## 1 Introduction
Aggregate demand models suffer from self-selection bias, as they rely solely on data from metro users and overlook the responses of non-users. Additionally, they cannot quantify the effect of intra-station variables (e.g., first- and last-mile connectivity and land use), and individual-level variables (e.g., socio-economic characteristics, work characteristics, and user perceptions) that vary across respondents within the same buffer area in a metro station [10; 17]. In addition, most existing aggregate demand models use unidirectional measures, such as boarding or alighting, which do not provide a comprehensive indication of demand for terminal planning or first- and last-mile service design. These limitations can lead to inaccurate or misleading estimates of demand. Conversely, disaggregate models of consideration and choice can overcome self-selection bias and capture intra-station and inter-person variables. However, their sample sizes are smaller compared to the aggregate demand data used from the automated fare collection (AFC) data. Also, these disaggregate models capture the behavioral intent of travelers to include the metro in their choice set, but not their actual usage level. Hence, they cannot be used for forecasting ridership or revenue estimates. The above discussion highlights the need for investigating both these stages of demand and their interactions, which are overlooked in the literature. A clear distinction between the aforementioned potential and realized demand dimensions becomes essential from a policy perspective. By improving the consideration propensities, transit agencies may be able to expand their user base, whereas interventions focused on football can enhance ridership. Thus, there is a need for different policy levers to address these dimensions. For example, it is plausible that out-of-vehicle attributes, as well as accessibility, could affect consideration, whereas in-vehicle experiences and station amenities could affect ridership. By explicitly modeling the two dimensions and their interactions, this study aims to examine the following research questions that are underexplored: (i) which segments show a deficit in consideration probabilities, and through what factors can they be enhanced? (ii) How do intra-station and inter-personal variables affect aggregate football demand? (iii) What is the role of self-selection bias in the aggregate demand model? (iv) Whether and to what extent does disaggregate consideration probability have a mediating effect on realized football? (v) At what locations does football remain low despite a high consideration rate, and how can this be mitigated? (vi) Are segment-specific, location-specific, and dimension-specific (consideration vs. footfall) policies more effective than uniform ones in enhancing demand? To address these research issues, the following objectives are pursued in this study. 1. To propose a framework for the joint analysis of both disaggregate consideration probabilities and aggregate football outcomes. 2. To investigate the behavioral differences in consideration propensity outcomes, using market segments based on personal mobility resource availability. 3. To investigate the interdependence between potential demand (disaggregate consideration) and realized demand (aggregate football) outcomes, with particular attention to distinguishing the role of key variables across the two dimensions. 4. To illustrate the differential impacts of policy interventions across consideration and footfall dimensions. These objectives are investigated based on empirical data from Chennai City, India, using two main data sources: footfall data from the automated fare collection (AFC) system at metro stations and mode consideration data from a household survey. Football is defined here as the total number of passengers entering and exiting the train station (sum of hourly boarding and alighting) during a given time period.