## 3 Data Collection and Exploratory Data Analysis
### The Data Description for the Consideration Model  
A model predicting train consideration probability, as an indicator of potential demand, was developed using data from a face-to-face household survey of about 815 commuters in Chennai City during 2015-2016 [21]. After data cleaning and assembly, 803 valid responses were obtained. This sample size is adequate based on sample size calculations [35], as it is larger than the required sample size of 666 respondents for a 5% relative error margin and 99% confidence interval (i.e., \(Z\) = 2.58), assuming an average consideration propensity (\(p\) = 0.5). This consideration probability was selected for calculation as it gives the highest possible variance and the largest required sample size. The survey was conducted using stratified random sampling across 12 zones, covering urban, peri-urban, and suburban areas in Chennai. The sample size in each zone was proportional to its population, relative to the total population of the selected zones. Figure 1a shows the survey zones, and Figure 1b presents the residential and workplace locations of the respondents, demonstrating good spatial coverage of the study sample used to analyze potential demand. Figure 1: Details about the study area and the distribution of the survey respondents. (**a**) The zoning strategy of the study area. (**b**) The residential and workplace locations of the survey respondents. The survey collected data on commute modes considered in the three months prior to the survey date and the primary mode chosen for work trips. Additionally, the respondents provided information on factors hypothesized to influence their decision to consider the train, such as first- and last-mile connectivity, walkability, perceptions of transit service, and socio-demographic characteristics, using the following scales:  
1. First- and Last-Mile Factors (FLMs): include the access and egress time, distance, and cost to reach nearby public transport stations. 2. Walkability-Related Variables (WLKs): assess footpath usability on an ordinal scale (low, medium, or high) and the ease of walking or crossing roads near railway stations (low, medium, or high). 3. Perception of Train Service Characteristics (TSCs): evaluate the comfort, security, and ease of purchasing tickets (each rated as low, medium, or high) and the availability of amenities, such as shops or restaurants, near train stations (yes/no). 4. Personal and Work-Related Characteristics (PERWs): include variables such as age, gender, income, and work distance. The sample was found to be reasonably representative of the key variables. The average household size in the sample was 4.0, closely matching the population value of 4.1 in 2011 [36]. The average monthly household income ranged between INR 10,000 and INR 20,000. The average number of two-wheelers and cars per household was 1.22 and 0.27, respectively, aligning with the population average of 1.26 across vehicles in 2008 [21]. The primary mode share for work commutes in the sample consisted of 61% personal vehicles (two-wheelers and cars), 28% public transit (bus and train), 7% intermediate public transport (IPT), including auto-rickshaws, shared autos, and company-provided services, and 4% non-motorized transport (walking and cycling). Additionally, the average commute distance was 11.4 km, with a median travel duration of approximately 45 min for home-to-work trips. This study defines train consideration as a binary variable indicating whether a respondent used the train within the three months before the survey. This operationalization, using recent usage, serves as a tangible proxy for identifying individuals for whom the train is a relevant part of their active choice set, ensuring that the most recent, as well as sufficiently consistent travel patterns, are captured, without compromising recall accuracy. It is to be noted that the definition for including the metro in a choice set, while conceptually aligned with the potential demand, was based on revealed preference rather than solely on stated preference.