A Functional Approach for Analyzing Time-Dependent Driver Response Behavior to Real-World Connected Vehicle Warnings
Di Yang, Kaan Özbay, Jingqin Gao, Fan Zuo
Abstract
Understanding driver response behavior to Connected Vehicle (CV) warnings is one of the fundamental tasks in improving road safety. However, previous studies often used aggregated safety measures and simulation environments to accomplish this task. Consequently, time-dependent driver response behavior under real-world conditions has rarely been investigated. This study proposes a new functional data analysis (FDA) approach to analyze time-dependent driver response behavior to CV warnings obtained from the New York City CV Pilot Deployment (NYC CVPD) project. Sparse functional design that can account for irregularly spaced functional measurements is adopted to accommodate the potential noise contaminated real-world CV data. Functional principal component analysis and nonparametric functional linear regression are used to smooth raw driver behavior profile and estimate time-dependent effect of driver response behavior, respectively. The speed compliance application implemented in the NYC CVPD project is used as the case study. By modeling time series of speed data after receiving warnings as continuous functions, the proposed FDA approach reveals new patterns of driver response behavior over time, such as the diminishing effect of drivers’ speed reduction and the increase in variability of driver response behavior over time, which have rarely been explored using traditional approaches. Moreover, the proposed FDA approach supports the extraction of certain traditional safety measures and has the potential to be generalized to analyze various CV applications. The findings of this study can support the calibration of detailed driver behavior in CV environments and facilitate better CV application design and further investigation of the benefits of CVs.