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This study analyzes a skip-stop strategy considering four types of train choice behavior with smartcard data. The proposed model aims to minimize total travel time with realistic constraints such as facility condition, operational condition, and travel behavior. The travel time from smartcard data is decomposed by two distributions of the express trains and the local trains using a Gaussian mixture model. The utility parameters of the train choice model are estimated with the decomposed distribution using the multinomial logit model. The optimal solution is derived by a genetic algorithm to designate the express stations of the Bundang line in the Seoul metropolitan area. The results indicate the travel times of the transfer-based strategy and the high ridership-based strategy are estimated to be 21.2 and 19.7 min/person, respectively. Compared to the travel time of the current system, the transfer-based strategy has a 5.8% reduction and the high ridership-based strategy has a 12.2% reduction. For the travel behavior-based strategy, the travel time was estimated to be 18.7 minutes, the ratio of the saved travel time is 17.9%, and the energy consumption shows that the travel behavior-based strategy consumes 305,437 (kWh) of electricity, which is about 12.7% lower compared to the current system.

More than seven million people rely on the metro system daily in Seoul, South Korea. The metro system plays a vital role in connecting passengers to desired locations with the advantage of allowing for travel reliability and relative safety, all the while being an environmentally friendly alternative to other transport systems. Despite the benefits mentioned above, crowding on the metro can contribute significantly to deterioration of the individual’s travel experience and necessitates its proper evaluation, so that user experience may be improved. To achieve this, this study quantifies the level of crowding by deriving estimates for route choice models with crowding as one of the main attributes for metro passenger decisions. The data used in the model are obtained through a stated preference survey, which was conducted at five major transfer stations in Seoul to estimate factors such as travel time, transfers, travel cost, and crowding level. These attributes are then analyzed using homogeneity and heterogeneity models of different trip purposes. Furthermore, crowding multipliers (CMs) for Seoul are estimated and compared with those of other cities around the world. Estimation results pinpoint crowding as one of the main factors in route choice for passengers for all types of trips, with multiplier values reaching the highest of all cities around the world for sitting CM and standing CM at 2.15 and 3.22, respectively. Our results indicate that Seoul metro passengers are more sensitive to crowding than any of the passengers analyzed in 18 major city metros around the world.

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As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.