## 7 Conclusions  
This study proposes a novel joint demand modeling framework to analyze metro travel behavior based on understanding both disaggregate consideration responses from individuals, indicating the potential demand, and aggregate station-level hourly footfall data from AFC systems, measuring the realized demand. This study overcomes the limitations of existing aggregate demand models by accounting for self-selection effects and intra-person variability, as well as heterogeneity, in consideration propensity across different segments of users. The proposed model enhances the behavioral realism and accuracy of overall demand estimation for the metro by disentangling the effects of key variables across consideration outcomes from footfall decisions. Further, empirical evidence of the mediating role of consideration propensity on footfall is presented, based on data from Chennai City, India. A discrete choice model is specified and estimated for the disaggregate consideration decision due to its binary nature. The results highlight that the decision to consider trains in the choice set, and the associated sensitivity to walkability, first- and last-mile connectivity, and transit perceptions, vary across segments, based on personal mobility resources. For instance, improved walkability is a strong enabler for encouraging train consideration among the low-mobility segment, whereas enhancing multimodal connectivity through affordable, reliable, and faster first- and last-mile alternatives can attract the medium personal mobility segment by increasing their consideration probability. In contrast, among the high personal mobility segment, a negative perception of train services is the main barrier to consider train travel. Neglecting these differences across the segments leads to biased estimates and misinformed policies. Hence, segment-specific policies are likely to be more effective in enhancing consideration of trains among non-users. An integrated footfall model is formulated, which includes consideration of propensity as an instrumental variable. The results confirm that a non-linear Box-Cox transformed model outperforms other specifications, including linear and log-linear specifications, by accounting for the non-normality and skewed nature of the dependent variable, namely, hourly footfall. Importantly, neglecting the influence of non-user consideration outcomes in the footfall model is shown to result in self-selection bias, which leads to a loss of fit. Disregarding this consideration effect also leads to erroneous inferences regarding the insignificance of variables, such as bus costs and the population in the buffer area around metro stations. The effect of metro costs and out-of-vehicle travel time coefficients are overestimated substantially (\(-1.17\) vs. \(-0.74\) and \(-0.07\) vs. \(-0.02\)), and sensitivity to the costs of competing modes is underestimated (0.39 vs. 0.66) when consideration effects are ignored. Further, from a policy perspective, due to these omissions, projected demand is overestimated, especially at higher fare levels (above INR 4.5/km). This study finds that factors to enhance multimodal network integration, such as the presence of an airport or intercity train station near the metro station, and the level of service of competing transit modes, such as frequency of buses, affect footfall but not consideration. However, train service quality indicators, such as ease of purchasing tickets and level of comfort while traveling on a train, affect footfall only indirectly through consideration. However, first- and last-mile connectivity and accessibility have an influence at both levels, as they affect footfall directly and indirectly via consideration. In contrast to the existing studies, this study clearly illustrates the effect of disaggregate person-level variables (such as household income and out-of-vehicle travel time) and station characteristics (usability of footpath) on the aggregate footfall outcomes.