Urban Air Mobility (UAM) has become a potentially competitive transport mode in cities. Therefore, companies are starting different projects to deploy their own UAM systems. This work comprises the first steps towards the construction of a travel demand model to forecast the potential demand for the Uber UAM system in the area of Dallas – Fort Worth. As a preparatory phase, general information about income, employment, and transport infrastructures in the area of Dallas – Fort Worth from open data sources have been collected and studied. Thereafter, a k -means clustering algorithm has been applied with different subsets of the data, in order to infer the transport mode. The different clustering models were evaluated by their coherence with the information collected in the first phase. Best results were obtained when clustering separately the trips with common origin and destination areas. Finally, a preliminary introduction to the construction of the demand model has been given. It includes an inventory of the available explanatory variables and different regression models using the results obtained from the clustering. These regressions aim to determine the capacity of the available variables to explain travel demand in the area of Dallas – Fort Worth. Future improvements of the results would require a deeper understanding of how the data have been collected.