  Simulating the human mobility and generating large-scale trajectories are of
great use in many real-world applications, such as urban planning, epidemic
spreading analysis, and geographic privacy protect. Although many previous
works have studied the problem of trajectory generation, the continuity of the
generated trajectories has been neglected, which makes these methods useless
for practical urban simulation scenarios. To solve this problem, we propose a
novel two-stage generative adversarial framework to generate the continuous
trajectory on the road network, namely TS-TrajGen, which efficiently integrates
prior domain knowledge of human mobility with model-free learning paradigm.
Specifically, we build the generator under the human mobility hypothesis of the
A* algorithm to learn the human mobility behavior. For the discriminator, we
combine the sequential reward with the mobility yaw reward to enhance the
effectiveness of the generator. Finally, we propose a novel two-stage
generation process to overcome the weak point of the existing stochastic
generation process. Extensive experiments on two real-world datasets and two
case studies demonstrate that our framework yields significant improvements
over the state-of-the-art methods.
