About the project
Understanding mobility behavior, for instance, traffic flows or public transportation usage in urban areas, has become an increasingly important challenge for a wide variety of stakeholders, including social and environmental scientists, urban planners, federal statistical agencies, and policymakers. These stakeholders are particularly interested in understanding the short-, mid-, and long-term evolution of mobility behavior in specific geographic regions and its interdependencies with other factors. Such research requires high-quality data regarding the actual mobility behavior, as for example, historical archives of vehicle traffic data, shared bike usage, and public transportation usage. RUSHMORE will address sparsity and costliness of mobility data through representative synthetic data generation and specialized machine learning models for spatio-temporal knowledge transfer.
