Large scale of data, various types of low-level attributes and uncertainty of prediction information exist in probability prediction of taking taxi. To solve these problems, this paper offline deals with the GPS data of taxi and road network data by using mining algorithms in the large-scale trajectory data domain, then builds a belief rule-base by transforming various types of information with uncertainty into rules which are in form of the belief structure, after that uses RIMER (belief rule-base inference methodology using evidential reasoning) to get the final probability of any points on the road network. Finally, the GPS data of Beijing’s taxi in November of 2012 are taken as an example to illustrate the usage of the online prediction method, and the results show the real-time and accuracy of the proposed method.
YANG Longhao,CAI Zhiling,HUANG Zhixin et al. Belief Rule-Base Inference Methodology for Predicting Probability of Taking Taxi[J]. Journal of Frontiers of Computer Science and Technology, 2015, 9(8): 985-994.