Multivariate analysis of car-following behavior data using a coupled hidden Markov model

Abstract

Accurate analysis of driving behavior data is important for improving traffic operations and for automakers to design safe and effective Advanced Driver Assistance System (ADAS). Although different approaches have been proposed to analyze driving behavior data and understand the car-following behavior, very few of them have considered the interaction effects among driving behavior variables and constructed a multivariate modeling framework to fully understand and extract the underlying primitive driving patterns. To bridge this gap, this paper introduces the coupled hidden Markov model (CHMM) and evaluates its applicability in obtaining the car-following behavioral semantics and comparing the similarity of different drivers’ driving styles. The Safety Pilot Model Deployment (SPMD) dataset, which contains two years of naturalistic driving data, is used to demonstrate the advantages of the proposed coupled modeling approach. The modeling results suggest that CHMM with 3 hidden states provides the most appropriate driving behavior pattern segmentation results. It generates reasonable durations for each behavioral semantic by accounting for the dependence relationship among variables in the analysis. Moreover, a similarity analysis is conducted considering three aspects of a driving behavior pattern: driving behavior pattern transfer, driving behavior pattern selection, and aggressiveness behavior. The result reveals that these three aspects do not necessarily show consistent trends for the same driving behavior. Overall, the findings from this paper provide a novel framework for better understanding and interpreting the driving behavior and primitive driving patterns, and confirm the importance of considering the dependence among different driving behavior variables.