• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.531-543
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• 2014

Research on activity-based travel demand forecasting and activity-travel simulator has received an international attention for the last two decades. Ways to develop the activity-based simulator may be manifold. It is obvious that importing an existing simulator that has been proven internationally likely reduces the development cost and the risk of failure. By definition of the activity-based approach, however, the details of an activity-based simulator inevitably relies on particular social, economic and cultural characteristics of the society where the simulator is developed. When importing such a simulator from overseas, the researcher should be aware of the importance of tuning the system for the society to which the imported system is applied. There are many potential works on this, including for example the tuning of data structure that is likely different form of the original system. The authors are yet aware of certain research on those. The current paper aims to report the result of transforming the input data for applying the existing activity-travel simulator to Seoul. The paper first introduces FEATHERS that was developed in Belgium having Albatross which is the core of system. FEATHERS Seoul that is under development and modified version of the original FEATHERS is briefly described and the related problems are discussed. The paper then explored to resolve and to alleviate such problems.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.