The Use of Deep Neural Networks (DNN) in Travel Demand Modelling

Traditional gravity models, while widely used in transport planning, often struggle to capture nonlinear spatial patterns and the heterogeneity of real-world mobility. In contrast, DNNs offer a flexible framework capable of integrating diverse explanatory variables and learning complex relationships from data. The study evaluates multiple DNN architectures trained on more than 90,000 observed OD pairs between 2477 municipalities, comparing their performance against a calibrated national gravity model. Key methodological considerations include the treatment of zero-trip observations, intra-zonal flows, and spatial aggregation levels. Results show that DNNs significantly outperform the gravity model in terms of prediction accuracy (MAE, R2, GEH), with the best-performing model achieving an R2 of 94%. The findings highlight the importance of data preprocessing, model architecture, and post-processing in improving predictive performance. Overall, the study demonstrates the potential of DNNs as a robust alternative to classical models in transport demand modeling, particularly when working with large, sparse, and heterogeneous datasets.