###### Abstract  
Activity-based models of travel demand have received considerable attention in transportation planning and forecasting over the last few decades. FEATHERS (The Forecasting Evolutionary Activity-Travel of Households and their Environmental Repercussions), developed by the Transportation Research Institute of Hasselt University, Belgium, is a micro-simulation framework developed to facilitate the implementation of activity-based models for transport demand forecasting. In this paper, we focus on several model considerations when applying this framework. First, the way to apply FEATHERS on a more disaggregated geographical level is investigated, with the purpose of obtaining more detailed travel demand information. Next, to reduce the computation time when applying FEATHERS on a more detailed geographical level, an iteration approach is proposed to identify the minimum size of the study area needed. In addition, the effect of stochastic errors inherently included in the FEATHERS framework is investigated, and the concept of confidence intervals is applied to determine the minimum number of model runs needed to minimize this effect. In the application, the FEATHERS framework is used to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders, Belgium. In doing so, all the aforementioned model considerations are taken into account. The results indicate that by integrating a light rail network into the current public transport network, there would be a relatively positive impact on public transport-related trips, but a relatively negative impact on the non-motorized-mode trips in this area. However, no significant change is found for car-related trips. activity-based models; travel demand; FEATHERS; geographical level; study area; stochastic errors; light rail network 2018 8 211 20  
Over the years, many techniques have evolved to estimate travel demand in different forms. Among others, the activity-based approach, as an alternative to the traditional four-step model of travel demand, has received considerable attention in transportation planning and forecasting in recent years [3,4]. Since the activity-based approach focuses on complete activity behavior patterns and adopts a holistic framework considering individual interactions and spatiotemporal constraints, it explicitly reveals the shortcomings of the conventional trip-based approach and can be used to address many policy issues and their impact ([4, 5, 6]. FEATHERS (The Forecasting Evolutionary Activity-Travel of Households and their Environmental Repercussions), developed by the Transportation Research Institute of Hasselt University, Belgium, is a multi-agent-based micro-simulation framework developed to facilitate the implementation of activity-based models for transport demand forecasting [7]. Since its birth, continuous efforts have been given to the methodological innovations and practical applications of this framework [8, 9, 10, 11]. In this study, several model considerations on this framework are investigated synthetically with the purpose of applying it for real-life travel demand forecasting in a more detailed, more reliable, and more practical way. First, to apply FEATHERS for travel demand forecasting, a geographical structure has to be determined in the first place, because based on different levels of geographical detail, i.e., the size of the traffic analysis zones (TAZs), the number of required data and the way to collect and compute these data are different, such as population density. Normally, when a more disaggregated geographical level is considered, more detailed travel demand information will be derived from the model, but, on the other hand, more input data are needed. Currently, the FEATHERS framework is being implemented in the Flanders region of Belgium, and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2386 TAZs with an average area of 5.8 km\({}^{2}\).