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Understanding the decision making process of travelers underlying complex travel behavior is the most significant issue for evaluating the effect of urban transport policy. In 21st century, our society faces big changes such as evolution of information and communication technologies, environmental problems and an aging society. Various policy options for these changes have been taken in urban transport planning. Transportation Demand Management (TDM) measures and Intelligent Transportation Systems (ITS) technologies are very promising for solution to the problems of congestion and environmental damages. Urban infrastructure has been improved under the concept of universal design for the aged society. Decision makers have to consider what groups of individuals and households are affected by a new measure, since each individual and household has specific characteristics and lifestyle, and faces specific environment. Travel is a derived demand from the demand for activity participation and activity-based analysis has been effective to evaluate how travelers respond to the various changes in conditions. The first comprehensive study of activities and travel behavior is the work at Transport Studies Unit (TSU) at Oxford University more than 20 years ago. Household Activity-Travel Simulator (HATS) methodology, essentially a gaming simulation, was used very successfully in trying to better understand household travel decisions and the constraints within which those decisions are made. On the HATS game board, spatial components of activity-travel patterns are represented on the map, and temporal components are represented on the timelines, using activity diary data of all members of a household. When a policy measure is introduced, household members discuss together considering changes of constraints on the game board, and then new activity-travel patterns are simulated in this board. Gaming simulation approach is especially interested in inter-personal linkages and constraints, gaining more realistic responses than simply asking hypothetical questions. Other than TSU's study, there have been many works based on gaming simulation or interactive stated response survey methods. The objectives of this study are mainly to enhance gaming simulation methods using GIS capability and to understand the elderly household activity-travel patterns by interactive surveys. A GIS-based gaming simulation tool is developed linked to the model generating alternative activity-travel patterns, which is named Simulation Model for Activity Planning (SMAP). SMAP improves previous gaming simulation practices by introducing space-time visualization on GIS, and feasible activity patterns are calculated based on space-time and inter-personal constraints. Generation of feasible alternative activity patterns is based on the idea of advanced space-time prism classifying activities into three types according to fixity of timing, duration and location of activities.
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