###### Abstract
Although a large amount of useful information can be deduced at this level, the size of each Subzone is still somewhat too large to avoid trip generation and attraction focusing too much on one point (e.g., TAZ centroid), which might result in unreasonable traffic allocation with some congested links and some empty links. Therefore, how to apply the FEATHERS framework to a zoning system with higher resolution is the first research question of this study. Extension of the FEATHERS framework to a more disaggregated geographical level provides us with the opportunity to generate more detailed travel demand information. However, the computation time of the model will be magnified dramatically. For instance, in the FEATHERS framework, it takes approximately 16 h for a single model run based on 50% of the full population of Flanders at the Subzone level (based on an Intel Core i5 CPU at 2.30 GHz, with Windows Seven (32 bits)). If a more disaggregated geographical level is under consideration, the running time of FEATHERS is expected to increase from hour-based unit to day-based unit, which may influence to a great extent the efficient use and the real-life application of the model. To reduce the computation time when applying FEATHERS, one possible solution is to restrain the size of the study area and to conduct the computation only for the selected area, as it is often the case that a small territory (e.g., a municipality) rather than the whole of Flanders is the focus of a specific study. Therefore, the second aim of this study is to identify the minimum size of study area needed for a target territory. In addition, activity-based models, focusing on activity-travel generation and activity scheduling decisions, use in most cases a micro-simulation approach, in which heterogeneity and randomness are fundamental characteristics since they simulate individual activity patterns by drawing randomly from marginal and conditional probability distributions that are defined for the various choice facets that make up an activity pattern [12]. Therefore, stochastic errors are inherently included in such models. As a result, running a transport micro-simulation model several times with the same input will result in different outputs due to the random number seed used in each run [13]. Analysis of their impacts on the model outputs thereby becomes a vital step for reliable transportation planning and forecasting. By taking the aforementioned research questions into account, we apply the FEATHERS framework to a practical project that investigates the potential impact of light rail initiatives on travel demand in a local network in Flanders. In doing so, the city of Leuven--the capital of the province of Flemish Brabant--is selected as a case study to predict the travel demand. The city has quite a large transport potential, and yet is reasonably compact in size. Nevertheless, the city has no urban or regional light rail system so far. The remainder of this paper is structured as follows. In Sections 2 and 3, we briefly introduce the FEATHERS framework and the light rail initiatives in the city of Leuven, respectively. The model considerations and the methodology proposed in this study are elaborated on in Section 4. In Section 5, the application of the model and the corresponding results are provided. The paper ends with concluding remarks in Section 6.