Travel Behavior Analysis. The transportation system has been affected in a positive way with growing technology. Various software has been developed to enhance the transit services such as the smart cards which not only facilitate the services offered to the people, but also generate a large amount of data that can be used by transit agencies for different purposes to keep enhancing the transportation system. As stated in the Travel Behavior Analysis using the Smart Card Data (Xxx et al., 2015), this paper attempts to utilize the smart card data as input for large-scale activity based public transport simulation for analyzing travel behavior pertaining to transit users, using MATSim, an open source agent-based transport simulation package. MATSim is a dynamic simulation toolkit that is implemented in Java modular structure and can be modified to various conditions to handle different scenarios. At first, it was used as traffic simulation, but then a new model was develop giving reliable results and its functionality was extended to support detailed public transport simulation. Transit ridership during Summer occupancy levels, calculate vehicle speed and in-vehicle travel time etc. A transportation service Korean Transport Institute (KOTI) in Seoul, South Korea was chosen as a case study since the service is the 2nd largest public transport modal share in the world with approximately 63% public transport ridership after Hong Kong where modal share is 90% for mass transit. The public transport system contains 19 urban railway lines and more than 400 bus routes in greater Seoul Metropolitan Area (SMA) (Ali et al., 2015). Seoul Metropolitan Government introduced a new system of fare collection called “T-Money” where users must validate their boarding and alighting when using the network services except for transfers within the subway system. Each transaction records individual’s information like card ID, boarding time, boarding station ID, alight time, alight station ID, transport method (subway, regional bus, circular bus, etc.), bus route ID, passenger type (adult, youth, children), total fare and total distance travelled. Data used were provided by the Korean Transport Institute (KOTI) for five weekdays from June 11 to June 15, 2012 with approximately 28 million transactions made on average working day. The methodology used to study the dataset was completed with simple coding and formulation, and spatial analysis was carried out using ArcGIS. The methodology steps are (Xxx et al., ...
