A neural network approach for population synthesis

This work explores techniques and metrics applied to the process of population synthesis used in activity-based modeling for traffic and transport simulation. The paper presents a novel population synthesis approach based on applying artificial neural networks (ANNs) and evaluates the approach against techniques derived from iterative proportional fitting (IPF), Bayesian networks, and data sampling methods. The documented research also investigates the appropriateness of goodness-of-fit measures and the need to consider similarity measures in assessing technique effectiveness with a focus on measures derived from Jaccard similarity coefficient. We established that IPF techniques should be preferred when datasets with the required composition are available, targeting few output variables and in relatively large zones of 5% region size. However, in smaller zones with sparser datasets, or inadequate dataset composition, the proposed ANN technique and identified sampling method are favorable. The proposed ANN method shows suitability for the population synthesis problem compared with the examined methods, but further work is required to improve model fitting speed, explore mixture models of multiple ANNs, and apply data reduction techniques to reduce the observation-decision space. The research findings also established that comparing scenarios of varying sizes and variable numbers is challenging when employing specific goodness-of-fit measures. Furthermore, the mentioned similarity measures can reveal concerns regarding inconsistent archetypes and low-quality populations that can remain concealed when using error metrics.