## 1 Introduction  
Public transport is a more affordable and sustainable alternative to solo personal vehicle travel due to its lower fares and its mitigative effect on urban congestion and pollution [1, 2]. Therefore, several developing countries, such as India, are turning to trains (metro rail) to reduce urban congestion due to their higher passenger carrying capacity [3]. However, despite such large infrastructure investments in metro trains, actual ridership levels have fallen far below the targets in many cities [4]. Therefore, identifying and addressing key deterrents to the demand for metro services becomes an urgent priority in order to resolve urban mobility challenges. In this direction, this study proposes a joint model for train demand that is based on both disaggregate consideration probability at the individual level, as well as the aggregate hourly station demand. Several studies globally have shown that existing forecasts for metro demand are highly optimistic, whereas the realized demand is much lower. For example, [PERSON] et al. (2020) noted a substantial shortfall in daily metro ridership compared to the projections in Indian cities, such as Bengaluru (50% shortfall), Chennai (80% shortfall), and Jaipur (90% shortfall) [5]. Similar challenges are observed in neighboring South Asian countries, as well as the U.S. and Europe. For instance, in Dhaka, Bangladesh, mass rapid transit is used for 10% to 20% of short- to medium-distance trips [6]. In the U.S., Los Angeles and Houston metros fall short of their projected ridership by 30% and 80%, respectively [7; 8]. European urban rail projects have a 20% gap between forecasted and actual ridership [9]. These trends highlight the global challenge in achieving the expected rail transit ridership. The shortfall in train ridership may arise from either a demand deficit at the disaggregate choice level or a lower-than-expected aggregate ridership. At the disaggregate level, some individuals may not even include or consider trains in their choice set. Consideration is an initial and essential stage of demand since it captures the behavioral intent of travelers to include this mode in their choice set. This dimension can be viewed as an indicator of potential demand and comprises the expanded user base of regular users, as well as infrequent travelers. The consideration probability for the metro is influenced by factors including growing personal vehicle ownership, poor walkability, and low accessibility to train stations [10; 11]. Hence, effective policy interventions are necessary to encourage non-users to consider and shift to train travel. On the other hand, footfall represents the second or subsequent stage of demand, which is conditional on the consideration that reflects the realized demand or the actual extent of metro usage. The conventional literature on transportation mainly focuses on aggregated measures, such as footfall, boarding, or alighting at stations, representing indicators of the realized demand. This measure reflects the intensity of use and is influenced more by transit attributes such as fare reduction or increasing service frequency. Also, such aggregate demand may be influenced by factors such as (i) poor transit operational characteristics, such as low frequency or high fares [12; 13], (ii) inadequate station characteristics, including limited parking availability and complex navigation within the station [14], (iii) insufficient accessibility to the station through poor first- and last-mile connectivity and non-usable footpaths [15], and (iv) a lack of attractive land-use or built environment characteristics near the station [16]. The existing studies on metro demand focus either on the realized demand through ridership metrics or the potential demand through disaggregate consideration probabilities. Both approaches present only a partial picture of actual metro usage due to the following limitations.