm. A global sensitivity analysis was performed to identify the most important land-use model parameters. These parameters were then calibrated using the differential evolution algorithm with the Root Mean Square Error (RMSE) and Mean Absolute Normalized Error (MANE) as multi-objective functions. Five key capabilities are provided in the suggested technique for calibration of LUTI models including 1) global estimation rather than local estimation, 2) consideration of multi-objective functions, 3) continuously improving the results, 4) easily adaptability, and 5) involving multi parameters in the calibration process. The TRANUS land-use model was used to test the performance of the suggested calibration technique. The validation and consolidation of the approach were tested based on convergence, minimization of errors, and modeled/observed data ratio by comparing with the genetic algorithm and particle swarm optimization techniques. The suggested approach using a deferential evaluation algorithm outperformed both genetic and particle swarm optimization techniques and provided the most stable and diverse solutions.