Abstract
Analysis of long-distance travel demand has become more relevant in recent times due to the growing share of traffic induced by journeys related to remote activities. Consequently, there is a need of reliable long-distance travel forecasting tools like agent-based simulations. This paper presents a target-based simulation that simulates long-distance travel behavior for a long period of time. It is shown how decisions are modelled in this simulation. Activity type, duration, destination and mode are chosen simultaneously with respect to time and monetary budgets. The presented approach uses a heuristic to reduce the choice set followed by optimizing a discomfort function.
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