Simulation-Based Aggregate Calibration of Destination Choice Models Using Opportunistic Data: A Comparative Evaluation of SPSA, PSO, and ADAM Algorithms

This paper presents an initial contribution to a broader research initiative focused on the aggregate calibration of travel demand sub-models using low-cost and widely accessible data. Specifically, this first phase investigates methods and algorithms for the aggregate calibration of destination choice models, with the objective of assessing the possible utilization of an external observed matrix, eventually derived from opportunistic data. It can be hypothesized that such opportunistic data may originate from processed mobile phone data or result from the application of data fusion techniques that produce an estimated observed trip matrix. The calibration problem is formulated as a simulation-based optimization task and its implementation has been tested using a small-scale network, employing an agent-based model with a nested demand structure. A range of optimization algorithms is implemented and tested in a controlled experimental environment, and the effectiveness of various objective functions is also examined as a secondary task. Three optimization techniques are evaluated: Simultaneous Perturbation Stochastic Approximation (SPSA), Particle Swarm Optimization (PSO), and Adaptive Moment Estimation (ADAM). The application of the ADAM optimizer in this context represents a novel contribution. A comparative analysis highlights the strengths and limitations of each algorithm and identifies promising avenues for further investigation. The findings demonstrate the potential of the proposed framework to advance transportation modeling research and offer practical insights for enhancing transport simulation models, particularly in data-constrained settings.