CONCLUSION

This chapter has outlined the conceptual framework that will be used as a building block to model the dynamic process of agents' activity (location) choice in a large scale micro-simulation system. The framework considered the dynamic formation of the choice sets with the focus of location choice. It integrates cognitive learning and social learning. In the proposed approach, cognitive learning focuses on updating beliefs about a non-stationary environment that will impact the expected utility of alternatives and habit formation, while social learning emphasizes on deriving and updating aspirations that may trigger re-evaluating currently known alternatives (exploitation) or searching for new alternatives (exploration). As such, it provides a multi-agent modeling approach for predicting habitual choice, exploitation choice and exploration choice in activity-travel behavior as a function of discrepancies between dynamic, context-dependent aspirations and context-dependent expected utilities. A case study of shopping location choice is illustrated in this chapter. A similar framework can also be used for modeling other choice facets and learning behavior in activity-travel choices.

Our approach is scalable in the sense that it is applicable to study areas of large size (e.g., region wide). As expected, knowing the awareness set from which a choice is made may provide a parsimonious way in large scale micro-simulation in the areas of activity-based travel-demand modeling and integrated land-use – transportation systems. Some applications are straightforward. For example, conditions can be simulated under which learning leads to habitual behavior as well as what happens when moving to a new city. Likewise, the optimal location of a new shopping centre can be simulated. Also, spatial effects of the new shopping centre opening can be observed.