CM Sequence based Trajectory Modeling with Destination
For researchers in trajectory modeling, this provides a new probabilistic framework that explicitly incorporates destination information, addressing a limitation of standard Markov models.
The paper proposes using conditionally Markov (CM_L) sequences to model destination-directed trajectories (e.g., airliner flights), which incorporate origin, destination, and motion. Simulations demonstrate the approach's effectiveness for trajectory modeling and prediction.
In some problems there is information about the destination of a moving object. An example is an airliner flying from an origin to a destination. Such problems have three main components: an origin, a destination, and motion in between. To emphasize that the motion trajectories end up at the destination, we call them \textit{destination-directed trajectories}. The Markov sequence is not flexible enough to model such trajectories. Given an initial density and an evolution law, the future of a Markov sequence is determined probabilistically. One class of conditionally Markov (CM) sequences, called the $CM_L$ sequence (including the Markov sequence as a special case), has the following main components: a joint endpoint density (i.e., an initial density and a final density conditioned on the initial) and a Markov-like evolution law. This paper proposes using the $CM_L$ sequence for modeling destination-directed trajectories. It is demonstrated how the $CM_L$ sequence enjoys several desirable properties for destination-directed trajectory modeling. Some simulations of trajectory modeling and prediction are presented for illustration.