• Journal of Advanced Navigation Technology
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• v.23 no.5
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• pp.380-385
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• 2019

This paper is concerned with departure passengers' dwell time analysis using real system data. Previous researches emphasize the importance of dwell time analysis from perspective of airport terminal planning and non-aeronautical revenue. However, short-term airport operation using passengers' dwell time is considered impossible due to absence of passengers' behavior data. Recently, in accordance with the wave of smart airport, world leading airports are systematically collecting passenger data. So there is high possibility of analyzing passengers' dwell time with the data stacked in the airport database. We conducted dwell time analysis using data from Incheon Int'l airport. In order to handle passenger data, we adapted clustering algorithm which is one of data mining techniques. As a clustering result, passengers are divided into 3 clusters. One is the cluster for passengers whose dwell time is relatively short and who tend to spend longer time in the airside. Another is the cluster for passengers who have near 3 hours dwell time. The other is the cluster for passengers whose total dwell time is extremely long.

• Journal of Information Technology Services
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• v.19 no.2
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• pp.125-138
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• 2020

Currently, subway crowding is estimated by observing a specific point at specific hours once or twice every 1 or 2 years. Given the extensive subway network in Seoul Metropolitan Area covering 588 stations, 11 lines and 80 transfer stations as of 2017, implementing crowding mitigation policy may have its limitations due to data uncertainty. A proposal has recently been made to effectively use smart card data, which generates big data on the overall subway traffic related to an estimated 8 million passengers per day. To mitigate subway crowding, this study proposes two viable options based on data related to smart card used in Seoul Metropolitan Area. One is to create a subway passenger pattern model to accurately estimate subway crowding, while the other is to prove effectiveness of early bird policy to distribute subway demand that is concentrated at certain stations and certain time. A subway passenger pattern model was created to estimate the passenger routes based on subway terminal ID at the entrance and exit and data by hours. To that end, we propose assigning passengers at the routes similar to the shortest routes based on an assumption that passengers choose the fastest routes. In the model, passenger flow is simulated every minute, and subway crowding level by station and line at every hour is analyzed while station usage pattern is identified by depending on passenger paths. For early bird policy, highly crowded stations will be categorized based on congestion level extracted from subway passenger pattern model and viability of a policy which transfers certain traveling demands to early commuting hours in those stations will be reviewed. In particular, review will be conducted on the impact of policy implemented at certain stations on other stations and lines from subway network as a whole. Lastly, we proposed that smart card based subway passenger pattern model established through this study used in decision making process to ensure effective implementation of public transport policy.

• Journal of the Korean Geographical Society
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• v.45 no.5
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• pp.592-608
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• 2010

This study investigates the diurnal pattern of transit ridership in the Metropolitan Seoul area. For the purpose, we use a weekday Smart Card passenger transaction data in 2005. Eleven passenger trip patterns are found from 2.74 million passengers moving on the Metropolitan Seoul Metro system. Among them, we analyze 2.4 million passengers blonging to five trip types having only one or two transaction record during a day. A total of 357 metro stations are classified to four types according to their diurnal pattern of passenger riderships. We analyze the relationships between passenger's trip chain patterns and subway station's diurnal transit ridership patterns. The result shows that the ratio of the number of passengers of particular time of the day is hierarchically related with trip chain patterns.

• Journal of Korea Port Economic Association
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• v.27 no.1
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• pp.1-11
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• 2011

The main purpose of this paper is to estimate the impact of exchange rate and economic business activity on the passengers' demand for international transportation. The demand pattern depends upon the transport vehicles that the global passengers take. The global passengers' demand for transportation is modelled as exchange rate, industrial production and seasonal dummy variables. The seasonality is found in both water and air, but the former is far greater than the latter. All series span the period January 1990 to December 2008. The empirical results of this paper reveal that the income elasticity of sea transport is greater than that of air one, all of which are positive. The study also shows that the exchange rate has an significant impact on the demand for air transport, whereas it is insignificant in water transport. The impulse response function indicates that passengers increase steadily before peaking seven to eight months after the shocks to economic business activity and decline very slowly to its pre-shock level. The air passengers also respond negatively to the shocks in exchange rate and the impacts of exchange rate shock seem to decrease relatively slowly, while the water passengers respond positively after six months. The industrial production shocks remain above equilibrium for more than twenty four months, while the exchange rate shocks remain below equilibrium for more than twenty four months. Boosted by improved economic conditions worldwide, international tourism has recovered faster than expected from the impacts of the global financial crisis and economic recession of late 2008 and 2009. These facts suggest that the demand of global water transport has the high possibility of growing steadily and continuously.

• Journal of the Korean Data and Information Science Society
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• v.26 no.5
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• pp.1061-1069
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• 2015

Recently, transportation big data generated in the transportation sector has been widely used in the transportation policies making and efficient system management. Bus passengers' mobility patterns are useful insight for transportation policy maker to optimize bus lines and time intervals in a city. We propose a new methodology to discover mobility patterns by using transportation card data. We first estimate the bus stations where the passengers get-off because the transportation card data don't have the get-off information in most cities. We then applies LDA (Latent Dirichlet Allocation), the most representative topic modeling technique, to discover mobility patterns of bus passengers in Cheong-Ju city. To understand discovered patterns, we construct a data warehouse and perform multi-dimensional analysis by bus-route, region, time-period, and the mobility patterns (get-on/get-off station). In the case of Cheong Ju, we discovered mobility pattern 1 from suburban area to Cheong-Ju terminal, mobility pattern 2 from residential area to commercial area, mobility pattern 3 from school areas to commercial area.

• The Journal of the Korea Contents Association
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• v.10 no.12
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• pp.91-100
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• 2010

To understand the trip patterns of subway passengers is very important to making plans for an efficient subway system. Accordingly, there have been studies on mining and classifying useful patterns from large smart card transaction databases of the Metropolitan Seoul subway system. In this paper, we define a new classification of subway trip patterns and devise a classification algorithm for eleven trip patterns of the subway users from smart card transaction databases which have been produced about ten million transactions daily. We have implemented the algorithm and then applied it to one-day transaction database to classify the trip patterns of subway passengers. We have focused on the analysis of significant patterns such as round-trip patterns, commuter patterns, and unexpected interesting patterns. The distribution of the number of passengers in each trip pattern is plotted by the get-on time and get-off time of subway transactions, which illustrates the characteristics of the significant patterns.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.2109-2113
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• 2008

As the standard of living is higher, the passengers using public transportations desire better qualities of environment as well as more comfortable indoor environment. In case of train, the passengers' comfort in passenger cabin is one of the most important elements to be competitive with other transport systems. The indoor air quality of the cabin should be managed properly, because many passengers travel for a long time in the small space of $144\;m^3$. For proper management of the air quality, the heating, ventilation and air conditioning (HVAC) system is required for the ventilation of the compartment. To maintain comfortable environment in the compartment, the automatic ventilation system is needed to exchange the indoor air with fresh air or clean indoor air. In this study, we investigated the indoor air quality (PM-10, $CO_2$, and VOCs) in the compartment of train. In addition, type and pattern of PM-10 has been analyzed through the clustering analysis. Based on the analysis, we could found that the fine particulate matters in the compartment can be a serious hazard to human. To control the concentration of PM-10 and $CO_2$ air cleaners were developed. Through this study, it is expected that people who take a train will be in a more comfortable environment.

• Proceedings of the KIEE Conference
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• 1991.07a
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• pp.726-729
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• 1991

This paper proposes a new operation algorithm for elevator by considering both better service for passengers and minimization of energy consumption for elevator operation. The main idea of the proposed operation algorithm is based on the assumption that passengers push the numbered buttons indicating their destination, one of the main differences of proposed operation mode from the conventional one is that the elevator may move to the opposite direction for a few floors according to the rescheduled operational pattern determined by some factors. Some examples by computer simulation show the efficiency of the proposed operation algorithm.

• Proceedings of the KSR Conference
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• 1998.05a
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• pp.160-171
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• 1998

Express subway system is one of the effective systems adapting to improve service level. Express trains make fewer passenger stop, using a double track or a bypass track, than local trains which served all stations, Express service has been very popular with passengers who travel uninterrupted between terminals, but is has generated some dissatisfaction among passengers who experience longer waiting time on stations. This study aims at proposing the methodology to analyze changes of travel pattern in subway system adapting the express service and to estimate the time saving effects resulting from the installation of the express system. This methodology is evaluated in the fifth line under an assumpt ion that express subway system are adapted. Based on the results of the case study, the following conclusions are made: First, express system reduce a total travel time of 13% or above. Second, shorter headway of express trains increases the time saving effects on subway system. although it requests more waiting time to local train passenger. Third, an installation of Express system to Seoul subway system can augment subway demand in seoul metropolitan area.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.28-38
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• 2019

The smart card data stores the transit usage records of individual passengers. By using this, it is possible to analyze the traffic demand by station and time. However, since the purpose of the trip is not recorded in the smart card data, the demand for each purpose such as commuting, school, and leisure is estimated based on the survey data. Since survey data includes only some samples, it is difficult to predict public transport demand for each purpose close to the complete enumeration survey. In this study, we estimates the purposes of trip for individual passengers using the smart card data corresponding to the complete enumeration survey of public transportation. We estimated trip purposes such as commute, school(university) considering frequency of O-D, duration, and departure time of a passenger. Based on this, the passengers are classified as workers and university students. In order to verify our methodology, we compared the estimation results of our study with the patterns of the survey data.