Human activity data are useful for many applications, such as activity prediction and recommendations. However, real-world data exhibit certain weaknesses, such as privacy concerns, collection cost, and low quality. Thus, realistic simulation of a massive amount of activity data is of great value. Existing solutions for human activity simulation can be classified into two categories: rule-based and data-driven methods, which are both limited in generating high-quality activity data as they cannot effectively model the inner drive of activities, i.e. human need, which however plays a crucial role in the activity decision-making process. To address these shortcomings, we propose a need-aware activity simulation framework based on imitation learning, with the consideration that the need is the interval driving force of human activities. First, we extend the activity modeling to the need semantics based on well-respected psychology theories. Then, to model the dynamics of human need, which evolves continuously over time and can be changed instantly by happened activities, we design a need state extractor based on Neural Jump Stochastic Differential Equations. Based on the dynamic modeling, we characterize the activity simulation as a continuous-time Markov decision process and solve it with the framework of GAIL. Extensive experiments on two real-life human activity datasets demonstrate that our framework outperforms state-of-the-art baselines significantly on data fidelity in terms of a number of key metrics. Moreover, the data generated by our model can well support practical applications, showing its effectiveness especially when the real data are on small scales.