Trajectory Analysis Based on Clustering and Casual Structures

Causal structure discovery methods are investigated recently but none of them has taken possible time-varying structure into consideration. This paper uses a notion of causal time-varying dynamic Bayesian network (CTV-DBN) and define a causal boundary to govern cross-time information sharing. CTV-DBN is constructed by using asymmetric kernels to address sample scarcity and to adhere to causal principles; while maintaining good variance and bias trade-off. Upon satisfying causal Markov assumption, causal inference can be made based on manipulation rule. We explore trajectory data collected from taxis in Beijing which exhibit heterogeneous patterns, data sparseness and distribution skewness. Experiments show that by using casual structures and trajectory clustering, we can analyse the spatio-temporal behavior of the trajectory data.