This document discusses combining spatial data from multiple sources to improve transport modeling. It notes that combining large data sources on transport and land use can provide the biggest benefits by reducing reliance on assumptions. A method called Temporal and Spatial Identifiers is presented for isolating the impact of individual behavior from external factors using noisy behavioral data. An application called MetroScan-TI is highlighted that combines different data sources like smartcard data and GPS traces in transport models using open source software like R and PostgreSQL.