Abstract：Traffic congestion, traffic safety, and traffic related environmental problems caused by rapid urbanization have become increasingly important with "human-centered" intelligent transportation systems (ITS) needed that are based on individual driving characteristics. Thus, common driving styles were analyzed to more effectively utilize the existing road transport resources. However, driving styles are difficult to identify and quantify, which leads to difficulties modelling the individual driving characteristics. In this paper, the driving characteristics of 16 drivers on actual roads were analyzed using a topical model. The hidden driving characteristics were extracted and used to convert the data from "driving style-driving behaviour data" into "driving style-driving state-driving behaviour data". The discovered driving style structural information provides theoretical support for more efficient intelligent transportation systems. The reconstructed model data is shown to correlate well with the original data which verifies the model for analyzing driving style diversity.
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2016, Vol. 56 
