## 3 Data Collection and Exploratory Data Analysis
### Description of Footfall Data  
The data for modeling the realized demand, measured by station-level hourly footfall on a typical weekday at various metro stations, was collected using AFC systems at 32 metro stations by Chennai Metro Rail Limited (2022) in Chennai, India [32]. Footfall at a train station was used here as the aggregate demand measure, as it represents the total number of passengers utilizing a train station in a given time period. It is also a more comprehensive indicator of aggregate demand than boarding or alighting measures, as it captures both inbound and outbound flows at the station. This measure is also preferred over run-level or hourly ridership measures, as it captures spatial variability across different station locations effectively. Thus, it is a useful input for station design, parking planning, and operational planning for first-last-mile services. The variables pertinent to the footfall model are derived from multiple data sources. The metro and bus service headways and frequencies are extracted from official timetables available on their respective websites [32; 33]. Individual-level data, including average access and egress travel times across different groups, is sourced from the mode choice survey described in the previous subsection. Additionally, socio-demographic data related to the population is acquired from the master plan of Chennai City [34]. Table 2 presents an overview of the aggregate demand and supply characteristics of metro train services in Chennai City. The metro system comprises 32 stations spanning  
Figure 3: Perceptions of respondents about trains and walkability. over 70 km. The system experiences an average hourly footfall of 321 passengers, with a daily ridership of 2754. Metro frequency ranges from every 7 min during peak hours to nearly 30 min during off-peak hours, with an average headway of 13 min. Interestingly, the average headway of buses closely mirrors this pattern. Among the 32 stations, 4 stations serve as crucial transportation hubs, connecting major points, such as the airport, intercity bus terminus, and intra- and intercity train stations. Approximately 90% of the stations offer two-wheeler parking facilities, while 70% provide four-wheeler parking, facilitating convenient park-and-ride services that can boost the realized demand, i.e., footfall at metro stations. The average daily footfall was around 5500 passengers per day at each station. Figure 4a shows the spatial variation in the footfall across stations, whereas Figure 4b highlights the long-tailed and right-skewed distribution of hourly ridership data. Thus, the hourly footfall plot deviates from the normal distribution and could lead to the violation of the linear regression model assumptions. To address this, various power transformations, including log-linear models and the Box-Cox transformation, were tested. Figure 4a shows the variation in footfall across different stations, with larger circles representing higher realized demand. Footfall is notably high at stations serving intercity transport hubs, such as airports and bus terminus CMBT, indicating the key role of the metro as the first or last mile for intercity trips. However, the share of metro use for work and its consideration at or near the intercity terminals may be relatively less, and its frequency of use may be irregular. Alternatively, multimodal intra-city hubs, such as \"Guindy\" and \"Alandur\", have higher footfall because of greater connectivity with nearby bus and suburban train stations. Thus, multimodal integration is a key driver of metro train demand in Chennai. Therefore, it is necessary to understand these location and purpose-based differences to understand the shortfall in footfall at certain locations and propose policies to increase the overall use of the metro. Figure 4b shows that the original hourly footfall distribution is highly right-skewed and deviates from normality.