{'fraction_non_alphanumeric': 0.011349306431273645, 'fraction_numerical': 0.0, 'mean_word_length': 5.6722689075630255, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An Uncertain Trajectory Modelling Method Based on Kernel Density Estimation

The accurate analysis of trajectories is of great significance for route selection, traffic status analysis, and urban traffic planning and so on. Existing researches lack effective methods for dealing with possible uncertainties in trajectories caused by objective enviroment and subjective intention etc. This work studies the method of constructing an uncertain model for the trajectories with the same starting point and end point based on kernel density estimation, to discover the distribution characteristics of the trajectories between two points in historical data, and to lay the foundation for trajectory prediction. Finally, the validity of the proposed method is verified on the real trajectory dataset.