This paper introduces a microscopic traffic simulation that continuously simulates activity-based agent behavior. Rather than using iterative optimization, which builds on stochastic user equilibria, the simulation uses a continuous planning approach. This approach's behavioral model uses the concept of needs to model human demands. Several intuitive parameters control demand and facilitate calibration of various behaviors. These behaviors originate from a planning heuristic making just-in-time decisions about upcoming activities that an agent should execute. The planning heuristic bases its decisions on agents' current need levels and their short-term development. The model is illustrated through simulation runs, and directions for future research are suggested.
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