Titolo della tesi: Development and Implementation of Calibration Method for Macrosimulation Traffic Models
Macroscopic traffic flow models are indispensable for traffic surveillance, control, and infrastructure planning, providing critical insights into aggregate traffic behavior. However, calibrating these models remains a complex challenge, requiring not only extensive data but also a robust methodology to integrate and interpret this data effectively. This study introduces an aggregate calibration method for transport system models that leverages mobility data alongside other data sources within an optimization framework. The method explicitly accounts for congestion and anomalies, such as accidents and special events, ensuring its applicability under diverse traffic conditions. Comprehensive results are presented for various optimization algorithms, including Particle Swarm Optimization (PSO), Nelder-Mead (N-M), Simultaneous Perturbation Stochastic Approximation (SPSA), and the Firefly Algorithm, highlighting their performance in model calibration. Furthermore, the study delivers a benchmarking framework for mobility data analysis, enabling the effective integration of mobility counts data with control system performance outcomes. The proposed method is validated across multiple traffic scenarios, including both standard and abnormal conditions, demonstrating its robustness and versatility. Additionally, the study explores the effectiveness of machine learning techniques in traffic pattern recognition, offering new perspectives on data-driven approaches to traffic management. By addressing these challenges and providing innovative tools, this work advances the accuracy, reliability, and applicability of traffic models, paving the way for more efficient and sustainable transportation systems.