Traditional and innovative on-demand transport services, such as taxi, car sharing or dial-a-ride respectively, can provide a level of flexibility to the public transport with the aim to guarantee a better service and to reduce the exploitation costs. In this context, in order to point out the key-factors of on-demand services, this study focuses on traditional on-demand service (such as taxi one), and presents the results of a demand analysis and modelling, obtained processing taxi floating car data (FCD) available for the city of Rome. The GPS position of each taxi is logged every few seconds and it was possible to build a monthly database of historical GPS traces through around 27 thousands of GPS positions recorded per day (more than 750 thousands for the entire month). Further, the patterns of within-day and day-to-day service demand are investigated, considering the origin, the destination and other characteristics of the trips (e.g. travel time). The time-based requests for taxi service were obtained and used to analyse the trip distribution in space and on time. These analyses allow us to forecast trips generated/attracted by each zone within the cities according to land use characteristics and time slices. Therefore, a regression tree analysis was developed and different regressive model specifications with different set of attributes (e.g. number of subway stations, number of zonal employees, population) were tested in order to assess their contribution on describing such a service use.