Smart card transactions contain user information and travel patterns. Thus, in this study, elderly smart card transactions were analyzed to determine elderly captive riders’ hot spots that need appropriate social services for them. There has been minimal focus on the spatial autocorrelation of smart card big data when developing new traffic policies. Therefore, in this study, spatial autocorrelation analysis was performed using Seoul’s smart card data for six weeks. In the collected data, it was found that 76.3% of the elderly trips were concentrated on subways, which offer free tickets. For this reason, we examined elderly captive bus riders in this study. Moran’s I was 0.277 for the elderly smart card transactions, and it has a positive spatial autocorrelation with the significance level of 0.01. Local indicators of spatial association (LISA) analysis is used to determine the spatially autocorrelated areas. Fifty administrative units (dongs) in Seoul were considered hot spots, and spatial clustering was confirmed; 61 dongs were considered cold spots. The distributions of hot spots and cold spots seem to be closely related to the subway supply level rather than the elderly population. Twenty-eight hot spots seriously need appropriate social services for elderly bus users because those hot spots do not operate subway service. First, barrier-free bus stops should be installed at the 28 hot spots. Second, bus lines that pass the 28 hot spots need to have high priority when supplying low-floor buses. Third, the low-floor bus shuttle service from/to the 28 hot spots is proposed by analyzing the top nine origins and destinations of the elderly. To propose advanced public transportation policies for the elderly, smart card spatial autocorrelation analysis can be used.