A deep learning framework to generate synthetic mobility data

Synthetic datasets are useful when real-world data is limited or unavailable. They can be used in transport simulation models to predict travel behavior or estimate demand for transportation services. However, building these models requires large amounts of data. We propose a novel framework to generate a synthetic population with trip chains using a combination of generative adversarial network (GAN) with recurrent neural network (RNN). Our model is compared with other recent methods, such as Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesis (CTGAN) and shows improved results in predicting trip distributions. The model is evaluated using multiple assessment metrics to gauge its performance and accuracy.