## 2 Literature Review
### Aggregate Demand Models for Metro Train Services  
Boarding, alighting, ridership, and footfall are some of the most commonly used measures to model aggregate demand, typically aggregated at hourly, daily, or annual levels. Footfall is a comprehensive indicator that captures bi-directional station flows and offers a finer resolution than run-level measures, such as ridership or occupancy [14; 30]. Linear regression is widely used for modeling aggregate demand [14; 15]. However, some studies have highlighted the violations of the normality assumption, and hence have opted for log-transformed linear regression models [13]. Such log-linear models are still constrained in some respects, and their performance with regard to other non-linear specifications remains underexplored.  
[PERSON] and [PERSON] (2018) used a non-linear Transit Direct Ridership (TDR) model to investigate the effect of supply and demand factors on daily rail ridership [13]. Their results showed that the demand indicators, such as employment and housing density, positively influence rail ridership. The frequency of transit also showed a positive impact on train ridership, which is expected. [PERSON] and [PERSON] (2015) explored the role of socio-economics, stations, service, neighborhood, and land use attributes on transit ridership [15]. Their model found no impact of socio-demographic factors, but accessibility measures, such as park-and-ride facilities and feeder bus services, were significant. However, the role of the qualitative and quantitative aspects of supporting pedestrian and bicycle infrastructure, such as the quality of footpaths and the availability of public cycle docking stations, has received relatively less attention in the literature. Lastly, affordability and quality of transit service affect ridership in the expected direction [13; 14].  
Researchers have used ridership models to evaluate policy interventions aimed at increasing rail ridership. These interventions range from flexible fare systems or awareness programs to increasing fleet size or introducing new transit modes at access, line-haul, or egress stages. A study in South East Queensland, Australia, found that fare reduction and other incentives increased public transport ridership by 1.32% [12]. Similarly, [PERSON] et al. (2016) recommended transit-oriented development around boarding points and improvements in safety and reliability at each stage to enhance transit demand [14].  
Aggregate demand models, typically based on automated fare collection (AFC) or automated passenger counting (APC) data, offer large samples but suffer from self-selection bias, as they include only transit users. This exclusion prevents them from capturing the preferences and behaviors of non-users. Additionally, their aggregated nature makes it difficult to account for individual characteristics, spatial effects within stations, and multimodal connectivity. While linear and log-linear models are commonly used, non-linear approaches remain underexplored. Most studies focus on inter-station factors like connectivity between stops, but pay less attention to intra-station elements like terminal characteristics and surrounding land use. Furthermore, these models do not consider the interdependence between consideration decisions and ridership.