A Driving Behavior Distribution Fitting Method Based on Two-Stage Hybrid User Classification

Abstract

Determining the distribution fitting of traditional private vehicle user driving behavior is an effective way to understand the differences between different users and provides valuable information on user travel demands. The classification of users is significant to product improvement, precision marketing, and driving recommendations. This study proposed a method which includes four aspects: (1) data collection; (2) data preprocessing; (3) data analysis—a two-stage hybrid user classification, and (4) distribution fitting method. A two-stage hybrid user classification method is used to cluster traditional vehicle users. First, the first-stage classification of the classification method extracts the daily typical time–mileage-series travel patterns (TMTP) to obtain user driving time characteristics. This first-stage classification also extracts the mean and standard deviation of the daily vehicle mileage traveled (DVMT) to express user driving demands. Next, users are divided by K-means based on the driving time characteristics and driving demands from the first stage. Finally, a three-parameter log-normal distribution is used to fit the DVMT of different user types. Comparison with traditional clustering based on the mean and standard deviation and the proportion of each vehicle’s time series in the TMTP types, this study reveals that the new methods provide significant advantages in analyzing driving behavior and high reference value for enterprises making electric vehicle driving range recommendations, car market segmentation, and policy making decisions.